Dynamic and adaptive traffic scanning

Abstract

Systems and methods are provided that enable probabilistic application of data traffic scanning in an effort to catch malicious software or code being carried by the data traffic. The methodology and systems operate by monitoring data traffic in an data network via an interface with the data network, calculating a first conditional probability that content in first given data traffic being monitored is malicious, calculating a second conditional probability that content in second given data traffic being monitored is malicious, ranking the first and second conditional probabilities resulting in ranked conditional probabilities, and performing at least one of anti-virus (AV) or anti-malware (AM) scanning of the content of the first or second given data traffic depending on whose conditional probability is ranked higher in the ranked conditional probabilities.

Description

TECHNICAL FIELD

The present disclosure relates to electronic data network security.

BACKGROUND

Computer systems, networks and data centers are exposed to a constant and differing variety of attacks that expose vulnerabilities of such systems in order to compromise their security and/or operation. As an example, various forms of malicious software program attacks include viruses, worms, Trojan horses and the like that computer systems can obtain over a network such as the Internet. Quite often, users of such computer systems are not even aware that such malicious programs have been obtained within the computer system. Once resident within a given computer, a malicious program that executes might disrupt operation of that computer to a point of inoperability and/or might spread itself to other computers within a network or data center by exploiting vulnerabilities of the computer's operating system or resident application programs. Other malicious programs, such as “Spyware,” might operate within a computer to secretly extract and transmit information within the computer to remote computer systems for various suspect purposes.

To combat the proliferation of malicious software program attacks, Anti-Malware (AM) and Anti-Virus (AV) scanners have been widely deployed in different security appliances and Intrusion Prevention Systems (IPSs) to detect known malicious software, and to thereafter block or filter such threats. Compared with, e.g., universal resource locator (URL) and reputation-based malware filtering, which rely broadly on the source of content by monitoring, e.g., an IP address or a domain name, AM/AV scanning may enjoy a lower false positive rate by employing, e.g., well-defined signatures on the actual content that is responsible for malicious behavior. On the other hand, AM/AV scanning can be expensive in terms of central processing unit (CPU) and memory usage which, in many instances, especially as electronic data network traffic grows at increasing rates, makes it impractical to apply AM/AV scanning to all of the content being carried by network traffic, or even destined for a single given computer within that network.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example architecture including the deployment of AM/AV scanning application logic and AM/AV scanners.

FIG. 2 depicts an example host device on which AM/AV scanning application logic may be deployed.

FIG. 3 depicts a schematic diagram of the locations at which AM/AV scanning application logic can be deployed.

FIG. 4 is a flow chart depicting example operations for placing electronic data traffic in a ranked order according to computed conditional probabilities.

FIG. 5 illustrates an example rank list based on final conditional probabilities calculated for multiple segments or portions of electronic data traffic

FIG. 6 is a flow chart depicting example operations for selecting a second or subsequent scanner to scan electronic data traffic.

FIG. 7 is a flow chart depicting example operations for updating instances of AM/AV scanning application logic based on randomly sampled and scanned electronic data traffic.

FIG. 8 is flow chart depicting example operations for implementing a probabilistic methodology for AM/AV scanning.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Overview

Systems and methods are described that enable probabilistic application of data traffic scanning in an effort to catch malicious software or code being carried by the data traffic. The methodology and systems operate by monitoring data traffic in a data network via an interface with the electronic data network, calculating a first conditional probability that content in first given data traffic being monitored is malicious, calculating a second conditional probability that content in second given data traffic being monitored is malicious, ranking the first and second conditional probabilities resulting in ranked conditional probabilities, and performing at least one of anti-virus (AV) or anti-malware (AM) scanning of the content of the first or second given data traffic depending on whose conditional probability is ranked higher in the ranked conditional probabilities. In one embodiment, the functionality for computing or determining the conditional probabilities is embodied in computer executable instructions or code that is hosted by a network device or appliance, which can be deployed in one of several different locations in a network topology.

