Abstract:
A system is described for scheduling the processing of items of suspicious network content to determine whether these items contain malicious network content. The system features a memory and an analyzer that may comprise a processor-based digital device in which at least one virtual machine (VM) and a scheduler operates. The scheduler is configured to generate an order of processing of a plurality of items of network content by the processor based on a plurality of probability scores, each corresponding to an item of network content. The analyzer is configured to process the items of network content in at least the virtual machine by replaying these items in accordance with the order of processing. The virtual machine is configured with a software profile corresponding to each of the processed items and being adapted to monitor behavior of each of the items during processing, thereby to detect malicious network content.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 12/263,971. U.S. patent application Ser. No. 12/263,971 is related to co-pending U.S. patent application Ser. No. 11/409,355 entitled “Heuristic Based Capture with Replay to Virtual Machine” and filed on Apr. 20, 2006, which is a continuation-in-part of U.S. patent application Ser. No. 11/152,286 entitled “Computer Worm Defense System and Method” and filed on Jun. 13, 2005, which claims the priority benefit of U.S. Provisional Patent Application Ser. No. 60/579,910 entitled “Computer Worm Defense System and Method” and filed on Jun. 14, 2004. U.S. patent application Ser. No. 11/409,355 is also a continuation-in-part of U.S. patent application Ser. No. 11/096,287 entitled “System and Method of Detecting Computer Worms” and filed on Mar. 31, 2005, which claims the priority benefit of U.S. Provisional Patent Application Ser. No. 60/559,198 entitled “System and Method of Detecting Computer Worms” and filed on Apr. 1, 2004. U.S. patent application Ser. No. 11/409,355 is also a continuation-in-part of U.S. patent application Ser. No. 11/151,812 entitled “System and Method of Containing Computer Worms” and filed on Jun. 13, 2005, which claims the priority benefit of U.S. Provisional Patent Application No. 60/579,953 entitled “System and Method of Containing Computer Worms” and filed on Jun. 14, 2004. Each of the aforementioned patent applications are incorporated by reference herein. 
    
    
     BACKGROUND 
     1. Field of the Invention 
     The present invention relates generally to network security and more particularly to detecting malicious network content. 
     2. Related Art 
     Presently, malicious network content (e.g., malicious software or malware) can attack various devices via a communication 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, adware, spyware, or any programming that gathers information about a computer user or otherwise operates without permission. 
     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&#39;s browser. Spyware is a program configured to collect information regarding the user, the computer, and/or a user&#39;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&#39;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&#39;s function. 
     Various processes and devices have been employed to prevent the problems that malicious network content can cause. For example, computers often include antivirus scanning software that scans a particular client device for viruses. Computers may also include spyware and/or adware scanning software. The scanning may be performed manually or 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 or spyware is detected by the scanning software, some damage on the particular computer or loss of privacy may have already occurred. 
     In some instances, malicious network content 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&#39;s legitimate owner. 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. A bot may also be propagated by a malicious web site configured to exploit vulnerable computers that visit its web pages. 
     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). 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, or engage in criminal enterprises. In one example, bot infected computers may be directed to flood another computer on a network with excessive traffic 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. In a third example, bots may host illegal businesses such as pharmaceutical websites that sell pharmaceuticals without a prescription. 
     Malicious network content may be distributed over a network via web sites, e.g., servers operating on a network according to an HTTP standard. Malicious network content distributed in this manner may be actively downloaded and installed on a user&#39;s computer, without the approval or knowledge of the user, simply by accessing the web site hosting the malicious network content. The web site hosting the malicious network content may be referred to as a malicious web site. The malicious network content may be embedded within data associated with web pages hosted by the malicious web site. For example, a web page may include JavaScript code, and malicious network content may be embedded within the JavaScript code. In this example, the malicious network content embedded within the JavaScript code may be obfuscated such that it is not apparent until the JavaScript code is executed that the JavaScript code contains malicious network content. Therefore, the malicious network content may attack or infect a user&#39;s computer before detection by antivirus software, firewalls, intrusion detection systems, or the like. 
     SUMMARY 
     A method for detecting malicious network content comprises inspecting one or more packets of network content, identifying a suspicious characteristic of the network content, determining a score related to a probability that the network content includes malicious network content based on at least the suspicious characteristic, identifying the network content as suspicious if the score satisfies a threshold value, executing a virtual machine to process the suspicious network content, and analyzing a response of the virtual machine to detect malicious network content. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram of an exemplary malicious network content detection environment  100 . 
         FIG. 2  illustrates an exemplary analysis environment. 
         FIG. 3  illustrates an exemplary method for detecting malicious network content. 
         FIG. 4  illustrates another exemplary method for detecting malicious network content. 
         FIG. 5  illustrates an exemplary controller. 
     
    
    
     DESCRIPTION OF EXEMPLARY EMBODIMENTS 
     Network content may include any data transmitted over a network (i.e., network data). Network data may include text, software, images, audio, or other digital data. An example of network content includes web content, or any network data that may be transmitted using a Hypertext Transfer Protocol (HTTP), HyperText Markup Language (HTML) protocol, or be transmitted in a manner suitable for display on a web browser software application. Another examples of network content includes email messages, which may be transmitted using an email protocol such as Simple Mail Transfer Protocol (SMTP), Post Office Protocol version 3 (POP3), or Internet Message Access Protocol (IMAP4). A further example of network content includes Instant Messages, which may be transmitted using an Instant Messaging protocol such as Session Initiation Protocol (SIP) or Extensible Messaging and Presence Protocol (XMPP). In addition, network content may include any network data that is transferred using other data transfer protocols, such as File Transfer Protocol (FTP). We distinguish network content from network protocol header information used for addressing, routing, and otherwise delivering the network content. 
