US 8,561,177 B1Grant
Systems and methods for detecting communication channels of bots
Issue Date:2013-10-15
•31 Claims
•7 Drawing Sheets
Abstract
Exemplary systems and methods for detecting a communication channel of a bot. In exemplary embodiments, presence of a communication channel between a first network device and a second network device is detected. Data from the communication channel is scanned and used to determine if a suspected bot communication exists. If a bot communication is detected, then a recovery process may be initiated.
Metadata
Assignee
- FireEye, Inc.
Inventors
- Ashar Aziz
- Wei-Lung Lai
- Jayaraman Manni
Application Information
Application Number:US 11/998,605
Filing Date:2007-11-30
Priority Date:2004-04-01
Art Unit:2432
Classifications
IPC:
G06F21/00
Patent Drawings (7 sheets)
Description
Cross-Reference to Related Applications
[0001] This application claims benefit of provisional patent application No. 60/868,323, filed Dec. 1, 2006, entitled, “Detecting Command & Control Communication Channels of Botnets”, and is a continuation-in-part of U.S. patent application Ser. No. 11/494,990, filed Jul. 28, 2006, entitled “Dynamic Signature Creation and Enforcement”, which is a continuation-in-part of U.S. patent application Ser. No. 11/471,072, filed Jun. 19, 2006, entitled “Virtual Machine with Dynamic Data Flow Analysis”, which is a continuation-in-part of U.S. patent application Ser. No. 11/409,355, filed Apr. 20, 2006, entitled “Heuristic Based Capture with Replay to Virtual Machine”, which is a continuation-in-part of U.S. patent application Ser. No. 11/096,287, filed Mar. 31, 2005, entitled “System and Method of Detecting Computer Worms”, and is a continuation-in-part of U.S. patent application Ser. No. 11/151,812, filed Jun. 13, 2005, entitled “System and Method of Containing Computer Worms,” and is a continuation-in-part of U.S. patent application Ser. No. 11/152,286, filed Jun. 13, 2005, entitled “Computer Worm Defense System and Method”; U.S. patent application Ser. No. 11/096,287 claims the benefit of U.S. Provisional Application No. 60/559,198 filed on Apr. 1, 2004, U.S. patent application Ser. No. 11/151,812 claims the benefit of U.S. Provisional Application No. 60/579,953 filed on Jun. 14, 2004, and U.S. patent application Ser. No. 11/152,286 claims the benefit of U.S. Provisional Application No. 60/579,910 filed on Jun. 14, 2004, all of which are incorporated by reference herein.
[0002] This application is related to U.S. patent application Ser. No. 11/998,750, filed on Nov. 30, 2007, and entitled “Systems and Methods for Detecting Encrypted Bot Command & Control Channels.”
[0003] The above-referenced related patent application is incorporated by reference herein.
Background of the Invention
[0004] 1. Field of the Invention
[0005] The present invention relates generally to network security and more particularly to detecting command and control communication channels of a bot.
[0006] 2. Background Art
[0007] Presently, malicious software (i.e., malware) can attack various devices via a network. For example, malware may include any program or file that is harmful to a computer user, such as bots, computer viruses, worms, Trojan horses, spyware, or any programming that gathers information about a computer user or otherwise operates without permission. Various processes and devices have been employed to prevent the problems that malware can cause.
[0008] For example, computers often include antivirus scanning software that scans a particular client device for viruses. The scanning may be performed based on a schedule specified by a user associated with the particular computer, a system administrator, and so forth. Unfortunately, by the time a virus is detected by the scanning software, some damage on the particular computer may have already occurred.
[0009] Another option for preventing malware is a honey pot. A honey pot is a computer system on the Internet that is expressly set up to attract and “trap” an illicit user that attempts to penetrate another's computer system. The illicit user can include a hacker, a cracker, or a script kiddy, for example. The honey pot records the activities associated with the invasion of the computer system. Disadvantageously, as the honey pot is being invaded, so too are other users' computer systems on the same network. Thus, other users' computer systems may be harmed while the honey pot determines the nature of the malware invading the honey pot's own computer system.
[0010] In some instances, malware comprises a bot. A bot is a software robot configured to remotely control all or a portion of a digital device (e.g., a computer) without authorization by the digital device's user. Bot related activities include bot propagation and attacking other computers on a network. Bots commonly propagate by scanning nodes (e.g., computers or other digital devices) available on a network to search for a vulnerable target. When a vulnerable computer is scanned, the bot may install a copy of itself. Once installed, the new bot may continue to seek other computers on a network to infect.
[0011] A bot may also, without the authority of the infected computer user, establish a command and control communication channel to receive instructions. Bots may receive command and control communication from a centralized bot server or another infected computer (e.g., via a peer-to-peer (P2P) network established by a bot on the infected computer).
[0012] The bot may receive instructions to perform bot related activities. When a plurality of bots (i.e., a botnet) act together, the infected computers (i.e., zombies) can perform organized attacks against one or more computers on a network. In one example, bot infected computers may be directed to ping another computer on a network in a denial-of-service attack. In another example, upon receiving instructions, one or more bots may direct the infected computer to transmit spam across a network.
[0013] A bot may also receive instructions to transmit information regarding the infected host computer. In one example, the bot may be instructed to act as a keylogger and record keystrokes on the infected host computer. The bot may also be instructed to search for personal information and email addresses of other users contained in an email or contacts file. This information may be transmitted to one or more other infected computers or a user in command of the bot or botnet.
Summary of the Invention
[0014] Systems and methods for detecting a command and control communication channel of a bot are provided. In exemplary embodiments, presence of a communication channel between a first network device and a second network device is detected.
[0015] Data from the communication channel is scanned and used to determine if a suspected bot communication exists. Several different methods may be utilized to detect a command and control (C&C) communication within the communication channel. In one embodiment, a fingerprint module may scan for a bot oriented command communications in an IRC channel. In one example, the fingerprint module scans for commands or messages that indicate that an IRC channel is being established. In an alternative embodiment, a port module may monitor for communications originating from a non-standard port. In a further embodiment, a virtual machine may be utilized to detect C&C communication channels either in a replay virtual machine environment or in a direct entry virtual machine environment. Accordingly, intercepted or replayed network data obtained from the communication channel is transmitted to the virtual machine, and the virtual machine response is then analyzed to determine if the virtual machine is infected. In some embodiments, an analysis environment may wait for an outbound domain name system (DNS) request, which may also identify the C&C channel. A pseudo-DNS server in the virtual machine can respond to the request with an IP address mapped to an internal-to-virtual machine-analysis pseudo-server. The outbound IRC or web request made to the supplied IP address may confirm the C&C channel.
[0016] If a bot communication is detected, then a recovery process may be initiated. In one embodiment, during the recovery process, the devices that are suspected as being infected may be flagged and/or proper users and administrators notified. For example, icons associated with nodes coupled to a network may be color coded based on their association with any infection propagation, command and control communication with a bot, and/or bot attack. In another embodiment, a router (i.e., switch) may be configured to direct all data from a bot server (e.g., from the source IP address of the bot server) to a controller. As a result, all the network data from the bot server, not only that which is transmitted to the network device, may be intercepted.
Brief Description of the Drawings
[0017] FIG. 1 is a diagram of a channel detection environment in which embodiments of the present invention may be practiced.
[0018] FIG. 2 is a block diagram of an exemplary bot detector implementing some embodiments of the present invention.
[0019] FIG. 3 is a block diagram of an exemplary controller implementing some embodiments of the present invention.
[0020] FIG. 4 is a block diagram of an exemplary analysis environment, in accordance with some embodiments of the present invention.
[0021] FIG. 5 is a flowchart of an exemplary method for detecting a C&C channel of a bot.
[0022] FIG. 6 is a block diagram of the controller, in accordance with one embodiment of the present invention.
Description of Exemplary Embodiments
[0023] Exemplary systems and methods for detection of a command and control communication channel of a bot are provided. The bot running on a compromised device may be part of a plurality of software robots (e.g., a botnet) which run autonomously on a collection of compromised devices under a common command and control (C&C) infrastructure. In one example, a bot on the compromised device may open an Internet Relay Chat (IRC) channel with another device to receive commands. This IRC channel may be referred to as a C&C communication channel.
[0024] In some embodiments, the communication channel detection system may comprise a dynamic honey pot. A dynamic honey pot can monitor network traffic to detect the presence of a C&C communication channel. If a C&C channel or a suspected C&C channel is detected, then the network data from the C&C communication channel may be intercepted. In exemplary embodiments, the network traffic does not need to be directly transmitted to the dynamic honey pot. Rather, the dynamic honey pot can detect possible bot infection attempts or command and control communication with an existing bot on other devices on the network. Upon detection, the dynamic honey pot can then intercept future network data.
[0025] In exemplary embodiments, network data from a communication network may be copied and analyzed. If a C&C channel or a suspected C&C channel is detected, related network data may be intercepted. The intercepted network data may continue to be analyzed. If the intercepted network data comprises a network attack, command and control communication, and/or an attempt to propagate the bot, an unauthorized activity signature configured to identify the activity and/or bot may be generated.
