Patent Publication Number: US-10764311-B2

Title: Unsupervised classification of web traffic users

Description:
TECHNICAL BACKGROUND 
     Various kinds of automated attacks are possible on web servers that provide web services, such as using stolen credentials to fraudulently access the service, brute-force attacks that try several username and password combinations to gain access, registering fake accounts, scraping websites to harvest web data, and others. Such velocity attacks typically require a large number of transactions with the web service in a very short period of time, and commonly-used web browsers are prohibitively slow for such large-scale and high-speed transactions. Instead, attackers use a wide variety of attack tools, ranging from simple shell scripts to sophisticated custom tools designed to speed up transactions. 
     Unfortunately, attack tools are often designed to deceive a web service into believing that the traffic is actually originating from a prevalent web browser. To achieve this subterfuge, the User-Agent header of a well-known browser may be forged in the hypertext transfer protocol (HTTP) request header of traffic originating from a malicious attack tool. Because the User-Agent string exactly matches one of the well-known web browsers, the web service and any attack-prevention techniques that rely on identifying the User-Agent string are unable to differentiate between a real web browser and a forgery, leaving the web service vulnerable to exploitation by malicious individuals employing attack tools to access the service. 
     OVERVIEW 
     Disclosed herein are techniques to facilitate web traffic classification. In at least one implementation, web traffic between a plurality of clients and at least one web server is monitored, and the web traffic is analyzed to determine attribute data points associated with each individual client of the plurality of clients. The attribute data points associated with each individual client are compared to define a plurality of client groups based on similarities in the attribute data points among each individual client. A client of the plurality of clients is identified as malicious when the client is included in more than one of the client groups. 
     This Overview is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. It may be understood that this Overview is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram that illustrates a communication system. 
         FIG. 2  is a flow diagram that illustrates an operation of the communication system. 
         FIG. 3  illustrates exemplary User-Agent header fields for various web browsers. 
         FIG. 4  illustrates exemplary security parameters from a modern web browser. 
         FIG. 5  is a block diagram that illustrates an exemplary representation of user fingerprints having various attributes. 
         FIG. 6  is a block diagram that illustrates an exemplary representation of a user fingerprint having a modified attribute. 
         FIG. 7  is a block diagram that illustrates an exemplary representation of grouping user fingerprints having similar attributes. 
         FIG. 8  is a block diagram that illustrates a computing system. 
     
    
    
     DETAILED DESCRIPTION 
     The following description and associated figures teach the best mode of the invention. For the purpose of teaching inventive principles, some conventional aspects of the best mode may be simplified or omitted. The following claims specify the scope of the invention. Note that some aspects of the best mode may not fall within the scope of the invention as specified by the claims. Thus, those skilled in the art will appreciate variations from the best mode that fall within the scope of the invention. Those skilled in the art will appreciate that the features described below can be combined in various ways to form multiple variations of the invention. As a result, the invention is not limited to the specific examples described below, but only by the claims and their equivalents. 
     Some security technologies detect a forged browser by actively injecting JavaScript code or modifying the pages being served by the web service as part of the web server&#39;s response to client requests. The web server can then use the results of the JavaScript execution to determine whether or not the client making the requests is actually a genuine web browser. However, this technique requires integration within the web service or operating in-line with the web service, which may involve additional development on the web service and may adversely affect its performance due to active modification of the pages being served. The following disclosure enables passive detection of malicious activity without any active page modification. 
     There are many attributes associated with a user interacting with a web server. However, if the user is employing a malicious automation tool to attack the web service, some of these attributes may be modified in order to appear as legitimate traffic, such as traffic originating from a well-known web browser. For example, malicious automation tools might change the most visible attributes in order to pose as legitimate traffic, but inspection of less visible layers could reveal the true identity of the user. One approach to detecting these malicious users involves the discovery of inconsistencies between these attributes. However, keeping track of the numerous variations between attributes belonging to legitimate web traffic can be arduous and intensive. 
     Implementations disclosed herein provide unsupervised web traffic classification to facilitate detection of malicious users. In at least one implementation, web traffic is monitored between client systems and a web server. The web traffic is then analyzed to determine attributes associated with each individual client. The different clients are then grouped together based on the similarity of their various attributes. The grouping is performed such that the differences between the groups are maximized, thereby greatly improving the classification and detection of malicious automation tools. The grouping process is unsupervised and may be automated and updated dynamically when unknown clients are introduced. Inclusion of a client in more than one group is evidence of tampering with one or more attributes, which is indicative of a user with malicious intent. 