     To detect malicious network content (e.g., malicious web content) being transmitted over a communication network to a computing device, a virtual machine may be used to simulate the receipt and processing of network content on the receiving system. A determination may be made as to whether the network content is malicious based on a response of the virtual machine to the network content. Sometimes, suspicious network content is determined to be non-malicious. Processing the suspicious network content in a virtual machine is an important step to determine whether the suspicious network content is in fact malicious and prevent a false assumption that the suspicious network content is malicious. False positives in detecting malicious network content may be avoided by processing suspicious network content in a virtual machine and detecting malicious network content by analyzing the virtual machine&#39;s response to the suspicious network content. 
     In the prior art, a proxy may be used in the network between the computing device and a web server hosting the malicious network content. The proxy may intercept a request for network content issued by a web browser executing on the computing device. The proxy may then issue the request to the web server as a proxy on behalf of the computing device. The proxy may receive a response to the request from the web server. The proxy may then process a data exchange including the request and response on a virtual machine and evaluate the virtual machine&#39;s response to the data exchange to detect malicious network content. If no malicious network content is detected, the proxy may forward the requested network content to the computing device from which the original request originated. 
     Because each data exchange is processed using a virtual machine, this approach is highly computation intensive, and is not scalable for large numbers of computing devices on a network. Also, because the requested network content is not delivered to the computing device until after it has been determined that the requested network content does not include malicious network content, a significant delay is introduced between the request for network content and the delivery of the requested network content. 
     Provos et al. (N. Provos, P. Mavrommatis, M. A. Rajab, and F. Monrose, “All your iFRAMEs Point to Us,” Google Technical Report provos-2008a, Feb. 4, 2008) reported on an analysis of web malware using a large web repository and corpus of malicious URLs. Provos et al. collected data for the analysis by first using a machine-learning framework in a pre-processing phase to extract features from web pages in the web repository and translate the features into a likelihood score. Next, a virtual machine was used in a verification phase to verify candidates identified by the machine-learning framework. Approximately 0.1% of the web pages in the web repository were processed by the virtual machine in the verification phase. Provos et al. noted that exhaustive inspection of each URL in the repository is prohibitively expensive. The system used by Provos et al. relied on a crawler proceeding gradually through the web to gather data in the repository for inspection, and could not inspect and select web pages in transit in the network for examination in a virtual machine. 
       FIG. 1  is a diagram of an exemplary malicious network content detection environment  100 . The malicious network content detection environment  100  comprises a server device  105 , a client device  110 , and a tap  115 , each coupled to a communication network  120 . In various embodiments, there may be multiple server devices  105  and multiple client devices  110 . The tap  115  is further coupled to a malicious network content detection system  125 . The malicious network content detection system  125  may monitor exchanges of network content (e.g., web content) rather than intercepting and holding the network content until after determining whether the network content includes malicious network content. The malicious network content detection system  125  may be configured to inspect exchanges of network content over the communication network  120 , identify suspicious network content, and analyze the suspicious network content using a virtual machine to detect malicious network content. In this way, the malicious network content detection system  125  may be computationally efficient and scalable as data traffic volume and a number of computing devices communicating over the communication network  120  increase. Therefore, the malicious network content detection system  125  may not become a bottleneck in the malicious network content detection environment  100 . 
     The communication network  120  may include a public computer network such as the Internet, or a private computer network such as a wireless telecommunication network, wide area network, or local area network, or a combination of networks. Though the communication network  120  may include any type of network and be used to communicate different types of data, communications of web data may be discussed below for purposes of example. 
     The server device  105  and the client device  110  may include digital devices. Some examples of digital devices include computers, servers, laptops, personal digital assistants, and cellular telephones. The server device  105  may be configured to transmit network data over the communication network  120  to the client device  110 . The client device  110  may be configured to receive the network data from the server device  105 . The network data may include network content, such as web pages transmitted using a network communications protocol (e.g., Hypertext Transfer Protocol, or HTTP). In various embodiments, the server device  105  may include a web server configured to provide network content. The client device  110  may include a web browser configured to retrieve and/or display network content. 
     The tap  115  may include a digital data tap configured to monitor network data and provide a copy of the network data to the malicious network content detection system  125 . Network data may comprise signals and data that are transmitted over the communication network  120  including data flows from the server device  105  to the client device  110 . In one example, the tap  115  monitors and copies the network data without an appreciable decline in performance of the server device  105 , the client device  110 , or the communication network  120 . The tap  115  may copy any portion of the network data. For example, the tap  115  may receive and copy any number of data packets from the network data. 
     In some embodiments, the network data may be organized into one or more data flows and provided to the malicious network content detection system  125 . In various embodiments, the tap  115  may sample the network data based on a sampling scheme. Data flows may then be reconstructed based on the network data samples. 