[0026] The bot compromises one or more compromised devices which may send spam and malware, such as viruses, worms, or Trojan horses, for example. A virus is an intrusive program that infects a computer file by inserting a copy of itself in the file. The copy is usually executed when the file is loaded into memory, allowing the virus to infect other files. A worm is a program that propagates itself across multiple computers, usually by creating copies of itself in each computer's memory. A worm may duplicate itself in a computer so many times that it causes the computer to crash. A Trojan horse is a destructive program disguised as a game, utility, or application. When run by a user or computer program, a Trojan horse can harm the computer system while appearing to do something useful.
[0027] Malware may also include adware and spyware. Adware is a program configured to direct advertisements to a computer or a particular user. In one example, adware identifies the computer and/or the user to various websites visited by a browser on the computer. The website may then use the adware to either generate pop-up advertisements or otherwise direct specific advertisements to the user's browser. Spyware is a program configured to collect information regarding the user, the computer, and/or a user's network habits. In an example, spyware may collect information regarding the names and types of websites that the user browses and then transmit the information to another computer. Adware and spyware are often added to the user's computer after the user browses to a website that hosts the adware and/or spyware. The user is often unaware that these programs have been added and are similarly unaware of the adware and/or spyware's function.
[0028] FIG. 1 is a diagram of a channel detection environment 100 in which embodiments of the present invention may be practiced. The channel detection environment 100 may comprise a bot server 105 in communication via a communication network 110 with a network device 115. Additionally, a tap 120 may be coupled to the communication network 110. The tap 120 may be further coupled to a controller 125. Optionally, a router (not shown) may be provided for re-routing data from the communication network 110.
[0029] The bot server 105 and the network device 110 comprise digital devices. A digital device comprises any device with a processor. Some examples of digital devices include computers, servers, laptops, personal digital assistants, and cellular telephones. The bot server 105 is configured to transmit network data over the communication network 110 to the network device 115, which is configured to receive the network data. In some embodiments, the bot server 105 may establish a C&C communication channel with the network device 115 via the communication network 110. The C&C communication channel may be utilized by the bot server 105 to control a bot on the node or the node itself on the network device 115.
[0030] The bot server 105 may attempt to control the network device 115 by transmitting instructions or a bot to the network device 115. In one example, the bot server 105 is a computer controlled by an illicit user to control one or more bots or one or more network devices 115 through the use of bots. In another example, the bot server 105 is a network device similar to the network device 115; the bot server 105 may be a part of a P2P communication network for transmitting instructions to a bot on another digital device. In this example, once infected, the network device 115 may be a part of a P2P communication network whereby the network device 115 may transmit instructions to another network device similar to a bot server 105.
[0031] The tap 120 may comprise a digital data tap configured to monitor network data and provide a copy of the network data to the controller 125. In some embodiments, the tap 120 comprises a span port. The network data comprises signals and data that are transmitted over the communication network 110 including data flows from the bot server 105 to the network device 115. As discussed herein, the network data may include command and control instructions transmitted from the bot server 105. In one example, the tap 120 copies the network data without an appreciable decline in performance of the bot server 105, the network device 115, or the communication network 110. The tap 120 may copy any portion of the network data. For example, the tap 120 can receive and copy any number of data packets of the network data. In exemplary embodiments, the tap 120 can monitor and copy data transmitted from multiple devices without appreciably affecting the performance of the communication network 110 or the devices coupled to the communication network 110. In various embodiments, the tap 120 can sample the network data based on a sampling scheme.
[0032] The tap 120 can also capture metadata from the network data. The metadata can be associated with the bot server 105 and/or the network device 115. In one example, the metadata may identify the bot server 105 and/or the network device 110. In some embodiments, the bot server 105 transmits metadata, which is captured by the tap 120. In other embodiments, a heuristic module, described in more detail below, can detect the bot server 105 and/or the network device 110 by analyzing data packets within the network data and generate the metadata.
[0033] The communication network 110 may comprise a public computer network such as the Internet, a private computer network such as a wireless telecommunication network, wide area network, local area network, or any other type of network enabled to provide communications between coupled devices.
[0034] Although FIG. 1 depicts data transmitted from the bot server 105 to the network device 115, either device can transmit and receive data from the other device. Similarly, although only one bot server 105, communication network 110, network device 115, tap 120, and controller 125 are depicted in FIG. 1 , there may be any number of bot servers 105, communication networks 110, network devices 115, taps 120, and controllers 125.
[0035] The controller 125 may comprise a processor and/or software configured to receive and analyze network data for the presence of network data sent via the C&C communication channel. In exemplary embodiments, the controller 125 receives network data over the tap 120. If the controller 125 detects commands within network data that potentially establishes a C&C communication channel, the controller 125 may intercept the associated network data. In one example, the controller 125 may intercept network data from the same data flow as that which potentially established the C&C communication channel. In another example, the controller 125 may intercept all network data from a node on the communication network that either received or sent the commands (e.g., the bot server 105 and the network device 115). When network data is intercepted, the network data is no longer received by the intended recipient but rather is received by the controller 125. In some embodiments, the associated network data is intercepted when network data is flagged as suspicious.
[0036] In some embodiments, the controller 125 can organize the network data into one or more data flows. Data flows can then be reconstructed based on the network data samples received from the tap. The controller 125 is further discussed in more detail in connection with FIG. 3 .
[0037] FIG. 2 is a block diagram of an exemplary bot detector 200 implementing some embodiments of the present invention. In various embodiments, the bot detector 200 may be coupled to or comprised within the controller 125. In other embodiments, the bot detector 200 is coupled to the communication network 110. In various embodiments, the bot detector 200 is software that is loaded on a digital device. For example, the bot detector 200 may be provided to a user for installation onto their LAN or a network device (e.g., network device 115).
[0038] The exemplary bot detector 200 may comprise a protocol fingerprint module 205, a protocol state description module 210, a port module 215, a signature module 220, and a tracking module 225. Alternative embodiments may comprise more, less, or functionally equivalent modules.
[0039] In various embodiments, the use of an IRC protocol is used for bot command and control. Therefore, detecting the existence or establishment of an IRC channel in the network may indicate a possible botnet C&C communication channel. In one embodiment, the protocol fingerprint module 205 is utilized to detect an IRC C&C channel. The exemplary protocol fingerprint module 205 may comprise input/output related behavior that uniquely identifies a protocol implementation (e.g., version number, feature, vendor, etc.). In some embodiments, a network trace may map routes between the bot server 105 and the network device 115.
[0040] In exemplary embodiments, network data is scanned to detect a bot oriented IRC command, such as .advscan and SCAN, to highlight IRC channels to a potential bot server 105. Stateful protocol fingerprinting analysis by the protocol fingerprint module 205 may be performed to detect bot oriented commands in the IRC channels. For example, instead of simply scanning for .advscan in an input stream, the protocol fingerprint module 205 may first look for an IRC channel establishment (e.g., JOIN and JOIN confirm commands), and then scan for an .advscan message.
[0041] In some embodiments, the protocol fingerprinting module 205 may be extensible to other protocols via protocol feature description using protocol state descriptions provided by the protocol state description module 210 and regular expressions. A description of the IRC protocol is made possible using this technique. For example, if the protocol state description module 210 determines that the protocol being used is IRC, the protocol fingerprint module 205 may be configured by the protocol state description module 210 to detect IRC commands.
[0042] In various embodiments, the signature module 220 provides signatures which identify bot related behavior, known bot servers 105, suspected bot servers, mechanisms to block attacks, mechanisms to block bot propagation, and/or mechanisms that remove bots from network devices 115. These signatures may be provided to the protocol fingerprint module 205 and/or the controller 125 to take corrective action.
[0043] By correlating infection propagation with C&C communication activity, a higher degree of confidence can be ascribed to a suspected bot list. For example, if traffic is observed on a suspected IRC C&C channel and immediately thereafter there is discovery of infection propagation from the IRC server (e.g., bot server 105 or network device 115) that provided the C&C communication, then all nodes that have communicated to the same IRC server are highly suspect. This broadens the visibility of infected systems from those that are observed actively propagating infections to systems that have not been observed actively propagating but have been in communication with a confirmed active bot server 105.
[0044] Furthermore, detection of a central C&C server allows authorities and system administrators to take the central C&C server offline and/or block communications with the central C&C server. Once the C&C server has been neutralized, the bots that may otherwise receive commands from the C&C server are no longer controlled and are, in some examples, unable to function.
[0045] However, because the bot server 105 may be easily neutralized by shutting down the central C&C server, botnets controlled using a Peer-to-Peer (P2P) communications protocol have been developed. Due to the distributed nature of P2P communication channel, it becomes much harder to shut down a P2P controlled botnet.
[0046] In various embodiments, detection of a P2P C&C channel may be performed by the port module 215 detecting communications on a seldom used (non-standard) port. During base-lining, standard well-known ports are marked. For example, all well known ports and services in a network environment may be categorized as “standard.” A standard list may be compiled and stored by the port module 215. In exemplary embodiments, the standard list may comprise all ports defined in Internet RFCs as well as ports and services used by standard versions of Windows and Linux.
[0047] In some embodiments, the port module 215 may “learn” standard ports through observation of network data on a communication network 110. In one example, the software on multiple network devices 115 may transmit and receive network data on a variety of ports. The network data is received by the controller 125 and the port module 215 may update the standard list based on the ports of the network devices 115 that receive and transmit data over a predetermined period of time.
[0048] Any port not on the standard list may be considered a non-standard port. In some embodiments, the port module 215 will mark a number of nodes communicating over a non-standard port over a predetermined period of time a P2P communications channel when the number of nodes is over some threshold (e.g., 3 or 4 nodes). In one example, the port module 215 will mark a potential P2P communications channel when four network devices 115 communicate with each other over a non-standard port within 4 seconds.