     Referring now to  FIG. 1 , a block diagram of communication system  100  is illustrated. Communication system  100  includes client computing systems  101 ,  102 , and  103 , communication network  120 , and web server computing system  130 . Client computing system  101  and communication network  120  communicate over communication link  121 . Likewise, client computing system  102  and communication network  120  communicate over communication link  122 . Client computing system  103  and communication network  120  communicate over communication link  123 . Communication network  120  and web server computing system  130  are in communication over communication link  131 . Note that most modern communication systems would typically include many more client computing systems  101 - 103 , but only three are shown in  FIG. 1  to simplify this discussion. 
     In operation, the client computing systems  101 - 103  submit HTTP requests and exchange web traffic with web server  130 . Various aspects of the web traffic and the HTTP requests can then be analyzed by web server  130  or some other processing system to determine attribute data points for each client  101 - 103  that describe the particular form and content of the HTTP request, connection behavior and interactions with server  130  during the HTTP request, and any other attributes that uniquely identify the web traffic and HTTP requests. There are thousands of variations of attributes that legitimate traffic might exhibit, and the attributes can be processed to identify correlations and patterns among these variations. These correlations and patterns can be exploited in order to classify web clients by grouping them together based on a similarity metric. Various attributes associated with each web client are processed to determine group membership for each client. A client belonging to more than one group is evidence of tampering with one or more attributes, which is indicative of a user with malicious intent. An exemplary implementation to facilitate web traffic classification will now be discussed with respect to  FIG. 2 . 
       FIG. 2  is a flow diagram that illustrates an operation  200  of communication system  100 . The operation  200  shown in  FIG. 2  may also be referred to as classification process  200  herein. The steps of the operation are indicated below parenthetically. The following discussion of operation  200  will proceed with reference to client computing systems  101 - 103  and web server  130  of  FIG. 1  in order to illustrate its operations, but note that the details provided in  FIG. 1  are merely exemplary and not intended to limit the scope of process  200  to the specific implementation shown in  FIG. 1 . 
     Operation  200  may be employed by computing system  130  to facilitate web traffic classification. As shown in the operational flow of  FIG. 2 , computing system  130  monitors web traffic between a plurality of clients  101 - 103  and at least one web server  130  ( 201 ). Clients  101 - 103  comprise computing systems executing any application that interacts with web server  130 , such as a genuine web browser, attack tools such as a script or bot, or any other software program. Web server  130  typically provides a web service to clients  101 - 103 , which could comprise any service that may be available over a communication network, such as file transfers, streaming media, email, financial services, e-commerce, social media, online gaming services, or any other web service, including combinations thereof. In this example, the web traffic is both hosted and monitored by server computing system  130 , although the web traffic could be provided and/or monitored by a different computing system in some implementations. The web traffic monitored between web server  130  and clients  101 - 103  includes hypertext transfer protocol (HTTP) requests transmitted by clients  101 - 103  and HTTP responses from web server  130 , which typically include payload data requested by clients  101 - 103 . 
     Computing system  130  analyzes the web traffic to determine attribute data points associated with each individual client of the plurality of clients  101 - 103  ( 202 ). The attribute data points for clients  101 - 103  describe the unique form and content of any HTTP requests in the web traffic, fields in the HTTP request headers transmitted by clients  101 - 103 , the connection behavior of clients  101 - 103  when interacting with web server  130 , security parameters and other information exchanged between clients  101 - 103  and web server  130 , and any other attributes associated with the web traffic. In at least one implementation, computing system  130  could analyze the web traffic to determine the attribute data points by analyzing the web traffic to determine fields in individual HTTP request headers transmitted by each individual client of the plurality of clients  101 - 103 . Computing system  130  could also determine an order of the fields listed in each of the individual HTTP request headers transmitted by each individual client  101 - 103  and include this information in the attribute data points associated with each individual client  101 - 103 . The presence or absence of different header fields, the order the fields are listed in the header, and the content of the fields can all be used to generate attribute data points for each respective client  101 - 103 . For example, an HTTP request sent by client computing system  101  may include multiple header fields such as Host, Accept, and Accept-Encoding, among others. Some of these fields are optional and therefore will not always be included in an HTTP request header. Thus, the particular fields that client  101  includes in the HTTP request header may be factored in when analyzing the web traffic to determine the attribute data points associated with client  101 . In other words, the attribute data points could be partly based on what fields are included in the HTTP request header. Further, different web browsers and even different versions of the same web browser may arrange these fields in different orders in the header. Thus, in some implementations, computing system  130  could generate the attribute data points for client  101 , in part, based on an order or arrangement of the fields in the HTTP request header transmitted by the client  101 . Accordingly, the attribute data points could be based on which of the fields are included in the HTTP request header, the order in which the fields are listed, and the content provided in the fields. 