     The tap  115  may also capture metadata from the network data. The metadata may be associated with the server device  105  and/or the client device  110 . For example, the metadata may identify the server device  105  and/or the client device  110 . In some embodiments, the server device  105  transmits metadata which is captured by the tap  115 . In other embodiments, a heuristic module  130  (described herein) may determine the server device  105  and the client device  110  by analyzing data packets within the network data in order to generate the metadata. 
     The malicious network content detection system  125  may include a digital device, software, or a combination thereof that receives network data from the tap  115 . The malicious network content detection system  125  includes a heuristic module  130 , a heuristics database  135 , a scheduler  140 , a virtual machine pool  145 , and an analysis environment  150 . In some embodiments, the tap  115  may be contained within the malicious network content detection system  125 . 
     The heuristic module  130  receives the copy of the network data from the tap  115  and applies heuristics to the data to determine if the network data might contain suspicious network content. The heuristics applied by the heuristic module  130  may be based on data and/or rules stored in the heuristics database  135 . In one example, the heuristic module  130  flags network data as suspicious after applying a heuristic analysis. The network data may then be buffered and organized into a data flow. The data flow may then be provided to the scheduler  140 . In some embodiments, the suspicious network data is provided directly to the scheduler  140  without buffering or organizing the data flow. In other embodiments, a notification of a group of data flows (e.g., a set of related web page requests and responses) may be sent to the scheduler  140  for later retrieval by the virtual machine. 
     The heuristic module  130  may perform one or more heuristic analyses on the network data. The heuristic module  130  may retain data packets belonging to a particular data flow previously copied by the tap  115 . In one example, the heuristic module  130  receives data packets from the tap  115  and stores the data packets within a buffer or other memory. Once the heuristic module  130  receives a predetermined number of data packets from a particular data flow, the heuristic module  130  performs the heuristics and/or probability analysis. 
     In some embodiments, the heuristic module  130  performs a heuristic analysis on a set of data packets belonging to a data flow and then stores the data packets within a buffer or other memory. The heuristic module  130  may 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 analysis may be performed upon the combination of buffered and new data packets to determine a likelihood of suspicious network content. 
     In some embodiments, an optional buffer receives the flagged network data from the heuristic module  130 . The buffer may be used to store and organize the flagged network data into one or more data flows before providing the one or more data flows to the scheduler  140 . In various embodiments, the buffer is used to store network data until the network data is provided to the scheduler  140 . In one example, the buffer stores the network data to allow other components of the malicious network content detection system  125  time to complete functions or otherwise clear data congestion. 
     In some embodiments, the heuristic module  130  may maintain copies of network content data of potential interest to virtual machines and provide the network content data on request (e.g., when a web browser later executes inside a virtual machine and requests entities that were transmitted on the network earlier). The length of time that the heuristic module  130  keeps this data in memory may be based on how suspicious the data is, how much workload the system is under, and/or other factors. 
     The scheduler  140  may identify the client device  110  and retrieve a virtual machine associated with the client device  110 . A virtual machine is software that is configured to mimic the performance of a device (e.g., the client device  110 ). The virtual machine may be retrieved from the virtual machine pool  145 . Furthermore, the scheduler  140  may identify a web browser running on the client device  110 , and retrieve a virtual machine associated with the web browser. 
     In some embodiments, the heuristic module  130  transmits the metadata identifying the client device  110  to the scheduler  140 . In other embodiments, the scheduler  140  receives one or more data packets of the network data from the heuristic module  130  and analyzes the one or more data packets to identify the client device  110 . In yet other embodiments, the metadata may be received from the tap  115 . 
     The scheduler  140  may retrieve and configure the virtual machine to mimic the pertinent performance characteristics of the client device  110 . In one example, the scheduler  140  configures the characteristics of the virtual machine to mimic only those features of the client device  110  that are affected by the network data copied by the tap  115 . The scheduler  140  may determine the features of the client device  110  that are affected by the network data by receiving and analyzing the network data from the tap  115 . Such features of the client device  110  may 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 client device  110  that can respond to the network data. In other embodiments, the heuristic module  130  may determine the features of the client device  110  that are affected by the network data by receiving and analyzing the network data from the tap  115 . The heuristic module  130  may then transmit the features of the client device to the scheduler  140 . 
     The virtual machine pool  145  may be configured to store one or more virtual machines. The virtual machine pool  145  may include software and/or a storage medium capable of storing software. In one example, the virtual machine pool  145  stores a single virtual machine that can be configured by the scheduler  140  to mimic the performance of any client device  110  on the communication network  120 . The virtual machine pool  145  may store any number of distinct virtual machines that can be configured to simulate the performance of a wide variety of client devices  110 . 
     The analysis environment  150  simulates the receipt and/or display of the network content from the server device  105  after the network content is received by the client device  110  to analyze the effects of the network content upon the client device  110 . The analysis environment  150  may identify the effects of malware or malicious network content by analyzing the simulation of the effects of the network content upon the client device  110  that is carried out on the virtual machine. There may be multiple analysis environments  150  to simulate multiple streams of network content. The analysis environment  150  is further discussed with respect to  FIG. 2 . 
     Although  FIG. 1  depicts data transmitted from the server device  105  to the client device  110 , either device can transmit and receive data from the other. Similarly, although only two devices are depicted, any number of devices can send and/or receive data across the communication network  120 . Moreover, the tap  115  can monitor and copy data transmitted from multiple devices without appreciably effecting the performance of the communication network  120  or the devices coupled to the communication network  120 . 