[0049] These nodes do not need to be communicating on the same port, as long as the ports are seldom used non-standard ports. For example, the port module 215 may detect P2P chains that use a different port for each leg of the chain. In some embodiments, the time difference between anomaly propagation in the chain may be assumed to be small (e.g., less than 10 seconds). This short time difference allows the tracking module 225 to track various nodes without running into resource constraint issues. In one example, the tracking module 225 identifies network devices 115 that communicate with other network devices 115 over the predetermined period of time. The port module 215 may identify those network devices 115 communicating over non-standard ports. Once the port module 215 detects a network device 115 communicating over a non-standard port, the port module 215 may check the tracking module 225 to determine if any other network device 115 has been communicating over non-standard ports.
[0050] In other embodiments, the tracking module 225, tracks the source and destination of at least some communications over the communication network 110. If a bot server 105 or a potential bot server is detected, the tracking module 225 can provide a list of network devices 115 in communication with the bot server 105 or the potential bot server. In one example, the tracking module 225 can provide a list of nodes in communication with a suspected bot server 105 over a predetermined period of time.
[0051] While existence of a P2P channel is not conclusive evidence of a botnet, network operators may benefit from notification of P2P communications on their networks. If a P2P communication can be correlated to infection propagation via one or more nodes of the P2P chain, then all nodes of the P2P network may become highly suspect as members of a P2P controlled botnet.
[0052] In exemplary embodiments of the present invention, systems may be marked in order to identify infections. For example, any nodes that are not associated with any infection propagation may be placed in a yellow category. These nodes (e.g., network devices 115) may be considered “nodes of interest.” Nodes in an IRC or P2P network where at least one of the nodes (e.g., in a chain of nodes) is observed propagating an infection may be placed in, for example, an orange category. Nodes that are observed to be actively propagating an infection may be placed in a red category. Any nodes that have not been categorized as yellow, orange, or red may be assigned to a green category. In various embodiments, icons associated with nodes may be colored and/or associated with a color category.
[0053] FIG. 3 is a block diagram of an exemplary controller 125 implementing embodiments of the present invention. The controller 125 may be any digital device or software that receives network data. The exemplary controller 125 may comprise bot detection components similar to the bot detector 200 of FIG. 2 including a protocol fingerprint module 305, a protocol state description module 310, and a tracking module 315. In this example, the functions of the tracking module 225 (FIG. 2 ) and the port module 215 (FIG. 2 ) are combined.
[0054] The controller 125 may further comprise a heuristic module 320, a scheduler 325, a fingerprint module 330, a virtual machine pool 335, an analysis environment 340, a signature module 345, and a policy engine 350. In some embodiments, the controller 125 comprises a tap which is further coupled to the communication network 110. In other embodiments, the controller 125 is coupled to an external tap 120 or may be directly coupled to the communication network 110.
[0055] The exemplary heuristic module 320 may receive a copy of network data from the communication network 110. The heuristic module 320 applies heuristics and/or probability analysis to determine if the network data may contain suspicious activity (such as bot related activity). In one example, the heuristic module 320 flags network data as suspicious. The network data can then be buffered and organized into a data flow. The data flow is then provided to the scheduler 325. In some embodiments, the network data is provided directly to the scheduler 325 without buffering or organizing the data flow.
[0056] The heuristic module 320 can perform any heuristic and/or probability analysis. In some embodiments, once a C&C communication channel has been detected or suspected, analysis may be performed to confirm and/or verify the C&C channel. Once the protocol fingerprint module 305 identifies a potential C&C communication channel, network data from the channel is forwarded to the scheduler 325.
[0057] In other embodiments, the heuristic module 320 performs a dark internet protocol (IP) heuristic. A dark IP heuristic can flag network data coming from a bot server 105 that has not previously been identified by the heuristic module 320. The dark IP heuristic can also flag network data going to an unassigned IP address. In an example, an attacker scans random IP addresses of a network to identify an active server or workstation. The dark IP heuristic can flag network data directed to an unassigned IP address.
[0058] The heuristic module 320 can also perform a dark port heuristic. A dark port heuristic can flag network data transmitted to an unassigned or unusual port address. Such network data transmitted to an unusual port can be indicative of a port scan by a worm, hacker, or bot. Further, the heuristic module 320 can flag network data from the bot server 105 or network device 115 that is significantly different than traditional data traffic transmitted by the bot server 105 or network device 115. For example, the heuristic module 320 can flag network data from the bot server 105 such as a laptop that begins to transmit network data that is common to a server.
[0059] The heuristic module 320 can retain data packets belonging to a particular data flow previously copied by the tap 120. In one example, the heuristic module 320 receives data packets from the tap 120 and stores the data packets within a buffer or other memory. Once the heuristic module 320 receives a predetermined number of data packets from a particular data flow, the heuristic module 320 performs the heuristics and/or probability analysis.
[0060] In some embodiments, the heuristic module 320 performs heuristic and/or probability analysis on a set of data packets belonging to a data flow 320 can then continue to receive new data packets belonging to the same data flow. Once a predetermined number of new data packets belonging to the same data flow are received, the heuristic and/or probability analysis can be performed upon the combination of buffered and new data packets to determine a likelihood of suspicious activity.
[0061] In some embodiments, an optional buffer receives the flagged network data from the heuristic module 320. The buffer can buffer and organize the flagged network data into one or more data flows before providing the one or more data flows to the scheduler 325. In various embodiments, the buffer can buffer network data and stall before providing the network data to the scheduler 325. In one example, the buffer stalls the network data to allow other components of the controller 125 time to complete functions or otherwise clear data congestion.
[0062] The scheduler 325 is a module that identifies the network device 115 to receive the copied network data and retrieves a virtual machine associated with the network device 115. A virtual machine may be software that is configured to mimic the performance of a device (e.g., the network device 115). The virtual machine can be retrieved from the virtual machine pool 335.
[0063] In some embodiments, the heuristic module 320 transmits the metadata identifying the network device 115 to receive the copied network data to the scheduler 325. In other embodiments, the scheduler 325 receives one or more data packets of the network data from the heuristic module 320 and analyzes the one or more data packets to identify the network device 115. In yet other embodiments, the metadata can be received from the tap 120.
[0064] The scheduler 325 can retrieve and configure the virtual machine to mimic pertinent performance characteristics of the network device 115. In one example, the scheduler 325 configures characteristics of the virtual machine to mimic only those features of the network device 115 that are affected by the network data copied by the tap 120. The scheduler 325 can determine the features of the network device 115 that are affected by the network data by receiving and analyzing the network data from the tap 120. Such features of the network device 115 can include ports that are to receive the network data, select device drivers that are to respond to the network data and any other devices coupled to or contained within the network device 115 that can respond to the network data. In other embodiments, the heuristic module 320 can determine the features of the network device 115 that are affected by the network data by receiving and analyzing the network data from the tap 120. The heuristic module 320 can then transmit the features of the destination device to the scheduler 325.
[0065] The optional fingerprint module 330 is configured to determine the packet format of the network data to assist the scheduler 325 in the retrieval and/or configuration of the virtual machine. In one example, the fingerprint module 330 determines that the network data is based on a transmission control protocol/internet protocol (TCP/IP). Thereafter, the scheduler 325 will configure a virtual machine with the appropriate ports to receive TCP/IP packets. In another example, the fingerprint module 330 can configure a virtual machine with appropriate ports to receive user datagram protocol/internet protocol (UDP/IP) packets. The fingerprint module 330 can determine any type of packet format of the network data.
[0066] In other embodiments, the optional fingerprint module 330 passively determines a software profile of the network data to assist the scheduler 325 in the retrieval and/or configuration of the virtual machine. The software profile may comprise the operating system (e.g., Linux RH6.2) of the bot server 105 that generated the network data. The determination can be based on analysis of the protocol information of the network data. In an example, the fingerprint module 330 determines that the software profile of network data is Windows XP, SP1. The fingerprint module 330 can then configure a virtual machine with the appropriate ports and capabilities to receive the network data based on the software profile. In other examples, the fingerprint module 330 passes the software profile of the network data to the scheduler 325, and the scheduler 325 either selects or configures the virtual machine based on the profile.
[0067] The virtual machine pool 335 is configured to store virtual machines. The virtual machine pool 335 may include any storage capable of storing virtual machines. In one example, the virtual machine pool 335 stores a single virtual machine that can be configured by the scheduler 325 to mimic the performance of any network device, such as the network device 115 on the communication network 110. The virtual machine pool 335 can store any number of distinct virtual machines that can be configured to simulate the performance of any of the network devices 115.
[0068] The analysis environment 340 is a module that simulates transmission of unencrypted or decrypted network data between the bot server 105 and the network device 115 to identify the effects of malware or illegitimate computer users (e.g., a hacker, computer cracker, or other computer user) by analyzing the simulation of the effects of the network data upon the network device 115 that is carried out on the virtual machine. In exemplary embodiments, there may be multiple analysis environments 340 in order to simulate multiple network data.