Example Embodiments

Embodiments described herein include a computer or other processing system configured to execute a probabilistic application of Anti-Malware/Anti-Virus (AM/AV) scanning. The number of security threats introduced by electronic network (e.g., world wide web) traffic is reaching epidemic proportions. Traditional gateway defenses are proving inadequate against a variety of malware, leaving corporate electronic data networks exposed. The speed, variety and damage potential of malware attacks highlight the utility of a robust, secure platform to protect the enterprise network perimeter or, at the very least, individual end point or end user devices within that perimeter.

FIG. 1 illustrates an example computer networking environment 100 suitable for use in explaining example embodiments disclosed herein. The computer networking environment 100 includes a computer network 101 such as a local area network (LAN) that interconnects multiple end point devices 105(1), 105(2), 105(3), etc. End point devices 105 may be wired or wirelessly connected desktop computers or laptop computers, mobile devices including mobile telephones and tablets, or like devices that provide some sort of user experience to an end user as a result of receiving data over the network.

Network 101 is also connected to an edge router 110 that couples network 101 to a wide area network (WAN) 115 such as the Internet and that allows communication between the end point devices 105 and other computers, servers and other devices worldwide. Such other computers, servers, etc. (not shown) may be disposed, for example, inside a data center 120 that may be a physical data center or a virtual data center that functions, from the perspective of an end point device 105, as a dedicated physical data center.

Also shown in FIG. 1 and interconnected by network 101 are AM/AV scanners 125(1), 125(2), the function of which will be explained in more detail later herein. A host 130 is also shown connected to network 101. Host 130 could be a stand alone computer or other network device, or could be any one of the network connected devices 105, 110, 120, 125 among others. Host 130 hosts, stores or otherwise incorporates AM/AV scanning application logic 200 in the form of, e.g., computer executable instructions or code. As will be explained in detail below, AM/AV scanning application logic 200 is configured to select which electronic data traffic traversing network paths within computer architecture 100 is to be scanned for viruses/malware. Logic 200 may be further configured to select which one of a plurality of AM/AV scanners is to be employed as well as an order by which selected AM/AV scanners may be applied to given electronic data traffic.

In addition, yet another device shown in computer architecture 100 is an administration device 140, which hosts AM/AV scanning administration logic 250. Administration device 140 may be a stand alone computer or device as shown or may be incorporated into any one of the network connected devices shown in FIG. 1. As will be explained below, AM/AV scanning administration logic 250 may be used to control or configure the operation of AM/AV scanning application logic 200 to change the way in which electronic data traffic traversing networks paths within computer architecture 100 may be processed by AM/AV scanners 125.

FIG. 2 depicts an example host 130 on which AM/AV scanning application logic 200 may be deployed. Host 130 may comprise a processor 210, associated memory 220 (a tangible medium), which may include AM/AV scanning application logic 200, and a network interface unit 240 such as a network interface card, which enables host 130 to communicate externally with other devices via network 101. Although not shown, host 130 may also include input/output devices such as a keyboard, mouse and display (not shown) to enable direct control of host 130 by a user. Those skilled in the art will appreciate that host 130 may be a rack mounted device, such as a blade in a server rack, and that may not have dedicated respective input/output devices. Instead, such a rack mounted device might be accessible via a centralized console, or some other (even remote) arrangement by which host 130 can be accessed, controlled or configured by, e.g., a user, perhaps using AM/AV administration logic 250. It is noted that administration device 140 may be configured similarly to host 130 and store in memory thereof AM/AV administration logic 250.

FIG. 3 depicts a schematic diagram of several locations at which instances of AM/AV scanning application logic 200 can be deployed. As computer architecture continues to mature, there may be advantages in terms of cost, efficiency, processing capacity, among other considerations, to host AM/AV scanning application logic 200 in different locations. For example, AM/AV scanning application logic 200 may be deployed, in accordance with one possible embodiment, within what is referred to as the “cloud” or, in this case, network security cloud 310. This cloud may physically map, for example, to data center 120 shown in FIG. 1. AM/AV scanning application logic 200 may also be hosted by a network security appliance 315 that is logically located between an end point device 305 (like 105 in FIG. 1) and a firewall 320. Network security appliance 315 may be configured as an intrusion detection system, web security appliance, or other like computing component. In still another embodiment, AM/AV scanning application logic 200 may be deployed on what may be referred to as a “next-generation” firewall 325. Such a component might perform the functions of both network security appliance 315 and firewall 320. Finally, in yet another embodiment, AM/AV scanning application logic 200 may be deployed directly on end point devices 305 (105). Again, precisely where in a given computer network architecture AM/AV scanning application logic 200 is deployed may be a function of one or more factors.