       FIG. 2  illustrates an exemplary analysis environment. The analysis environment  150  includes a replayer  205 , a virtual switch  210 , and a virtual machine  215 . The replayer  205  receives network content that has been flagged by the heuristic module  130  and provides the network content to the virtual machine  215  via the virtual switch  210  (i.e., replays the network content) in the analysis environment  150 . In some embodiments, the replayer  205  mimics the behavior of the server device  105  in transmitting the flagged network content. There may be any number of replayers  205  simulating the transmission of network content between the server device  105  and the client device  110 . In a further embodiment, the replayer  205  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. 
     The virtual switch  210  may include software that is capable of forwarding packets of flagged network content to the virtual machine  215 . In one example, the replayer  205  simulates the transmission of the data flow by the server device  105 . The virtual switch  210  simulates the communication network  120 , and the virtual machine  215  simulates the client device  110 . The virtual switch  210  may route the data packets of the data flow to the correct ports of the virtual machine  215 . 
     In some embodiments, requests for data from client software in the virtual machine  215  (e.g., a web browser) may be proxied by the replayer to the heuristic module  130  where the data has been cached, and a response from the heuristic module  130  may then be proxied back to the client software executing in the virtual machine  215 . 
     The virtual machine  215  includes a representation of the client device  110  that may be provided to the analysis environment  150  by the scheduler  140 . In one example, the scheduler  140  retrieves an instance of the virtual machine  215  from the virtual machine pool  145  and configures the virtual machine  215  to mimic a client device  110 . The configured virtual machine  215  is then provided to the analysis environment  150  where it may receive flagged network content from the virtual switch  210 . 
     As the analysis environment  150  simulates the transmission and reception of the network content, behavior of the virtual machine  215  can be closely monitored for unauthorized activity. If the virtual machine  215  crashes, performs illegal operations, performs abnormally, or allows access of data to an unauthorized entity (e.g., an unauthorized computer user, a bot, etc.), the analysis environment  150  may react. In one example, the analysis environment  150  may transmit a command to the client device  110  to stop accepting the network content or data flows from the server device  105 . 
     In some embodiments, the analysis environment  150  monitors and analyzes the behavior of the virtual machine  215  in order to determine a specific type of malware or malicious network content. The analysis environment  150  may also generate computer code configured to eliminate new viruses, worms, bots, adware, spyware, or other malware or malicious network content. In various embodiments, the analysis environment  150  generates computer code configured to repair damage performed by malware or malicious network content. By simulating the transmission and reception of suspicious network content and analyzing the response of the virtual machine  215 , the analysis environment  150  may identify known and previously unidentified malware and malicious network content before a computer system is damaged or compromised. 
       FIG. 3  illustrates an exemplary method  300  for detecting malicious network content. In step  305 , a packet of network content is intercepted or copied. The packet may be intercepted and/or copied from a network data transmission between the server device  105  and an intended destination (e.g., the client device  110 ), such as by the tap  115 . Alternatively, the packet may be intercepted and/or copied from a network data transmission between the client device  110  and an intended destination (e.g., the server device  105 ). The packet may include a request for data, such as network content, or data provided in response to a request. 
     In step  310 , a packet of network content is inspected. The heuristic module  130  may utilize one or more heuristics to inspect the packet of network content for suspicious network content which indicates the potential presence of malicious network content or malware within the packet. 
     A packet of network content may be part of a data flow which includes additional packets of network content. For example, the packet of network content may represent a portion of a web page, while other related packets in the data flow represent additional portions of the web page. The packet of network content may be stored along with the other related packets of network content comprising the data flow, such that multiple packets of network content within the data flow may be inspected in a sequence or in parallel. The malicious network content detection system may store the packets of network content and all or a portion of a data flow. The data flow and data packets may be stored for any length of time, from a few seconds to minutes, tens of minutes, or more, for analysis at any time. 
     To facilitate longer storage times for data flows over a high data rate communication network, large data objects comprised of numerous data packets may be truncated to a small subset of representative data packets. Data object truncation is particularly useful where network communication bandwidth is mostly utilized by a small percentage of large data objects, such as video. For example, video data may be truncated to a few data packets, such as the first few data packets. An extent to which the large data objects are truncated may be adaptive based on available memory, data bandwidth, type of data objects, and other factors. An amount of memory allocated to storing a data flow may also be dependent upon a characteristic of the data flow, such as data type. In an example, octet streams, text streams, HTML streams, and miscellaneous binary streams may be allocated 1 megabyte (MB). Images and PDF files may be allocated 384 kilobytes (kB). Video, audio, and most other data types may be allocated 128 kB. The memory allocated to storing each data flow type may be adjusted, periodically or dynamically, to improve analysis throughput while maintaining accuracy in detection of malicious network content and working within memory limitations. 
     In step  315 , a suspicious characteristic of the network content is identified. The heuristic module  130  may identify the suspicious characteristic of the network content as a result of inspecting the network content in step  310 . When a characteristic of the packet, such as a sequence of characters or keyword, is identified that meets the conditions of a heuristic used in step  310 , a suspicious characteristic or “feature” of the network content is identified. The identified features may be stored for reference and analysis. In some embodiments, the entire packet may be inspected and multiple features may be identified before proceeding to the next step. In some embodiments, features may be determined as a result of an analysis across multiple packets comprising the network content. 