[0069] In one example, the analysis environment 340 simulates transmission of the network data between the bot server 105 and the network device 115 to analyze the effects of the network data upon the network device 115 to detect unauthorized activity. As the analysis environment 340 simulates the transmission of the network data, behavior of the virtual machine can be closely monitored for unauthorized activity. If the virtual machine crashes, performs illegal operations, or performs bot related activity, the analysis environment 340 can react. In some embodiments, the analysis environment 340 performs dynamic taint analysis to identify unauthorized activity.
[0070] Once unauthorized activity is detected, the analysis environment 340 can generate the unauthorized activity signature configured to identify network data containing unauthorized activity (e.g., malware attacks or bot related activity). Since the unauthorized activity signature does not necessarily require probabilistic analysis to detect unauthorized activity within network data, unauthorized activity detection based on the unauthorized activity signature may be very fast and save computing time.
[0071] In various embodiments, the unauthorized activity signature may provide code that may be used to eliminate or “patch” portions of network data containing an attack. Further, in some embodiments, the unauthorized activity signature may be used to identify and eliminate (i.e., delete) the malware causing the attack. The unauthorized activity signature may also be used to configure digital devices to eliminate vulnerabilities (e.g., correct system settings such as disabling active-x controls in a browser or updating an operating system.)
[0072] The analysis environment 340 may store the unauthorized activity signature within the signature module 345. The analysis environment 340 may also transmit or command the transmission of the unauthorized activity signature to one or more other controllers 125, bot detectors 200 (e.g., to the signature module 220), network devices 115, switches, and/or servers. By automatically storing and transmitting the unauthorized activity signature, known malware, previously unidentified malware, and the activities of illicit computer users can be quickly controlled and reduced before a computer system is damaged or compromised. The analysis environment 340 is further discussed with respect to FIG. 4 .
[0073] The signature module 345 receives, authenticates, and stores unauthorized activity signatures. The unauthorized activity signatures may be generated by the analysis environment 340 or another controller 125. The unauthorized activity signatures may then be transmitted to the signature module 345 of one, or more controllers 125.
[0074] The policy engine 350 coupled to the heuristic module 320 and is a module that can identify network data as suspicious based upon policies contained within the policy engine 350. In one example, the network device 115 can be a computer designed to attract hackers and/or worms (e.g., a “honey pot”). The policy engine 350 can contain a policy to flag any network data directed to the “honey pot” as suspicious since the “honey pot” should not be receiving any legitimate network data. In another example, the policy engine 350 can contain a policy to flag network data directed to any network device 115 that contains highly sensitive or “mission critical” information.
[0075] The policy engine 350 can also dynamically apply a rule to copy all network data related to network data already flagged by the heuristic module 320. In one example, the heuristic module 320 flags a single packet of network data as suspicious. The policy engine 350 then applies a rule to flag all data related to the single packet (e.g., associated data flows) as suspicious. In some embodiments, the policy engine 350 flags network data related to suspicious network data until the analysis environment 340 determines that the network data flagged as suspicious is related to unauthorized activity.
[0076] The policy engine 350 may scan network data to detect unauthorized activity based upon an unauthorized activity signature. In some embodiments, the policy engine 350 retrieves the unauthorized activity signature from the signature module 345. The network data is then scanned for unauthorized activity based on the unauthorized activity signature.
[0077] The policy engine 350 can scan both the header and body of a packet of network data. In some embodiments, the policy engine 350 scans only the header of the packet for unauthorized activity based on the unauthorized activity signature. If unauthorized activity is found, then no further scanning may be performed. In other embodiments, the policy engine 350 scans the packet contents for unauthorized activity.
[0078] Unauthorized activity may be found by scanning only the header of a packet, the contents of the packet, or both the header and the contents of the packet. As a result, unauthorized activity that might otherwise evade discovery can be detected. In one example, evidence of unauthorized activity may be located within the contents of the packet. By scanning only the contents of the packet, unauthorized activity may be detected.
[0079] If the packet contents or the packet header indicate that the network data contains unauthorized activity, then the policy engine 350, the protocol fingerprint module 305, the heuristic module 320, or the signature module 345 may take action. In one example, the policy engine 350 may generate a rule or command an interceptor module (not shown) to intercept network data from the node that transmitted the network data and delete or bar the packet from the communication network 110. The policy engine 350 and/or the interceptor module may also quarantine, delete, or bar other packets belonging to the same data flow as the unauthorized activity packet.
[0080] Based on a determination that the network data is suspicious, the interceptor module can re-route the associated network data to a virtual machine from the virtual machine pool 335. As discussed herein, the heuristic module 320 can provide information that the network data is suspicious. The interceptor module can intercept all of the network data that is initially flagged by the heuristic module 320. The interceptor module can also base the interception of data on the detection of a malware attack by the analysis environment 340 or a policy or signature by the policy engine 350.
[0081] The interceptor module can provide the intercepted data to the heuristic module 320 for analysis with a heuristic or to the analysis environment 340 to orchestrate the transmission of the intercepted data to detect a malware attack. If no malware attack is detected, the interceptor module can transmit some or all of the intercepted data to the intended recipient (e.g., network device 115.) If a malware attack is detected within the intercepted data, the unauthorized activity signature may be generated by the signature module 345 and transmitted to one or more controllers 125 or other digital devices.
[0082] The interceptor module can redirect network data from the bot server 105 in any number of ways including, but not limited to, configuring a switch, Address Resolution Protocol (ARP) manipulation, or DHCP services.
[0083] The interceptor module may send a request to a switch to redirect network data from any bot server 105 to the controller 125. The switch includes any device configured to receive and direct network data between one or more digital devices. Examples of a switch include, but is not limited to, a router, gateway, bridge, and, or server.
[0084] In some embodiments, executable code is loaded onto the switch. In one example, the executable code configures the switch to direct network data from any bot server 105 to the controller 125. In another example, the executable code allows the interceptor module to transmit a request to the switch to direct network data from the bot server 105 to the controller 125. In some embodiments, the interceptor module configures the router to intercept network data from the bot server 105 for a predetermined time. The predetermined time may be set by the interceptor module, preloaded into the switch, or configured by a user.
[0085] The interceptor module may manipulate dynamic host configuration protocol (DHCP) services to intercept network data. As the bot server 105 transmits network data that is flagged as suspicious or otherwise identified as containing a malware attack. The interceptor module may manipulate DHCP services to assign new IP addresses, associate the controller 125 MAC address with the IP address of the network device 115, or otherwise redirect network data from the bot server 105 to the controller 125.
[0086] In various embodiments, the interceptor module can manipulate the DHCP server to configure the bot server 105 with a gateway IP address which is the same as the controller's IP address to send all network data to the controller 125. In other embodiments, the interceptor module may perform DHCP services for the communication network 110 as a DHCP server.
[0087] In one example of ARP manipulation, the heuristic module 320 or the interceptor module scans the copied network data flagged as suspicious to identify a source IP address and a target IP address. In this example, the source IP address is the IP address of the bot server 105 and the target IP address is the IP address of the network device 115. In some embodiments, the interceptor module may send an ARP reply to the bot server 105. The ARP reply is configured to identify the MAC address of the controller 125 with the IP address of the network device 115. When the bot server 105 receives the ARP reply, the bot server 105 may begin to send network data intended for the destination device to the controller 125.
[0088] In other embodiments, a policy within the policy engine 350 may indicate which IP addresses are bot servers 105. Whenever a bot server 105 sends network data for the first time to a network device 115, the bot server 105 may transmit an ARP request. The network data identifying the source IP address is copied by the tap 120 and the policy within the policy engine 350 can flag the source IP address as a bot server 105. Thereafter, the interceptor module may store the ARP request, and provide the controller 125 MAC address in an ARP reply to the switch and/or the bot server 105. Once the switch and/or the bot server 105 receives the controller 125 MAC address in the ARP reply, the IP address of the digital device (e.g., network device 115) will be associated with the controller 125 MAC address (e.g., in memory storage or cache). Network data intended for the network device 115 may then be transmit from the bot server 105 to the controller 125.
[0089] The bot server 105 may send the network data to any number of digital devices. Before the attack can proceed, the bot server 105 may send a separate ARP request for the IP address of every other digital device the malware wishes to send data to. The controller 125 detects and responds to each ARP request by sending an ARP reply to each request with the controller 125 MAC address. The controller 125 MAC address may be associated with the IP address of the other digital devices on a table within the bot server 105, switch, and/or server (not depicted). The table may be within memory, storage, buffered, and/or cached. As a result, network data transmitted by the bot server 105 to multiple network devices 115 may be intercepted by the controller 125.
[0090] Once the network data is intercepted, the network data is re-routed to the virtual machine, as discussed herein. Because the network data is re-routed, the actual machine or the network device 115 for which the network data is intended may not receive the network data and is, as a result, unaffected. A plurality of the network data can be re-routed to more than one virtual machine at one time (e.g., in parallel.) Thus, if the network data intended for a plurality of the network devices 115 is flagged as suspicious, or as coming from the device that has previously been deemed suspicious (e.g., the bot server 105), the interceptor module can select a plurality of virtual machines on which to test the suspicious network data.
[0091] The policy engine 350 may scan network data to detect unauthorized activity (e.g., including some bot related activity) based upon an unauthorized activity signature. In some embodiments, the policy engine 350 retrieves the unauthorized activity signature from the signature module 345. The network data is then scanned for unauthorized activity based on the unauthorized activity signature. The policy engine 350 can also flag network data as suspicious based on policies, as discussed herein.
[0092] Although FIG. 3 depicts various modules comprising the controller 125, fewer or more modules can comprise the controller 125 and still fall within the scope of various embodiments.