In the same way, although AM/AV scanners 125 shown in FIG. 1 are depicted as stand alone components, the functionality of AM/AV scanners 125 may be incorporated into any of the components depicted in FIG. 3. That is, AM/AV scanners 125 could be incorporated into network security cloud 310, next-generation firewall 325, network security appliance 315 or end point device 305. Thus, those skilled in the art will appreciate that AV/AM scanning application logic 200 and AM/AV scanners 125 may be hosted by a same physical component or be hosted by different network elements within network architecture 100. Likewise, AM/AV scanning administration logic 200 could also be hosted separately as shown in FIG. 1 or be hosted on any of the components shown in FIG. 3.

The functionality of AM/AV scanning application logic 200 will now be described. At a high level, a main function of AM/AV scanning application logic 200 is to identify, based on probabilistic methodologies, which electronic data traffic is to be scanned by a given one of the AM/AV scanners 125, and where appropriate, to determine, in a probabilistic manner, which additional AM/AV scanner or scanners 125 may also be applied to the electronic data traffic after a given first one of the AM/AV scanners 125 has completed its scan operations.

A typical AM/AV scanner can accommodate approximately 100 scanning requests per second, i.e., a request to scan content carried by given electronic data traffic. In today's network environment, however, in order to effectively scan substantially all electronic data traffic, a scanner would have to support potentially thousands of requests per second. To address this problem, some scanning implementations scan only higher risk but lower volume file types, such as executable (.exe.) and zipped (.zip) file types. However, the trend is that malware and viruses are increasingly embedded in many other file types including hyper text markup language (HTML) files, JavaScript files, portable document format (PDF) documents and other image format files

To address the forgoing, AM/AV scanning application logic 200 provides a dynamic electronic data traffic scanning approach that determines which high-risk traffic to scan based on a probabilistic prioritization, selects which AM/AV scanner(s) 125 to user if there are two or more AM/AV scanners available for use, and monitors overall system load to dynamically scan more content when a given one or more of AM/AV scanners 125 may be idle. AM/AV scanner application logic 200 may also periodically cause AM/AV scanners 125 to scan random electronic data traffic, determine whether that traffic includes malicious software programs, and then based on results of the determination, provide a feedback mechanism by which future probabilistic prioritization techniques can be tuned or optimized.

More specifically, probabilistic prioritization, in accordance with principles of embodiments described herein, is based on conditional probability. In the case of identifying malicious software in electronic data traffic, the conditional probability of an event B (i.e., existence of malicious software programs) is the probability that the event will occur given the knowledge that an event A (i.e., selected characteristics of the electronic data traffic are present) has already occurred. This probability is written as P(B|A) and referred to as “the probability of B given A.” In the instant context, this would be referred to as the probability of finding malicious code or software in given electronic data traffic given the attributes or characteristics of that electronic data traffic.

If events A and B are not independent, then the probability of the intersection of A and B (the probability that both events occur) is defined by P(A and B)=P(A)P(B|A). From this definition, the conditional probability P(B|A) is obtained by dividing by P(A):

$P\left(B|A\right)=\frac{P\left(A\mathrm{and}B\right)}{P\left(A\right)}$

Event “A” in the context of characteristics of the electronic data traffic might include, for example, the URL from which the electronic data traffic is being received, and/or a corresponding reputation thereof, a category of the content being received (e.g., gambling pages, or cooking recipes), or type of file be carried by the electronic data traffic (e.g., .exe, .pdf, etc.), a user-agent type (e.g., an application used to request the content), geographical of either or both a server from which the electronic data traffic is being received or a user or end point device that is to receive the electronic data traffic, among many other possibilities.