     Keywords used by heuristics may be chosen by performing an approximate Bayesian probability analysis of all the keywords in an HTML specification using a corpus of malicious network content and a corpus of non-malicious network content. The approximate Bayesian probability analysis may be based on the principles of the Bayesian theorem and/or naïve Bayesian classification. For instance, a probability P m  that the keyword appears in malicious network content may be computed using the corpus of malicious network content, while a probability P n  that the keyword appears in non-malicious network content may be computed using the corpus of non-malicious network content. A given keyword may be determined to be a suspicious characteristic for being associated with malicious network content if a score based on a computed ratio P m /P n  exceeds a threshold of suspicion. The threshold of suspicion may be a value greater than 1, 10, 30, 60, 100, or some other number indicating how much more likely the suspicious characteristic is to indicate malicious network content than to indicate non-malicious network content. 
     In step  320 , a score related to a probability that the suspicious characteristic identified in step  315  indicates malicious network content is determined. An approximate Bayesian probability analysis may be used to determine the score. In various embodiments, the approximate Bayesian probability analysis may be performed in real-time or using a look-up table based on a previously performed approximate Bayesian probability analysis. 
     For example, the approximate Bayesian probability analysis may be performed to determine a relative probability score that a particular feature is associated with the presence of malicious network content in a packet by comparing a corpus of malicious network content and a corpus of regular, non-malicious network content. A feature may include a characteristic of the packet, such as a sequence of characters or keyword, that meets the conditions of a heuristic used in step  310 . The feature may also include a characteristic involving more than one packet inspected in sequence or in parallel. An example of a feature may include the character sequence “eval(unescape(”, which indicates a JavaScript “unescape” command nested within a JavaScript “eval” command argument. Further examples of features are described below with respect to step  445  in method  400 . A probability P flm  that the feature is present in a packet of malicious network content is computed by analyzing the corpus of malicious network content. A probability P flm  that the feature is present in a packet of non-malicious network content is computed by analyzing the corpus of non-malicious network content. A malicious probability score is computed as the base two logarithm of a relative probability factor P mlf  that the feature is associated with malicious network content. The malicious probability score is computed by computing the ratio of the base two logarithm (log 2 ) of the probability that the feature is present in a packet of malicious network content and the base two logarithm of the probability that the feature is present in a packet of non-malicious network content. The relative probability factor P mlf  may be expressed as follows:
 
log 2 ( P   mlf )=log 2 ( P   flm )/log 2 ( P   fln )  Equation 1
 
     The size of the result log 2 (P mlf )(i.e., malicious probability score) may indicate the probability that the suspicious network content includes malicious network content. For example, a result of eleven may indicate that the feature is approximately two thousand times more likely to appear in malicious network content than in non-malicious network content. Likewise, a value of twelve may indicate that the feature is approximately four thousand times more likely to appear in malicious network content. 
     In some embodiments, the malicious corpus and/or the non-malicious corpus may be continuously updated in response to monitored network data traffic, and the malicious probability scores associated with the features may be continuously updated in response to the updates to the corpuses. In other embodiments, the corpuses may be created and used in advance to store pre-computed malicious probability scores in a look-up table for reference when features are identified. The features associated with significant probabilities of malicious network content may change as the corpuses change. 
     In step  325 , malicious network content is identified or flagged if the malicious probability score of a feature computed in step  320  satisfies an analysis threshold. The analysis threshold may be greater than 1, 10, 30, 60, 100, 1000, 2000, or higher. The analysis threshold may be preset, or may be variable based on operating conditions of the malicious network content detection system  125 . If the malicious probability score does not satisfy the analysis threshold, no action may be taken with regard to the feature associated with the malicious probability score. Otherwise, the analysis may proceed to the next step, such as step  330  for analysis through processing by a virtual machine, such as the virtual machine  215 . In some embodiments, the malicious probability scores of all features computed in step  320  may be compared against the analysis threshold to assign a priority level to each feature and/or the packet as a whole. The priority level may be computed based on a variety of factors, such as the number of features identified in the packet, the highest malicious probability score of a feature in the packet, an average malicious probability score of the features in the packet, a mean malicious probability score of the features in the packet, and the like. 
     The analysis threshold may be adaptive or be frequently updated based on operating conditions of the malicious network content detection system  125 . For example, the threshold value may be dynamically revised according to a quantity of packets of network content to be inspected. As a quantity of data packets which are intercepted and/or copied from the network data transmission in step  310  increases, a quantity of data packets to be inspected may also increase. This may increase a computational load and leave less computational bandwidth available for more detailed analysis of the data packets. Consequently, the threshold may be increased to compensate for the decrease in available computational bandwidth for more detailed analysis. As another example, the threshold value may be dynamically revised according to an availability of one or more virtual machines to be used for the more detailed analysis. The threshold value may be set such that only features which have a significant probability of indicating malicious network content are processed using a virtual machine. For example, out of over one thousand features, less than fifty may be considered significant. 