[0093] FIG. 4 is a block diagram of an exemplary analysis environment 340, in accordance with some embodiments of the present invention. The analysis environment 340 comprises a replayer 405, a virtual switch 410, and a virtual machine 415. The replayer 405 is a module that receives network data that has been flagged by the heuristic module 320 and replays the network data in the analysis environment 340. In some embodiments, the replayer 405 mimics the behavior of the infected bot server 105 in transmitting the flagged network data. There can be any number of replayers 405 simulating the transmission of network data between nodes on the communication network (e.g., the bot server 105 and the network device 115). In a further embodiment, the replayer 405 dynamically modifies session variables, as is appropriate, to emulate a “live” client or server of the protocol sequence being replayed. In one example, dynamic variables that may be dynamically substituted include dynamically assigned ports, transaction IDs, and any other variable that is dynamic to each protocol session. In other embodiments, the network data received from the heuristic module 205 is transmitted to the virtual machine 415 without a replayer 405.
[0094] The virtual switch 410 is a module that is capable of forwarding packets of flagged network data to the virtual machine 415. The virtual switch 410 simulates network device 115. The virtual switch 410 can route the data packets of the data flow to the correct ports of the virtual machine 415.
[0095] The virtual machine 415 is a representation of the network device 115 that can be provided to the analysis environment 340 by the scheduler 325. In one example, the scheduler 325 retrieves a virtual machine 415 from the virtual machine pool 335 and configures the virtual machine 415 to mimic the network device 115. The configured virtual machine 415 is then provided to the analysis environment 340 where it can receive flagged network data from the virtual switch 410.
[0096] As the analysis environment 340 simulates the transmission of the network data, behavior of the virtual machine 415 can be closely monitored for unauthorized activity. If the virtual machine 415 crashes, performs illegal operations, performs abnormally, or allows access of data to an unauthorized computer user, the analysis environment 340 can react.
[0097] In exemplary embodiments, virtual machines may be used to detect C&C channels and botnet infected systems using the C&C channels. C&C channel detection may occur in a replay virtual machine environment or in a direct entry virtual machine environment. While replay virtual analysis of virtual machines may be leveraged to extract C&C channel information, this may not be possible for all infection protocols. For infections protocols that can be replayed to result in a full bot infection, this technique may yield positive results. For infection protocols that do not go proceed to completion due to an inability to effectively replay unknown worms protocols, for example, the replay environment may not result in a full infection of the virtual machine 415. This may result in a denial of C&C channel information extraction, which will only become evident post-infection. In those instances, the analysis environment 340 may flag the devices involved in the suspected C&C channel as possibly infected with a bot and continue to track the nodes that communicate with those devices that participate within the suspected C&C channel.
[0098] Passive replay virtual machine environments may be effective for C&C channel discovery, since a passive worm may introduce no new worm protocol. Instead, a passive worm may merely piggyback on an existing protocol. Therefore, the existing passive worm replay may be adequate to detect a full bot infection. Passive replay of, for example, web based exploits may be extended to result in full infection and extraction of C&C channel information. Direct entry virtual machine environments are effective in extracting C&C channel information, since there is no need to replay an unknown worm protocol.
[0099] In some embodiments, the analysis environment 340 performs dynamic taint analysis to identify unauthorized activity. For a malware attack to change the execution of an otherwise legitimate program, the malware attack may cause a value that is normally derived from a trusted source to be derived from the user's own input. Program values (e.g., jump addresses and format strings) are traditionally supplied by a trusted program and not from external untrusted inputs. Malware, however, may attempt to exploit the program by overwriting these values.
[0100] In one example of dynamic taint analysis, all input data from untrusted or otherwise unknown sources are flagged. Program execution of programs with flagged input data is then monitored to track how the flagged data propagates (i.e., what other data becomes tainted) and to check when the flagged data is used in dangerous ways. For example, use of tainted data as jump addresses or format strings often indicates an exploit of a vulnerability such as a buffer overrun or format string vulnerability.
[0101] In some embodiments, the analysis environment 340 monitors and analyzes the behavior of the virtual machine 415 in order to determine a specific type of malware or the presence of an illicit computer user. The analysis environment 340 can also generate computer code configured to eliminate new viruses, worms, bots, or other malware. In various embodiments, the analysis environment 340 can generate computer code configured to identify data within the network data indicative of a malware attack, repair damage performed by malware, or the illicit computer user. By simulating the transmission of suspicious network data and analyzing the response of the virtual machine, the analysis environment 340 can identify known and previously unidentified malware and the activities of illicit computer users before a computer system is damaged or compromised.
[0102] Once the virtual machine is infected, via either replay or direct entry, the environment can wait for an outbound domain name system (DNS) request. The requested name in the DNS request is likely a C&C channel. A pseudo-DNS server in the virtual machine environment can respond to this request with an IP address mapped to an internal-to-virtual machine-analysis pseudo-server. If an outbound IRC or web request is made to the supplied IP address, then this confirms the existence of the C&C channel.
[0103] In some embodiments, all outbound DNS requires may be logged in a circular buffer (not shown). Once a C&C channel DNS name is identified, a search may be performed on all entries in the buffer for other source IP addresses that have requested the same DNS name. These source IP addresses are now highly suspect to be infected with the same bot or malware family that infected the virtual machine, even though these other IP addresses may not have been acknowledged as propagating an infection.
[0104] Once a C&C DNS name is discovered, the name may be communicated to all other devices as well as a cloud server. This allows other distributed devices to detect attempts to connect to the same C&C channel.
[0105] FIG. 5 is a flowchart 500 of an exemplary method for detecting a C&C channel of a bot. In step 505, the system (e.g., controller 125 and/or bot detector 200) determines if there is communication detected in the communication network 110. The determination may be performed, in accordance with some embodiments, by the tap 120, the bot detector 200, and/or the controller 125. If there is communication detected, then in step 510, data within the communication may be scanned. In one embodiment, the data may be copied. For example, the network data from the network device 115 to the bot server 105 may be copied by the tap 120. The network data is then sent from the tap 120 to the controller 125 for analysis. In an alternative embodiment, the data may be scanned directly by, for example, the bot detector 200.
[0106] In step 515, a bot communication analysis is performed. As discussed herein, the bot detector 200 or the controller 125 may utilizes various modules (e.g., protocol fingerprint module 305, heuristic module 320, and analysis environment 340) to determine whether the copied network data contains a possible bot communication or may otherwise be potentially harmful to the network device 115 for which the copied network data may be intended. Subsequently, suspicious nodes can be flagged. If the controller 125 or bot detector 200 does not identify the copied network data as possibly containing a possible bot communication, the network data may be transmitted to the intended destination (e.g., network device 115).
[0107] As described herein, several different methods may be utilized to detect a C&C communication within a channel on the communication network 110. In one embodiment, a fingerprint module 205 or 305 may scans for a bot oriented command in an IRC channel including IRC channel establishment commands or messages.
[0108] In an alternative embodiment, a port module 215 may monitor the use of non-standard ports. During base-lining a list of standard ports is compiled. Communications not originating from a standard port may be considered non-standard or an anomaly. As such, associated nodes may be flagged (e.g., color coded) and tracked.
[0109] In a further embodiment, a virtual machine may be utilized to detect C&C communication channels. The C&C communication channel detection may occur in a replay virtual machine environment or in a direct entry virtual machine environment. Accordingly, a virtual machine is retrieved which is used to mimic the network device 115. Intercepted or replayed network data obtained from the communication channel is transmitted to the virtual machine. The virtual machine response is then analyzed to determine if the virtual machine is infected. In some embodiments, the analysis environment 340 may wait for an outbound domain name system (DNS) request, which likely identifies the C&C channel. A pseudo-DNS server in the virtual machine can respond to the request with an IP mapped to an internal-to-virtual machine-analysis pseudo-server. If the outbound IRC or web request is made to the supplied IP address, then this confirms a C&C channel.
[0110] If a suspected bot communication is detected in step 520, then a recovery process may be performed in step 525. In one embodiment, the associated devices may be flagged and/or proper users and administrators notified. For example, any nodes that are not associated with any infection propagation may be placed in a yellow category. Nodes in an IRC or P2P network where at least one of the nodes (e.g., in a chain of nodes) is observed propagating an infection may be placed in, for example, an orange category. Nodes that are observed to be actively propagating an infection may be placed in a red category. Any nodes that have not been categorized as yellow, orange, or red may be assigned to a green category.
[0111] In another embodiment, a router (i.e., switch) may be configured to direct all data received from the bot server 105 (e.g., from the source IP address of the bot server 105) to the controller 125. As a result, all the network data from the bot server 105, not only that which is transmitted to the network device 115, may be intercepted.
[0112] FIG. 6 is a block diagram of the controller 125, in accordance with one embodiment of the present invention. The controller 125 comprises a processor 600, a memory system 605, a storage system 610, an I/O interface 615, and a communication network interface 620 which are all coupled to a system bus 625. The processor 600 is configured to execute executable instructions. In some embodiments, the processor 600 comprises circuitry or any one or more processors capable of processing the executable instructions.
[0113] The memory system 605 is any memory configured to store data. Some examples of the memory system 605 include storage devices, such as RAM or ROM.