In one implementation, AM/AV scanning application logic 200 has a set of a priori knowledge about the likelihood (probability) of given electronic data traffic carrying malicious software given certain characteristics of that given electronic data traffic. As the traffic passes through host 130, AM/AV scanning application logic 200 operates on or processes the traffic and generates a conditional probability that the traffic is carrying malicious software. It is noted that there may be multiple characteristics that AM/AV scanning application logic 200 considers in determining the conditional probability. As such, a final conditional probability that given electronic data traffic carries malicious software may be a combination of multiple conditional probabilities.

FIG. 4 is a flow chart depicting example operations for placing electronic data traffic in a ranked order according to computed conditional probability. At 410, the electronic data traffic is monitored. At 412, conditional probabilities that given electronic data traffic contains or carries malicious software are calculated in connection with respective characteristics of the electronic data traffic (e.g., URL/reputation, content category, file type, etc.). At 414, the calculated conditional probabilities are added or otherwise aggregated to obtain a final conditional probability for the given electronic data traffic. At 416, an entry is made in, e.g., a table or list stored in memory wherein the entry is placed in rank order in the list.

FIG. 5 illustrates an example ranked list of electronic data traffic based on final conditional probabilities calculated for respective segments or portions of electronic data traffic. In the example shown, the portions are ranked in order as #8, #2, #12 and #1. These portions may represent, e.g., individual sessions, individual packets, collections of packets, or any segmentation of the overall traffic that transits a host on which AM/AV scanning application logic 200 resides. As shown in the drawing, traffic portions #8, #2 and #12 are ranked near the top of the list. In this case, and as shown by broken line 510, those three portions will be scanned by one or more scanners 125, whereas portion #1 will not be scanned at all, as the conditional probability that portion #1 contains a virus or malware is relatively lower than the other three portions. By ranking segments of electronic data traffic in this way, it is possible to optimize which traffic is scanned since it may not be efficient (or even possible) to scan all such traffic.

That is, rather than setting hard thresholds or limiting scanning of content carried by electronic data traffic to certain types of files, embodiments described herein rank the electronic data traffic based on a plurality of characteristics and resulting conditional probabilities. Scanning can then take place on the electronic data traffic (i.e., the content therein) with the highest rankings. In this way, scanner 125 can be put to use in the a more optimized way by passing content through the scanners that is determined to more likely carry malicious software.

FIG. 6 is a flow chart depicting example operations for selecting a second or subsequent scanner to scan electronic data traffic. Preliminarily, it is noted that scanners can be configured or tuned in such a way to be better at catching or identifying malicious code in different types of files. For instance, one type of scanner might be better at catching malicious code in executable files, while another scanner might be better at catching malicious code in a PDF type file. Thus, as an initial matter, one embodiment of AM/AV scanning application logic 200 selects which scanner from among possibly multiple scanners should be used to scan any given electronic data traffic. Not only can the selection be based on the type of traffic that is to be analyzed or scanned, but the selection of a given scanner might also be based on its availability at that moment to process the traffic. It should be kept in mind that the methodologies being described herein may be operating in real-time or near-real-time contexts, such that end users might detect latency should scanning requests become backlogged. By selecting available scanners 125, such latency can be reduced.

Thus, in accordance with an embodiment, AM/AV scanning application logic 200 first selects an available and appropriate scanner 125 to employ for a first pass of scanning. Then, if another scanner might be available and it is determined that such a scanner might catch malicious software that the first scanner did not detect, then AM/AV scanning application logic 200 may further cause the electronic data traffic being processed to be processed by a second or even third, etc. scanner.

Still with reference to FIG. 6, using AM/AV scanning application logic 200, at 610, the electronic data traffic is scanned with a first scanner. At 612, a conditional probability that a second or subsequent scan by another scanner can find a virus or malware beyond that already found by the first scan is computed. The particular traffic may then be placed in ranked order similar to the ranking shown in FIG. 5. At 616, it is determined whether more scanners might be available for subsequent scanning of the same electronic data traffic. If no, the operation ends. If yes, another conditional probability is computed with respect to any such subsequent scanner, as indicated by 612.