     There may be multiple dynamically adaptive thresholds, which may be synchronized with each other. For example, the scheduler  140  may use a threshold to determine whether a virtual machine should be dispatched to process a queued suspicious network content. The scheduler  140 &#39;s threshold may increase due to lack of available computational resources for the analysis environment  150  to execute virtual machines. The heuristic module  130  may use another threshold to determine whether heuristics should be applied to an identified feature. The heuristic module  130 &#39;s threshold may be based on the malicious probability score for the identified feature. As the scheduler  140 &#39;s threshold increases, the heuristic module  130 &#39;s threshold may also increase. This is because flagging suspicious network content based on running heuristics on identified features may be irrelevant and an inefficient use of computational resources if the scheduler  140  will not process the suspicious network content in a virtual machine due to an increased threshold in the scheduler  140 . 
     After suspicious network content has been flagged at step  325  for further analysis, the entire stored data flow including the suspicious network content may be reanalyzed. Each feature may be given a higher malicious probability score by virtue that one feature in the data flow has been found to have a malicious probability score greater than the threshold. A priority level for each feature found in the data flow may also be increased. Furthermore, all data packets and data flows associated with any domains associated with suspicious network content may be cached and given higher priorities and malicious probability scores than they would otherwise. The scheduler  140  may execute the virtual machine to process each flagged suspicious network content in the data flow individually, in priority order, in their original sequence of presentation, or in some other order. The virtual machine may process the suspicious network content until pre-empted by a higher priority suspicious network content. 
     In step  330 , a virtual machine is executed to process the suspicious network content. The virtual machine may effectively replay the suspicious network content in a web browser executing on the virtual machine. The heuristic module  130  may provide the packet containing the suspicious network content to the scheduler  140 , along with a list of the features present in the packet and the malicious probability scores associated with each of those features. Alternatively, the heuristic module  130  may provide a pointer to the packet containing the suspicious network content to the scheduler  140  such that the scheduler  140  may access the packet via a memory shared with the heuristic module  130 . In another embodiment, the heuristic module  130  may provide identification information regarding the packet to the scheduler  140  such that the scheduler  140 , replayer  205 , or virtual machine may query the heuristic module  130  for data regarding the packet as needed. 
     The heuristic module  130  may also provide a priority level for the packet and/or the features present in the packet. The scheduler  140  may then load and configure a virtual machine from the virtual machine pool  145 , and dispatch the virtual machine to the analysis environment  150  to process the suspicious network content. The virtual machine may be configured to execute for a minimum amount of processing, or for a minimum period of time, such as approximately 45 seconds. After the minimum period of time passes, the virtual machine may be pre-empted by the scheduler  140  to dispatch another virtual machine. Multiple virtual machines may be run simultaneously. 
     The scheduler  140  may choose which feature to process first according to the priority levels provided by the heuristic module  130 . The scheduler  140  may cause another virtual machine already processing or analyzing another feature or packet, or set of packets, in the analysis environment  150  to terminate prior to dispatching the loaded virtual machine. For example, this may occur if computational resources are occupied with other virtual machines processing other features and therefore are not available to execute the loaded virtual machine. The scheduler  140  may choose which virtual machine(s) to terminate based on the priority levels of the features being processed by the virtual machine, how much time the virtual machine has already spent executing, or other reasons. 
     The scheduler  140  may reprioritize suspicious network content already in queue to be processed by virtual machines based on newly identified suspicious network content. For example, already queued suspicious network content may be reprioritized if there is a domain identified in common with the newly identified suspicious network content. Numerous incidents of suspicious network content associated with a single domain may increase the priority of all suspicious network content associated with the domain. 
     The replayer  205  in the analysis environment  150  may keep track of network content requested by the virtual machine. If suspicious network content already in the scheduler  140 &#39;s queue is requested and processed by the virtual machine while processing other previously dispatched suspicious network content, and the queued suspicious network content is not found to be malicious, then the scheduler  140  may delete the queued suspicious network content from the queue. In this way, computational requirements can be reduced because an item of suspicious network content may only be processed in a virtual machine once, rather than each time a reference to the item of suspicious network content is made by another item of suspicious network content. 
     In step  335 , malicious network content is detected by analyzing the virtual machine response to the suspicious network content. The analysis environment  150  may be configured to monitor the virtual machine for indications that the suspicious network content is in fact malicious network content. The analysis environment  150  may monitor the virtual machine for unusual memory accesses, unusual spawning of executable processes, unusual network transmissions, crashes, unusual changes in performance, and the like. The analysis environment may flag the suspicious network content as malicious network content according to the observed behavior of the virtual machine. 
     If a virtual machine processes suspicious network content for greater than a predetermined amount of time without any malicious network content being detected, the scheduler  140  may terminate the virtual machine to free up computational resources. The predetermined amount of time may be variable, according to a queue of suspicious network content that is awaiting processing by a virtual machine, the probability that the suspicious network content may be malicious network content, the feature being evaluated by the virtual machine, available computational resources, and the like. For example, the predetermined amount of time may be 45 seconds, two minutes, twenty minutes, or any other length of time. 