[0114] The storage system 610 is any storage configured to retrieve and store data (e.g., a computer readable medium). Some examples of the storage system 610 are flash drives, hard drives, optical drives, and/or magnetic tape. The storage system 610 can comprise a database or other data structure configured to hold and organize data (e.g., network data, copies of network data, buffered data.) In some embodiments, the controller 125 includes memory in the form of RAM and storage in the form of flash data. The memory system 605 and/or the storage system 610 can comprise cache and buffers configured to retain network data or copies of network data.
[0115] The input/output (I/O) interface 615 is any device that can receive input and provide output to a user. The I/O interface 615 can be, but is not limited to, a keyboard, a mouse, a touchscreen, a keypad, a biosensor, or floppy disk drive.
[0116] The communication network interface 620 can be coupled to any user device via the link 630 through link 635. The communication network interface 620 may support communication over a USB connection, a firewire connection, an Ethernet connection, a serial connection, a parallel connection, or an ATA connection. The communication network interface 620 may also support wireless communication (e.g., 802.11a/b/g/n or wireless USB). It will be apparent to those skilled in the art that the communication network interface 620 can support many wired and wireless standards.
[0117] Although only two links (630 and 635) are depicted in FIG. 6 , there may be any number of links. In various embodiments, there may be one link 630 used by the tap 120 to transparently copy network data from the communication network 110. The other links may be used by the controller 125 to intercept data from one or more bot server 105 in parallel. In one example, the controller 125 comprises multiple IP addresses that may be broadcast from different links. Network data may be intercepted from different infected devices 105 by different links.
[0118] The above-described modules can be comprised of instructions that are stored on storage media (e.g., computer readable media). The instructions can be retrieved and executed by a processor (e.g., the processor 600). Some examples of instructions include software, program code, and firmware. Some examples of storage media comprise memory devices and integrated circuits. The instructions are operational when executed by the processor to direct the processor to operate in accordance with embodiments of the present invention. Those skilled in the art are familiar with instructions, processor(s), and storage media.
[0119] While various embodiments have been described above, it should be understood that they have been presented by way of example only, and not limitation. Thus, the breadth and scope of a preferred embodiment should not be limited by any of the above-described exemplary embodiments.
Claims
What is claimed is:
1. A method for detecting a communication channel of a bot, comprising:
detecting presence of a suspected command and control communication channel between a first network device and a second network device, the suspected command and control communication channel having an increased probability of being used for bot communication;
identifying the communication channel of the bot, the communication channel of the bot being a command and control communication channel permitting remote control of all or a portion of the second network device without authorization by a user of the second network device, the identifying comprising:
scanning data flow within the detected suspected command and control communication channel for a bot communication;
determining a first of a plurality of protocols and corresponding ports associated with the data flow; and
determining if a suspected bot communication exists within the data flow by analyzing a response of a virtual machine to the data flow, the virtual machine being configurable with ports corresponding to any of the plurality of protocols including the first protocol associated with the data flow, the virtual machine configured with the corresponding ports associated with the data flow; and
if a suspected bot communication is detected indicating existence of the communication channel of the bot, performing a recovery process.
2. The method of claim 1 wherein detecting presence of the suspected command and control communication channel occurs via a packet level analysis.
3. The method of claim 1 wherein detecting presence of the suspected command and control communication channel occurs via a virtual machine level analysis.
4. The method of claim 1 wherein determining if a suspected bot communication exists comprises detecting if any peer-to-peer (P2P) anomaly communication chains are present.
5. The method of claim 4 wherein the detecting if any peer-to-peer (P2P) anomaly communication chains are present comprises determining if any chain of nodes communicating to non-standard ports exceeds a predetermined threshold.
6. The method of claim 1 where determining if a suspected bot communication exists comprises identifying and scanning at least one network device.
7. The method of claim 1 wherein determining if a suspected bot communication exists comprises identifying an internet relay chat (IRC) channel establishment command.
8. The method of claim 1 wherein determining if a suspected bot communication exists comprises simulating data flow between a replayer and a virtual machine and analyzing a response of the virtual machine.
9. The method of claim 1 wherein determining if a suspected bot communication exists comprises analyzing a response of a virtual machine.
10. The method of claim 1 wherein performing the recovery process comprises providing notification to at least one administrator.
11. The method of claim 1 wherein performing the recovery process comprises assigning color codes for review by an administrator.
12. The method of claim 1 wherein performing the recovery process comprises performing infection propagation analysis on nodes associated with the bot communication.
13. The method of claim 1 wherein performing the recovery process comprises determining an identity of the first network device.
14. The method of claim 1 wherein performing the recovery process comprises redirecting all communications from the first network device to a virtual machine.
15. A system for detecting communication channels of a bot, comprising:
a processor;
a tap configured to access data from a detected suspected command and control communication channel, the detected suspected command and control communication channel having an increased probability of being used for bot communication; and
a bot detector comprising instructions executable by the processor, the bot detector being configured to identify the communication channels of the bot each communication channel of the bot being a command and control communication channel permitting remote control of all or a portion of the second network device without authorization by a user of the second network device, the identifying comprising instructions to:
scan the data from the detected suspected command and control communication channel for bot communication;
determining a first of a plurality of protocols and corresponding ports associated with the data flow; and
determine if the data from the detected suspected command and control communication channel comprises a bot communication by analyzing a response of a virtual machine to the data flow, the virtual machine being configurable with ports corresponding to any of the plurality of protocols including the first protocol associated with the data flow, the virtual machine configured with the corresponding ports associated with the data flow.
16. The system of claim 15 further comprising a router.
17. The system of claim 15 further comprising an interceptor module.
18. The system of claim 15 wherein the bot detector comprises a protocol fingerprint module configured to determine if the data comprises a control and command message.
19. The system of claim 15 wherein the bot detector comprises a port module configured to determine if the data is being sent from a non-standard port.
20. The system of claim 15 wherein the bot detector comprises a heuristic module configured to determine using a virtual machine whether the data comprises a bot communication.
21. A non-transitory computer readable medium having embodied thereon instructions executable by a processor for performing a method operations for detecting communication channels of a bot, comprising:
detecting presence of a suspected command and control communication channel between a first network device and a second network device, the suspected command and control communication channel having an increased probability of being used for bot communication;
identifying the communication channel of the bot the communication channel of the bot being a command and control communication channel permitting remote control of all or a portion of the second network device without authorization by a user of the second network device, the identifying comprising:
scanning data flow within the detected suspected command and control communication channel for a bot communication;
determining a first of a plurality of protocols and corresponding ports associated with the data flow; and
determining if a suspected bot communication exists within the data flow by analyzing a response of a virtual machine to the data flow, the virtual machine being configurable with ports corresponding to any of the plurality of protocols including the first protocol associated with the data flow, the virtual machine configured with the corresponding ports associated with the data flow; and
if a suspected bot communication is detected indicating existence of the communication channel of the bot, performing a recovery process.
22. The method of claim 1 being conducted by a hardware processor within a controller.
23. The method of claim 1, wherein the first of the plurality of protocols includes transmission control protocol/internet protocol (TCP/IP).
24. A method for detecting a communication channel of a bot, comprising:
detecting presence of a suspected command and control communication channel between a first network device and a second network device, the suspected command and control communication channel having an increased probability of being used for bot communication;
identifying the communication channel of the bot, the communication channel of the bot being a command and control communication channel permitting remote control of all or a portion of the second network device without authorization by a user of the second network device, the identifying comprising:
scanning data flow within the detected suspected command and control communication channel for a bot communication; and
determining if a suspected bot communication exists within the data flow; and
if a suspected bot communication is detected indicating existence of the communication channel of the bot, performing a recovery process, wherein performing a recovery process comprises determining suspicious nodes by identifying nodes that have participated in communications associated with the suspected command and control communication channel.
25. The method of claim 24, wherein the determining of the suspicious nodes by identifying nodes that have participated in communications associated with the suspected command and control communication channel further comprises identifying nodes that have participated in an internet relay chat (IRC) communication with a potential bot server.
26. The method of claim 24, wherein the determining of the suspicious nodes by identifying nodes that have participated in communications associated with the suspected command and control communication channel further comprises identifying nodes that have participated in a suspicious P2P network.
27. The method of claim 24, wherein the determining of the suspicious nodes by identifying nodes that have participated in communications associated with the suspected command and control communication channel further comprises identifying nodes that have made an outbound DNS request for a DNS name associated with a suspected command and control communication channel.
28. A method for detecting a communication channel of a bot, comprising:
detecting presence of a suspected command and control communication channel between a first network device and a second network device, the suspected command and control communication channel having an increased probability of being used for bot communication;
identifying the communication channel of the bot, the communication channel of the bot being a command and control communication channel permitting remote control of all or a portion of the second network device without authorization by a user of the second network device, the identifying comprising:
organizing network data within the detected suspected command and control communication channel into one or more data flows by utilizing protocol implementation information identified by a protocol fingerprint module;
scanning the one or more data flows within the detected suspected command and control communication channel for a bot communication; and
determining if a suspected bot communication exists within the data flow; and
if a suspected bot communication is detected indicating existence of the communication channel of the bot, performing a recovery process.
29. The method of claim 28, wherein the organizing of the network data within the detected suspected command and control communication channel into one or more data flows by utilizing protocol implementation information identified by a protocol fingerprint module further comprises organizing the network data into one or more internet relay chat (IRC) communication data flows.