With the additional scanner(s) so selected, the same electronic data traffic can be passed through the additional scanner(s) such that an overall catch rate of malicious code can be improved.

To keep the system and methodology relevant and updated over time, a periodic feedback mechanism is employed. FIG. 7 shows example operations for updating multiple instances of AM/AV scanning application logic 200 such that the conditional probabilities that are computed can be as accurate as practicable.

Referring to FIG. 7, and at 710, random electronic data traffic is scanned on a periodic basis. The period can be on the order of minutes, hours, days, or weeks, etc. AM/AV scanning administration logic 250 can be used to set the periodicity of the random scanning of 710. Likewise, the amount of traffic that is scanned can also be configured via AM/AV scanning administration logic 250. Random single packets, groups of packets, parts or complete sessions may be selected for scanning.

At 712, it is determined whether malicious software has been found via the scanning procedure. If not, operation 710 is repeated. If malicious code is found, then at 714, relevant characteristics of the electronic data traffic are captured. These characteristics might include, e.g., a URL, the category of the content being carried or the type of file be carried, among many other possible characteristics. At 716 this information (namely that malicious software was (or was not) found in electronic data traffic having the captured characteristics) is broadcast or otherwise made available to other instances of AM/AV scanning application logic 200 that may be operating within other network architectures. In this way, different instances of AM/AV scanning application logic 200 can share information with one another, enabling individual instances to be as up to date as may be practicable so that the conditional probabilities that are calculated or as accurate as possible.

Reference has been made to portions or segments of electronic data traffic. In the context of the embodiments described herein, those segments or portions can be based on information gleaned from layers 3-7 of Open Systems Interconnection (OSI) model wherein the headers and payloads of various packets or frames of the electronic data traffic at these several layers are inspected for maliciousness and for the characteristics that are used to compute the conditional probabilities.

Thus, as explained, electronic data traffic transiting a security appliance or host is dynamically selected to be scanned based on a function of the probability that the traffic contains malicious software or code. In one embodiment, scanners are employed at their maximum throughput such that the greatest amount of electronic data traffic can be scanned without substantially impacting an expected quality of service by an end user. Scanners can also be used in sequence in an attempt to capture as much malicious code as possible. Periodically, a random sample of electronic data traffic is scanned by one or more scanners and information gleaned from such scans is distributed to network appliances that operate in accordance with the principles described herein.

Reference is now made to FIG. 8, which depicts example operations for performing conditional probabilities-based AM/AV scanning according to the principles described herein.

Operation 810 includes monitoring electronic data traffic in an electronic data network via an electronic interface with the electronic data network. Operation 812 includes calculating a first conditional probability that content in first given electronic data traffic being monitored is malicious. Operation 814 includes calculating a second conditional probability that content in second given electronic data traffic being monitored is malicious.

Operation 816 includes ranking the first and second conditional probabilities resulting in ranked conditional probabilities, and operation 818 includes performing at least one of anti-virus (AV) or anti-malware (AM) scanning of the content of the first or second given electronic data traffic depending on whose conditional probability is ranked higher in the ranked conditional probabilities.

Operation 820 includes performing at least one of AV or AM scanning of content of third given electronic data traffic, determining, as a result of the AV or AM scanning, whether the content of the third given electronic data traffic is malicious, and when the content of the third given electronic data traffic is determined to be malicious, capturing characteristics of the third given electronic data traffic, wherein the characteristics are employed to calculate conditional probabilities that content of still other given electronic data traffic is malicious

Operations may further include selecting a first one of a plurality of scanners for performing the at least one of AV or AM scanning, wherein selecting is based on an efficacy value associated with respective ones of the plurality of scanners for a given type of content that is being carried by the first or second given electronic data traffic whose conditional probability is ranked higher in the ranked conditional probabilities.