     If the suspicious network content is determined to be malicious network content, the malicious network content detection system  125  may report the malicious network content and/or log the malicious network content for future reference. For example, the malicious network content detection system  125  may generate an alert for a network content packet detected to include malicious network content. The malicious network content detection system  125  may report the malicious network content to an entity responsible for the client device  105 . If the malicious network content was determined to originate from the server device  105 , the client device  110  may be instructed not to continue network transmissions with the server device  105 . If a party responsible for the server device  105  is known, the malicious network content detection system  125  may report the malicious network content to the party responsible for the server device  105 . The server device  105  may be added to a list of malicious network content providers, and future network transmissions originating from the server device  105  may be blocked from reaching their intended destinations. 
       FIG. 4  illustrates another exemplary method  400  for detecting malicious network content. The method  400  may be performed by the heuristic module  130 . In the method  400 , a packet of network content is inspected to identify features which may indicate the presence of malicious network content. The method  400  may include the use of a single pass parser and/or an augmented finite state machine, which may maintain a stack of states. The method  400  may begin processing a data packet starting with a character after a character sequence “HTTP” has been identified. 
     In step  405 , a data character is read from the data packet. The data character read may be subsequent to the character sequence “HTTP” or a data character previously read in a prior iteration of step  405 . A pointer may be incremented to indicate the next data character to read in the method  400 . 
     In step  410 , the data character read in step  405  is evaluated to determine if the data character may indicate the start of a possible keyword or a possible feature as described with respect to method  300 , or a different kind of data (e.g., JavaScript content embedded in HTML content). The data character may include a left angled bracket (i.e., “&lt;”), for example. If the data character read may indicate the start of a keyword or a feature, the method may proceed to step  415 . Otherwise, the method may proceed to step  420 . 
     In step  415 , a new state is pushed onto the stack of states to indicate that the method  400  has encountered the start of a keyword or feature. The new state may be an InKeyword state to indicate that the method is in the midst of processing a keyword. Depending on the character read, a different new state may be pushed onto the stack. A string of data characters may be stored, starting with the most recent character read or the next character to be read. The method  400  then proceeds to step  440 . 
     In step  420 , the data character read in step  405  is evaluated to determine if the data character may indicate the end of a keyword or a feature as described with respect to method  300 . The data character may include a right angled bracket (i.e., “&gt;”), for example. If the data character read may indicate the end of a keyword or a feature, the method may proceed to step  425 . Otherwise, the method may proceed to step  440 . 
     In step  425 , heuristics to be applied to the data packet are identified and applied based on a character string read, which may start with the data character identified in step  410  and end with the data character identified in step  420 . The heuristic module  300  may store the character string. The character string may be compared against a database of character strings stored in the heuristics database  135  to determine one or more heuristics that may be applied to the data packet based on the keyword. In some embodiments, a list of results of applying heuristics may be created. The list of results may be stored so that the list may be referenced in step  445 . 
     Some examples of a heuristic that may be applied to the packet include keyword matches. Some keywords may be associated more with malicious network content than non-malicious network content, and their presence in a packet of network content may be an indication that the packet contains suspicious network content. 
     In one exemplary heuristic, an object filename&#39;s extension following a period may be examined. For example, a filename ending in the characters “.ini”, “.anr”, or “.htm” may be determined to be suspicious. Also, a filename generally associated with one file type but associated with a different file type in the reference may be determined to be suspicious. For example, a filename ending in “.jpg” which is not referring to an image file may be determined to be suspicious. 
     In other exemplary heuristics, content of web pages may be analyzed to determine whether network content is suspicious. For example, presence of small iframes, such as an iframe in which the width and/or height is 0 or 1 pixel, in a web page may be determined to be suspicious. 
     Further examples of heuristics may be associated with JavaScript code sequences. When an “eval(unescape( . . . ))” JavaScript command sequence, which includes an “unescape” command nested within the argument of an “eval” command, is detected in the data packet, the heuristic may evaluate the command sequence to identify suspicious network content. The “eval(unescape( . . . ))” command sequence may be used to obfuscate malicious network content so that the malicious network content is not easily detected in the network data transmission, and may therefore indicate suspicious network content. 
     Another example of a heuristic is a length of the argument of the “unescape” or other JavaScript function from a starting character to an ending character. The length may be determined by counting a number of characters, or measuring a length of time, between the opening parenthesis and the closing parenthesis after “unescape” or other function name. A greater number of characters between the parentheses may indicate that an obfuscated body to the command is being used. 
     Bi-gram detection is another exemplary heuristic that may be employed in JavaScript or other types of network content. In bi-gram detection, character transitions within the network content are analyzed. A table of conditional probabilities may be generated and updated continuously as data is evaluated. The table of conditional probabilities indicates the probability of each second character appearing after each first character. The conditional probability of a second character C 2  given the first character C 1  may be written as P(C 2 |C 1 ). The heuristic may identify when a string of unusual character transitions occurs according to the table of conditional probabilities. Thresholds for the length of the string of unusual character transitions, combined with the values of the conditional probabilities that flags the character transitions as being unusual, may be set a priori based on an approximate Bayesian probability analysis using a corpus of malicious network content and a corpus of non-malicious network content. Alternatively, the thresholds may be adjusted in near real time as the table of conditional probabilities is updated. For example, a long string of unusual character transitions may indicate the presence of malicious network content in a JavaScript “eval(unescape( . . . ))” clause. 