30. The method of claim 28, wherein the organizing of the network data within the detected suspected command and control communication channel into one or more data flows by utilizing protocol implementation information identified by a protocol fingerprint module further comprises organizing the network data into one or more P2P communication data flows.
31. A method for detecting a communication channel of a bot, comprising:
detecting presence of a suspected command and control communication channel between a first network device and a second network device, the suspected command and control communication channel having an increased probability of being used for bot communication;
identifying the communication channel of the bot, the communication channel of the bot being a command and control communication channel permitting remote control of all or a portion of the second network device without authorization by a user of the second network device, the identifying comprising:
scanning data flow within the detected suspected command and control communication channel for a bot communication;
determining protocols and ports associated with the data flow; and
determining if a suspected bot communication exists within the data flow by analyzing a response of a virtual machine configured with the protocols and ports to receive and respond to the data flow;
if a suspected bot communication is detected
indicating existence of the communication channel of the bot; and
performing a recovery process, wherein performing a recovery process comprises determining suspicious nodes by identifying nodes that have participated in communications associated with the suspected command and control communication channel.
Patent Citations (151)
| Patent | Date | Inventor | Cited By |
|---|---|---|---|
| US4292580(A) | 1981-09-01 | Ott et al. | Applicant |
| US5175732(A) | 1992-12-01 | Hendel et al. | Applicant |
| US5440723(A) | 1995-08-01 | Arnold et al. | Applicant |
| US5657473(A) | 1997-08-01 | Killean et al. | Applicant |
| US5978917(A) | 1999-11-01 | Chi | Applicant |
| US6269330(B1) | 2001-07-01 | Cidon et al. | Applicant |
| US6298445(B1) | 2001-10-01 | Shostack | Applicant |
| US6357008(B1) | 2002-03-01 | Nachenberg | Applicant |
| US6424627(B1) | 2002-07-01 | Sørhaug et al. | Applicant |
| US6487666(B1) | 2002-11-01 | Shanklin et al. | Applicant |
| US6493756(B1) | 2002-12-01 | O'Brien et al. | Applicant |
| US6550012(B1) | 2003-04-01 | Villa et al. | Applicant |
| US6775657(B1) | 2004-08-01 | Baker | Applicant |
| US6832367(B1) | 2004-12-01 | Choi et al. | Applicant |
| US6898632(B2) | 2005-05-01 | Gordy | Applicant |
| US6981279(B1) | 2005-12-01 | Arnold et al. | Applicant |
| US7007107(B1) | 2006-02-01 | Ivchenko et al. | Applicant |
| US7043757(B2) | 2006-05-01 | Hoefelmeyer et al. | Applicant |
| US7069316(B1) | 2006-06-01 | Gryaznov | Examiner |
| US7080408(B1) | 2006-07-01 | Pak | Applicant |
| US7093239(B1) | 2006-08-01 | van der Made | Applicant |
| US7100201(B2) | 2006-08-01 | Izatt | Applicant |
| US7159149(B2) | 2007-01-01 | Spiegel | Applicant |
| US7231667(B2) | 2007-06-01 | Jordan | Applicant |
| US7240364(B1) | 2007-07-01 | Branscomb et al. | Applicant |
| US7240368(B1) | 2007-07-01 | Roesch | Applicant |
| US7287278(B2) | 2007-10-01 | Liang | Applicant |
| US7308716(B2) | 2007-12-01 | Danford et al. | Applicant |
| US7356736(B2) | 2008-04-01 | Natvig | Applicant |
| US7386888(B2) | 2008-06-01 | Liang | Applicant |
| US7392542(B2) | 2008-06-01 | Bucher | Applicant |
| US7418729(B2) | 2008-08-01 | Szor | Applicant |
| US7428300(B1) | 2008-09-01 | Drew et al. | Applicant |
| US7441272(B2) | 2008-10-01 | Durham | Applicant |
| US7448084(B1) | 2008-11-01 | Apap et al. | Applicant |
| US7458098(B2) | 2008-11-01 | Judge et al. | Applicant |
| US7464404(B2) | 2008-12-01 | Carpenter et al. | Applicant |
| US7464407(B2) | 2008-12-01 | Nakae et al. | Applicant |
| US7480773(B1) | 2009-01-01 | Reed | Applicant |
| US7487543(B2) | 2009-02-01 | Arnold et al. | Applicant |
| US7496960(B1) | 2009-02-01 | Chen et al. | Examiner |
| US7496961(B2) | 2009-02-01 | Zimmer et al. | Applicant |
| US7523493(B2) | 2009-04-01 | Liang | Applicant |
| US7530104(B1) | 2009-05-01 | Thrower et al. | Applicant |
| US7540025(B2) | 2009-05-01 | Tzadikario | Applicant |
| US7565550(B2) | 2009-07-01 | Liang et al. | Applicant |
| US7603715(B2) | 2009-10-01 | Costa et al. | Applicant |
| US7639714(B2) | 2009-12-01 | Stolfo et al. | Applicant |
| US7644441(B2) | 2010-01-01 | Schmid et al. | Applicant |
| US7676841(B2) | 2010-03-01 | Sobchuk et al. | Examiner |
| US7698548(B2) | 2010-04-01 | Shelest et al. | Examiner |
| US7707633(B2) | 2010-04-01 | Danford et al. | Applicant |
| US7779463(B2) | 2010-08-01 | Stolfo et al. | Applicant |
| US7784097(B1) | 2010-08-01 | Stolfo et al. | Applicant |
| US7849506(B1) | 2010-12-01 | Dansey et al. | Applicant |
| US7904959(B2) | 2011-03-01 | Sidiroglou et al. | Applicant |
| US7908660(B2) | 2011-03-01 | Bahl | Applicant |
| US7996556(B2) | 2011-08-01 | Raghavan et al. | Applicant |
| US7996905(B2) | 2011-08-01 | Arnold et al. | Applicant |
| US8028338(B1) | 2011-09-01 | Schneider et al. | Applicant |
| US8069484(B2) | 2011-11-01 | McMillan et al. | Applicant |
| US8087086(B1) | 2011-12-01 | Lai et al. | Applicant |
| US2001/0047326(A1) | 2001-11-01 | Broadbent et al. | Applicant |
| US2002/0018903(A1) | 2002-02-01 | Kokubo et al. | Applicant |
| US2002/0038430(A1) | 2002-03-01 | Edwards et al. | Examiner |
| US2002/0091819(A1) | 2002-07-01 | Melchione et al. | Applicant |
| US2002/0144156(A1) | 2002-10-01 | Copeland, III | Applicant |
| US2002/0162015(A1) | 2002-10-01 | Tang | Applicant |
| US2002/0184528(A1) | 2002-12-01 | Shevenell et al. | Examiner |
| US2002/0188887(A1) | 2002-12-01 | Largman et al. | Applicant |
| US2002/0194490(A1) | 2002-12-01 | Halperin et al. | Applicant |
| US2003/0074578(A1) | 2003-04-01 | Ford et al. | Applicant |
| US2003/0084318(A1) | 2003-05-01 | Schertz | Applicant |
| US2003/0115483(A1) | 2003-06-01 | Liang | Applicant |
| US2003/0188190(A1) | 2003-10-01 | Aaron | Applicant |
| US2003/0200460(A1) | 2003-10-01 | Morota et al. | Applicant |
| US2004/0019832(A1) | 2004-01-01 | Arnold et al. | Applicant |
| US2004/0047356(A1) | 2004-03-01 | Bauer | Applicant |
| US2004/0083408(A1) | 2004-04-01 | Spiegel | Applicant |
| US2004/0111531(A1) | 2004-06-01 | Staniford et al. | Applicant |
| US2004/0165588(A1) | 2004-08-01 | Pandya | Applicant |
| US2004/0236963(A1) | 2004-11-01 | Danford et al. | Applicant |
| US2004/0243349(A1) | 2004-12-01 | Greifeneder | Applicant |
| US2004/0249911(A1) | 2004-12-01 | Alkhatib | Applicant |
| US2004/0268147(A1) | 2004-12-01 | Wiederin et al. | Applicant |
| US2005/0033960(A1) | 2005-02-01 | Vialen et al. | Applicant |
| US2005/0033989(A1) | 2005-02-01 | Poletto | Applicant |
| US2005/0086523(A1) | 2005-04-01 | Zimmer et al. | Applicant |
| US2005/0091513(A1) | 2005-04-01 | Mitomo | Applicant |
| US2005/0114663(A1) | 2005-05-01 | Cornell | Applicant |
| US2005/0125195(A1) | 2005-06-01 | Brendel | Applicant |
| US2005/0157662(A1) | 2005-07-01 | Bingham et al. | Applicant |
| US2005/0183143(A1) | 2005-08-01 | Anderholm | Applicant |
| US2005/0201297(A1) | 2005-09-01 | Peikari | Applicant |
| US2005/0210533(A1) | 2005-09-01 | Copeland | Applicant |
| US2005/0238005(A1) | 2005-10-01 | Chen et al. | Applicant |
| US2005/0265331(A1) | 2005-12-01 | Stolfo | Applicant |