Operations may still further comprise selecting a second one of the plurality of scanners for a performing a subsequent operation of performing at least one of AV or AM scanning after scanning by the first one of the plurality of scanners, wherein selecting of the second one of the plurality of scanners is based on a catch rate of malicious content not caught by the first one of the plurality of scanners.

In one embodiment, the functionality for calculating the conditional probabilities and controlling which scanners are to be employed for scanning may be deployed at a network security appliance that is logically disposed between an end-point device and a firewall that is in communication with the Internet. This functionality may also be deployed with a cloud computing environment.

Although the apparatus, system and method are illustrated and described herein as embodied in one or more specific examples, it is nevertheless not intended to be limited to the details shown, since various modifications and structural changes may be made therein without departing from the scope of the apparatus, system, and method and within the scope and range of equivalents of the claims. A Data Center can represent any location supporting capabilities enabling service delivery that are advertised. A Provider Edge Routing Node represents any system configured to receive, store or distribute advertised information as well as any system configured to route based on the same information. Accordingly, it is appropriate that the appended claims be construed broadly and in a manner consistent with the scope of the apparatus, system, and method, as set forth in the following.

Claims

1. A method comprising: monitoring data traffic in a data network via an electronic interface with the data network, wherein monitoring comprises monitoring data packets traversing the data network at an appliance that is logically disposed between an end-point device and the Internet;calculating a first conditional probability that content in first given data traffic being monitored is malicious;calculating a second conditional probability that content in second given data traffic being monitored is malicious;ranking the first and second conditional probabilities with respect to each other resulting in ranked conditional probabilities; andperforming at least one of anti-virus or anti-malware scanning of the content of the first or second given data traffic depending on whose conditional probability is ranked higher in the ranked conditional probabilities to the exclusion of the other of the first or second given data traffic;selecting a first one of a plurality of scanners for performing the at least one of anti-virus or anti-malware scanning, wherein selecting is based on an efficacy value associated with respective ones of the plurality of scanners for a given type of content that is being carried by the first or second given data traffic whose conditional probability is ranked higher in the ranked conditional probabilities;selecting a second one of the plurality of scanners for a performing a subsequent operation of performing at least one of anti-virus or anti-malware scanning after scanning by the first one of the plurality of scanners, wherein selecting of the second one of the plurality of scanners is based on a conditional probability that the second one of the plurality of scanners can catch malicious content not caught by the first one of the plurality of scanners,wherein a same content of the first or second given data traffic is processed by the first one of a plurality of scanners and the second one of a plurality of scanners, andwherein selecting the first one of a plurality of scanners is based on a data type of the content of the first or second given data traffic.

2. The method of claim 1, wherein monitoring data traffic comprises monitoring data packets at a network security appliance that is logically disposed between an end-point device and a firewall that is in communication with the Internet.

3. The method of claim 1, further comprising: performing at least one of anti-virus or anti-malware scanning of content of third given data traffic;determining, as a result of the anti-virus or anti-malware scanning, whether the content of the third given data traffic is malicious; andwhen the content of the third given data traffic is determined to be malicious, capturing characteristics of the third given data traffic, wherein the characteristics are employed to calculate conditional probabilities that content of still other given data traffic is malicious.

4. The method of claim 3, wherein capturing characteristics comprises capturing a universal resource locator from which the third given data traffic was received.

5. The method of claim 1, further comprising selecting the first one of a plurality of scanners for performing the at least one of anti-virus or anti-malware scanning based on available throughput of respective ones of the plurality of scanners.