     The use of domain profiles is another exemplary heuristic that may be used to reduce a rate of false positives from other heuristics. The domain profiles heuristic may be used in conjunction with other heuristics in order to increase throughput and reduce computational requirements for detecting malicious network content. Each network domain with which monitored network content is exchanged may be cataloged and annotated with a list of the features present in network content associated with the network domain. A typical network domain may be approximately constant in the features present in associated network content. When a feature is identified by another heuristic, the feature may be looked up in the list of features associated with the network domain. If the feature is listed as being associated with the network domain, and malicious network content was not previously detected due to identification of the feature in network content associated with the domain, a virtual machine may not be executed to process the network content containing the feature associated with the network domain. If, on the other hand, the feature was not previously detected or associated with the network domain, the network content may be identified as being suspicious and processed by a virtual machine. 
     A list of domains or web sites containing malicious network content may be maintained. The list of sources of malicious network content may be hosted on the computer network and accessible by clients on the computer network. The heuristic module  130  may access the list of domains and web sites containing malicious network content to supplement the information provided by the domain profiles heuristic. For example, the threshold for network content associated with a web site on a list of malicious network content sources may be set to be lower and/or the priority of a suspicious network content may be set higher than for other network content. When malicious network content is detected, the list of domains may be notified or updated with the information for reference by others. 
     In step  430 , if a state is being exited, the state being exited is popped from the stack of states. The state being exited is the most recent state pushed onto the stack of states. For example, if the state being exited is the InKeyword state, the InKeyword state is popped from the stack of states to indicate that the method is no longer in the midst of reading a keyword. If a state is not being exited, a state may not be popped from the stack, and multiple states may be stored on the stack. In some embodiments, up to 32 states may be present on the stack of states at one time. For example, JavaScript may have embedded HTML, and therefore multiple states may be active at one time to account for nested features. In various embodiments, there may be more than 60 states associated with data packets being analyzed for malicious network content. 
     In step  435 , a new state is pushed onto the stack of states to indicate that the method is now in the midst of a new state. The new state may be determined by the last keyword that was read, or a character indicating a new kind of content. For example, the new state may be an InBetweenKeyword state to indicate that the method is awaiting another keyword to process. In some embodiments, the new state may be an InJavaScript state to indicate that the method is in the midst of reading a JavaScript segment. The state may impact which heuristics are identified and applied to the packet of web data in step  445 . For example, a first heuristic may be chosen if a first state is active, whereas a second heuristic may be chosen if a second state is active. 
     In step  440 , the count of characters read in step  405  is evaluated to determine if the data character may lie at the end of a packet. If the data character lies at the end of the packet, the method may proceed to step  445 . Otherwise, the method may proceed to step  405 . 
     In step  445 , the list of results produced by applying the heuristics in step  425  for the features in the data packet are referenced to determine which features in the data packet are to be processed using a virtual machine. Malicious probability scores for each feature may be compared against a threshold to determine whether the feature indicates suspicious network content. The features associated with the data packet may be ranked in priority order. The features may be used to prioritize whether to refer the data packet, and associated content, to a virtual machine in the order identified in step  425 , in the priority order determined by their respective malicious probability scores, or in some other order. 
       FIG. 5  illustrates an exemplary controller  500 . The controller  500  may comprise the malicious network content detection system  125  according to some embodiments. The controller  500  comprises at least a processor  505 , a memory system  510 , and a storage system  515 , which are all coupled to a bus  520 . The controller  500  may also comprise a communication network interface  525 , an input/output (I/O) interface  530 , and a display interface  535 . The communication network interface  525  may couple with the communication network  120  via a communication medium  540 . In some embodiments, the controller  500  may couple to a tap, such as the tap  115 , which in turn couples with the communication network  120 . The bus  520  provides communications between the communications network interface  525 , the processor  505 , the memory system  510 , the storage system  515 , the I/O interface  530 , and the display interface  535 . 
     The communications network interface  525  may communicate with other digital devices (not shown) via the communications medium  540 . The processor  505  executes instructions. The memory system  510  permanently or temporarily stores data. Some examples of the memory system  510  are RAM and ROM. The storage system  515  also permanently or temporarily stores data. Some examples of the storage system  515  are hard disks and disk drives. The I/O interface  530  may include any device that can receive input and provide output to a user. The I/O interface  530  may include, but is not limited to, a keyboard, a mouse, a touchscreen, a keypad, a biosensor, a compact disc (CD) drive, a digital versatile disc (DVD) drive, or a floppy disk drive. The display interface  535  may include an interface configured to support a display, monitor, or screen. In some embodiments, the controller  500  comprises a graphical user interface to be displayed to a user over a monitor in order to allow the user to control the controller  500 . 
     The embodiments discussed herein are illustrative. As these embodiments are described with reference to illustrations, various modifications or adaptations of the methods and/or specific structures described may become apparent to those skilled in the art. 
     The above-described modules may be comprised of instructions that are stored on storage media (e.g., computer readable media). The instructions may 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. 
     In the foregoing specification, the invention is described with reference to specific embodiments thereof, but those skilled in the art will recognize that the invention is not limited thereto. Various features and aspects of the above-described invention can be used individually or jointly. Further, the invention can be utilized in any number of environments and applications beyond those described herein without departing from the broader spirit and scope of the specification. The specification and drawings are, accordingly, to be regarded as illustrative rather than restrictive. It will be recognized that the terms “comprising,” “including,” and “having,” as used herein, are specifically intended to be read as open-ended terms of art.