| US2006/0015715(A1) | 2006-01-01 | Anderson | Applicant |
| US2006/0021054(A1) | 2006-01-01 | Costa et al. | Applicant |
| US2006/0075496(A1) | 2006-04-01 | Carpenter et al. | Applicant |
| US2006/0095968(A1) | 2006-05-01 | Portolani | Applicant |
| US2006/0101516(A1) | 2006-05-01 | Sudaharan | Applicant |
| US2006/0101517(A1) | 2006-05-01 | Banzhof et al. | Applicant |
| US2006/0117385(A1) | 2006-06-01 | Mester et al. | Applicant |
| US2006/0123477(A1) | 2006-06-01 | Raghavan | Applicant |
| US2006/0161983(A1) | 2006-07-01 | Cothrell | Applicant |
| US2006/0161987(A1) | 2006-07-01 | Levy-Yurista | Applicant |
| US2006/0164199(A1) | 2006-07-01 | Gilde et al. | Applicant |
| US2006/0184632(A1) | 2006-08-01 | Marino et al. | Applicant |
| US2006/0191010(A1) | 2006-08-01 | Benjamin | Applicant |
| US2006/0221956(A1) | 2006-10-01 | Narayan et al. | Applicant |
| US2007/0006288(A1) | 2007-01-01 | Mayfield et al. | Applicant |
| US2007/0006313(A1) | 2007-01-01 | Porras et al. | Applicant |
| US2007/0016951(A1) | 2007-01-01 | Piccard | Applicant |
| US2007/0033645(A1) | 2007-02-01 | Jones | Applicant |
| US2007/0038943(A1) | 2007-02-01 | FitzGerald et al. | Applicant |
| US2007/0064689(A1) | 2007-03-01 | Shin et al. | Applicant |
| US2007/0094730(A1) | 2007-04-01 | Bhikkaji et al. | Applicant |
| US2007/0192500(A1) | 2007-08-01 | Lum | Applicant |
| US2007/0192858(A1) | 2007-08-01 | Lum | Applicant |
| US2007/0198275(A1) | 2007-08-01 | Malden et al. | Applicant |
| US2007/0250930(A1) | 2007-10-01 | Aziz | Applicant |
| US2008/0005782(A1) | 2008-01-01 | Aziz | Applicant |
| US2008/0072326(A1) | 2008-03-01 | Danford et al. | Applicant |
| US2008/0080518(A1) | 2008-04-01 | Hoeflin et al. | Applicant |
| US2008/0120722(A1) | 2008-05-01 | Sima | Applicant |
| US2008/0141376(A1) | 2008-06-01 | Clausen | Applicant |
| US2008/0222729(A1) | 2008-09-01 | Chen et al. | Applicant |
| US2008/0295172(A1) | 2008-11-01 | Bohacek | Applicant |
| US2008/0301810(A1) | 2008-12-01 | Lehane | Applicant |
| US2009/0031423(A1) | 2009-01-01 | Liu et al. | Applicant |
| US2009/0083369(A1) | 2009-03-01 | Marmor | Applicant |
| US2009/0083855(A1) | 2009-03-01 | Apap et al. | Applicant |
| US2009/0089879(A1) | 2009-04-01 | Wang | Applicant |
| US2009/0094697(A1) | 2009-04-01 | Provos et al. | Applicant |
| US2009/0271867(A1) | 2009-10-01 | Zhang | Applicant |
| US2009/0300761(A1) | 2009-12-01 | Park et al. | Applicant |
| US2009/0328221(A1) | 2009-12-01 | Blumfield et al. | Applicant |
| US2010/0054278(A1) | 2010-03-01 | Stolfo et al. | Applicant |
| US2010/0083376(A1) | 2010-04-01 | Pereira | Applicant |
| US2010/0115621(A1) | 2010-05-01 | Staniford et al. | Applicant |
| US2010/0281541(A1) | 2010-11-01 | Stolfo et al. | Applicant |
| US2010/0281542(A1) | 2010-11-01 | Stolfo et al. | Applicant |
| US2011/0093951(A1) | 2011-04-01 | Aziz | Applicant |
| US2011/0099633(A1) | 2011-04-01 | Aziz | Applicant |
| US2011/0247072(A1) | 2011-10-01 | Staniford et al. | Applicant |
| US2011/0314546(A1) | 2011-12-01 | Aziz et al. | Applicant |
| GB2439806 | 2008-01-01 | Applicant | |
| WO26928(A2) | 2002-01-01 | Applicant | |
| WO206928(A2) | 2002-01-01 | Applicant | |
| WO223805(A2) | 2002-03-01 | Applicant |
Non-Patent Literature (26)
- Whyte et al. “DNS-Based Detection of Scannin Works in an Enterprise Network,” Proceedings of the 12th Annual Network and Distributed System Security Symposium, Feb. 2005. 15 pages.Applicant
- Kristoff, J. “Botnets, Detection and Mitigation: DNS-Based Techniques,” NU Security Day 2005, 23 pages.Applicant
- IEEE Xplore Digital Library Search results for “detection of unknown computer worms”. http://ieeexplore.ieee.org/search/searchresult.jsp?SortField=Score&SortOrder=desc&ResultC . . . Accessed on Aug. 28, 2009.Applicant
- AltaVista Advanced Search Results. “Event Orchestrator”. http://www.altavista.com/web/results?itag=ody&pg=aq&aqmode=s&aqa=Event+Orchestrator . . . Accessed on Sep. 3, 2009.Applicant
- AltaVista Advanced Search Results. “attack vector identifier”. http://www.altavista.com/web/results?itag=ody&pg=aq&aqmode=s&aqa=attack+vector+ide . . . Accessed on Sep. 15, 2009.Applicant
- Costa, M. et al. “Vigilante: End-to-End Containment of Internet Worms,” SOSP '05, Oct. 23-26, 2005, Association for Computing Machinery, Inc., Brighton U.K.Applicant
- Chaudet, C. et al. “Optimal Positioning of Active and Passive Monitoring Devices,” International Conference on Emerging Networking Experiments and Technologies, Proceedings of the 2005 ACM Conference on Emerging Network Experiment and Technology, Oct. 2005, pp. 71-82, CoNEXT '05, Toulousse, France.Applicant
- Crandall, J.R. et al., “Minos:Control Data Attack Prevention Orthognal to Memory Model,” 37th International Symposium on Microarchitecture, Dec. 2004, Portland, Oregon.Applicant
- Kim, H. et al., “Autograph: Toward Automated, Distributed Worm Signature Detection,” Proceedings of the 13th Usenix Security Symposium (Security 2004), Aug. 2004, pp. 271-286, San Diego.Applicant
- Kreibich, C. et al., “Honeycomb—Creating Intrusion Detection Signatures Using Honeypots,” 2nd Workshop on Hot Topics in Networks (HotNets-11), 2003, Boston, USA.Applicant
- Newsome, J. et al., “Polygraph: Automatically Generating Signatures for Polymorphic Worms,” In Proceedings of the IEEE Symposium on Security and Privacy, May 2005.Applicant
- Newsome, J. et al., “Dynamic Taint Analysis for Automatic Detection, Analysis, and Signature Generation of Exploits on Commodity Software,” In Proceedings of the 12th Annual Network and Distributed System Security, Symposium (NDSS '05), Feb. 2005.Applicant
- Singh, S. et al., “Automated Worm Fingerprinting,” Proceedings of the ACM/USENIX Symposium on Operating System Design and Implementation, Dec. 2004, San Francisco, California.Applicant
- Margolis, P.E., Random House Webster's “Computer & Internet Dictionary 3rd Edition,” ISBN 0375703519, Dec. 1998.Applicant
- Silicon Defense, “Worm Containment in the Internal Network”, Mar. 2003, pp. 1-25.Applicant
- Nojiri, D. et al., “Cooperative Response Strategies for Large Scale Attack Mitigation”, DARPA Information Survivability Conference and Exposition, Apr. 22-24, 2003, vol. 1, pp. 293-302.Applicant
- Moore, D. et al., “Internet Quarantine: Requirements for Containing Self-Propagating Code”, INFOCOM, Mar. 30-Apr. 3, 2003, vol. 3, pp. 1901-1910.Applicant
- Williamson, Matthew M., “Throttling Viruses: Restricting Propagation to Defeat Malicious Mobile Code”, ACSAC Conference, Dec. 2002, Las Vegas, NV, USA, pp. 1-9.Applicant
- “Packet”, Microsoft Computer Dictionary, Microsoft Press, Mar. 2002, 1 pg.Applicant
- Final-Office Action mailed Aug. 29, 2011 in U.S. Appl. No. 11/151,812, filed Jun. 13, 2005.Applicant
- Final-Office Action mailed Aug. 19, 2011 in U.S. Appl. No. 11/471,072, filed Jun. 19, 2006.Applicant
- Advisory Action mailed Jul. 26, 2011; Final-Office Action mailed Aug. 23, 2011; and Notices of Allowance mailed Dec. 15, 2011 and Feb. 7, 2012 in U.S. Appl. No. 11/409,355, filed Apr. 20, 2006.Applicant
- Non-Final Office Action mailed Dec. 20, 2011 in U.S. Appl. No. 11/717,408, filed Mar. 12, 2007.Applicant
- Notice of Allowance mailed Mar. 23, 2012 in U.S. Appl. No. 11/998,750, filed Nov. 30, 2007.Applicant
- Non-Final Office Action mailed Jan. 31, 2012 in U.S. Appl. No. 11/709,570, filed Feb. 21, 2007.Applicant
- Final-Office Action mailed Aug. 26, 2011 and Advisory Action mailed Nov. 9, 2011 in U.S. Appl. No. 12/,263,971, filed Nov. 3, 2008.Applicant