6. An apparatus comprising: a network interface unit configured to communicate over a data network;a memory; anda processor configured to:monitor electronic data traffic in the data network via the interface, by monitoring data packets traversing the data network when the appliance is logically disposed between an end-point device and the Internet;calculate a first conditional probability that content in first given data traffic being monitored is malicious;calculate a second conditional probability that content in second given data traffic being monitored is malicious;rank the first and second conditional probabilities with respect to one another resulting in ranked conditional probabilities; andcause at least one of anti-virus or anti-malware scanning of the content of the first or second given data traffic to be performed depending on whose conditional probability is ranked higher in the ranked conditional probabilities to the exclusion of the other of the first or second given data traffic,wherein the processor is further configured to select a first one of a plurality of scanners for performing the at least one of anti-virus or anti-malware scanning based on an efficacy value associated with respective ones of the plurality of scanners for a given type of content that is being carried by the first or second given electronic data traffic whose conditional probability is ranked higher in the ranked conditional probabilities,wherein the processor is further configured to select a second one of the plurality of scanners for performing a subsequent operation of performing at least one of anti-virus or anti-malware scanning after scanning by the first one of the plurality of scanners, wherein the processor is configured to so select the second one of the plurality of scanners based on a conditional probability that the second one of the plurality of scanners can catch rate malicious content not caught by the first one of the plurality of scanners,wherein a same content of the first or second given data traffic is processed by the first one of a plurality of scanners and the second one of a plurality of scanners, andwherein the first one of a plurality of scanners is selected based on a data type of the content of the first or second given data traffic.

7. The apparatus of claim 6, wherein the processor is further configured to: cause at least one of anti-virus or anti-malware scanning of content of third given data traffic to be performed;determine, as a result of the anti-virus or anti-malware scanning, whether the content of the third given data traffic is malicious; andwhen the content of the third given electronic data traffic is determined to be malicious, capture characteristics of the third given data traffic, wherein the characteristics are employed to calculate conditional probabilities that content of still other given data traffic is malicious.

8. The apparatus of claim 7, wherein the capture of characteristics comprises capturing a universal resource locator from which the third given data traffic was received.

9. The apparatus of claim 6, wherein the processor is further configured to select the first one of a plurality of scanners for performing the at least one of anti-virus or anti-malware scanning based on available throughput of respective ones of the plurality of scanners.

10. One or more non-transitory computer readable storage media encoded with software comprising computer executable instructions and when the software is executed operable to: monitor data traffic in a data network via an interface;calculate a first conditional probability that content in first given data traffic being monitored is malicious;calculate a second conditional probability that content in second given data traffic being monitored is malicious;rank the first and second conditional probabilities with respect to one another resulting in ranked conditional probabilities; andcause at least one of anti-virus or anti-malware scanning of the content of the first or second given data traffic to be performed depending on whose conditional probability is ranked higher in the ranked conditional probabilities to the exclusion of the other of the first or second given data traffic,wherein the instructions are further operable to select a first one of a plurality of scanners for performing the at least one of anti-virus or anti-malware scanning based on an efficacy value associated with respective ones of the plurality of scanners for a given type of content that is being carried by the first or second given electronic data traffic whose conditional probability is ranked higher in the ranked conditional probabilities,wherein the instructions are further operable to select a second one of the plurality of scanners for performing a subsequent operation of performing at least one of anti-virus or anti-malware scanning after scanning by the first one of the plurality of scanners, wherein the processor is configured to so select the second one of the plurality of scanners based on a conditional probability that the second one of the plurality of scanners can catch malicious content not caught by the first one of the plurality of scanners,wherein a same content of the first or second given data traffic is processed by the first one of a plurality of scanners and the second one of a plurality of scanners, andwherein the first one of a plurality of scanners is selected based on a data type of the content of the first or second given data traffic.

11. The computer readable storage media of claim 10, wherein the instructions are further operable to: cause at least one of anti-virus or anti-malware scanning of content of third given data traffic to be performed;determine, as a result of the anti-virus or anti-malware scanning, whether the content of the third given data traffic is malicious; andwhen the content of the third given electronic data traffic is determined to be malicious, capture characteristics of the third given data traffic, wherein the characteristics are employed to calculate conditional probabilities that content of still other given data traffic is malicious.

12. The computer readable storage media of claim 10, wherein the instructions are further operable to capture a universal resource locator from which the third given data traffic was received.

13. The computer readable storage media of claim 10, wherein the instructions are further operable to select the first one of a plurality of scanners for performing the at least one of anti-virus or anti-malware scanning based on available throughput of respective ones of the plurality of scanners.