     The values in the HTTP header fields are also driven by the capabilities of the web browsers and their implementation preferences, and computing system  130  could analyze the web traffic to determine the attribute data points for a particular client  101  based on capabilities supported by client  101  as indicated in the fields in the HTTP request header. For example, some browsers choose to expose the Accept-Encoding header field as one or more values from gzip, compress, deflate, and the like, depending on the capabilities available in the browser on a given platform, while other browsers may choose to completely forego including the Accept-Encoding header field altogether. The protocols, languages, and other features that the browser supports may also be listed in the HTTP header fields, such as support for various scripting languages, Flash® media, compression algorithms, and others. Moreover, as browsers release new versions, they include newer capabilities as well. For example, a default protocol version (i.e., 1.0, 1.1, 2.0) to use for the request may be continually updated in newer release versions of a browser, so computing system  130  could determine attribute data points for client  101  based on the default protocol version indicated in the HTTP request header. Further, new fields like Do Not Track (DNT) may be introduced in newer versions of a browser that were not present in older versions. Any of this kind of capability information that may be included in the HTTP request header could be used by web server  130  to determine attribute data points for client  101 . 
     In addition to the HTTP layer, computing system  130  may also consider security information in the secure socket layer (SSL). For example, a hypertext transfer protocol secure (HTTPS) session using SSL established between web server  130  and a client  101  typically includes various session configuration data and security parameters, such as protocol versions, session identifiers, cipher suites, compression methods, random values, and other session setup information. The particular cipher suites listed, the total number of cipher suites included, the order of the cipher suites, and other attributes of the security information transmitted by client  101  during HTTPS session establishment can be used to generate attribute data points that help provide a unique signature of client  101 , which is typically distinctive for each different type of legitimate web browser and other types of client applications. For example, different web browsers and even different versions of the same web browser may include different cipher suites in the list, and may arrange and order the cipher suites and other security information differently. Essentially, any nuances in the content, manner, and format in which a client  101  presents security parameters and other information during HTTPS session establishment with web server  130  can be used to determine attribute data points for that client  101  which help to provide a unique fingerprint of client  101 . 
     In some implementations, analyzing the web traffic to determine the attribute data points could comprise analyzing the web traffic to determine connection behavior between each individual client of the plurality of clients  101 - 103  and the at least one web server  130 . For example, web server  130  may monitor the connection behavior of web client  101  with web server  130  for use in generating the attribute data points for client  101 . Web browsers may interact with a web server in different ways. For example, some browsers choose to send multiple HTTP requests in the same transmission control protocol (TCP) connection, while others create a new connection for every request. Some browsers send multiple requests in the same connection even before they start receiving responses from the server, while others wait to send subsequent requests in the same connection until a response to an initial request on that connection is returned. Thus, in some implementations, web server  130  could determine the attribute data points for client  101  based on whether or not client  101  sends multiple HTTP requests over a same connection to web server  130 . In some examples, determining the connection behavior of client  101  can also include monitoring how client  101  responds to requests from web server  130 , such as observing how client  101  responds to a request from web server  130  to fall back to an older protocol version. Further, the attribute data points could indicate user behavior observed when a particular client  101  accesses the web service provided by web server  130 , such as the timing of clicks, keystrokes, and other user inputs, page navigations, page scrolling, content requests, and the like. 
     In certain cases, browsers may choose to keep a connection or multiple connections open for a period of time, even if there are no active requests or responses in transit. In other words, the length of time that a connection persists, even though no data may be flowing over that connection, can differ between different types of web browsers. Accordingly, the attribute data points for a particular client  101  may be determined based on a length of time that client  101  maintains a connection with web server  130 . Other behavior of client  101  could be determined as well, such as the order and manner in which client  101  parses the hypertext markup language (HTML) and other code when fetching a web page. For example, when parsing HTML, some browsers will parse hyperlinks and other textual content in a different way than images or video, such as fetching all images first, or processing all JavaScript code first before fetching images, or fetching images with a different connection than other page content, and any other nuances in HTML parsing and page fetching. Any of the above information in the web traffic, such as the HTTP header fields, connection behavior, and other data that can be observed from the interactions of client systems  101 - 103  with web server  130  can be used to generate the attribute data points associated with each individual client  101 - 103  that effectively provides a unique fingerprint or signature of how each client  101 - 103  is operating. 
     Computing system  130  compares the attribute data points associated with each individual client  101 - 103  to define a plurality of client groups based on similarities in the attribute data points among each individual client ( 203 ). For example, computing system  130  may employ a similarity metric to define the client groups by comparing the attribute data points and grouping clients together that have similar attribute data points. For example, in at least one implementation, comparing the attribute data points to define the plurality of client groups could comprise determining an order of the fields listed in each of the individual HTTP request headers transmitted by each individual client and grouping individual clients of the plurality of clients  101 - 103  associated with ones of the individual HTTP request headers having similarly ordered fields to define the plurality of client groups. In some examples, the attribute data points for each client  101 - 103  could be processed to generate unique fingerprints for each individual client  101 - 103 , where the fingerprints comprise numerical vectors in multidimensional space generated by encoding the attribute data points associated with each respective client  101 - 103 . Thus, in some implementations, comparing the attribute data points to define the plurality of client groups could comprise generating individual fingerprints for each individual client  101 - 103  by encoding the attribute data points associated with each individual client  101 - 103  into numerical vectors for each individual client and processing the individual fingerprints to define the plurality of client groups. In some examples, attributes may appear similar between two different clients at a particular layer, such as the transport layer or the session layer, but deeper analysis and comparison of the fingerprints may reveal differences in the attributes at the data link layer or the network layer. 
     In at least one implementation, processing the individual fingerprints to define the plurality of client groups may comprise calculating distances between the numerical vectors in multidimensional space and grouping the individual fingerprints based on the distances between the numerical vectors. For example, a numerical vector generated by encoding the attribute data points associated with a particular client  101  could be plotted along with other vectors in multidimensional space, and the distance between each numerical vector can be computed using a stand metric such as the Euclidean distance. The calculated distances between the vectors in multidimensional space can then be used to define the client groups based on grouping proximate vectors. Thus, in some implementations, grouping the individual fingerprints based on the distances between the numerical vectors could comprise grouping the individual fingerprints based on the distances between the numerical vectors falling within a proximity threshold. For example, different groups may be identified where multiple clients have similar attributes appearing in clusters on the vector plots that fall within the proximity threshold. The proximity threshold could be adjusted to provide for different levels of security in some examples. For example, the thresholds could be set on a per-client basis according to the level of security desired, but they are ideally set to accurately identify malicious users while also avoiding any misclassification of valid users as malicious by mistakenly including them in multiple groups. 
     Computing system  130  identifies a client of the plurality of clients  101 - 103  as malicious when the client is included in more than one of the client groups ( 204 ). If a client does not have any modified attributes, that client will only belong to one client group. However, if a malicious user modifies any attributes, the techniques disclosed herein will ensure that the user will belong to more than one group, and can therefore be flagged as suspicious. Computing system  130  is thus able to identify one of the clients  101 - 103  as malicious when a particular client is a member of multiple client groups. 
     Advantageously, web server computing system  130  is capable of unsupervised web traffic classification. By passively monitoring web traffic and interactions between clients  101 - 103  and web server  130  to generate attribute data points for the clients  101 - 103 , computing system  130  is able to compare the attribute data points among the different clients  101 - 103  to define groups of different clients based on similarity metrics. Computing system  130  can then discover potentially malicious clients by identifying clients that belong to more than one group. Accordingly, by detecting malicious clients and eliminating their illegitimate requests from forged web browsers and other malicious attack tools, the techniques described herein provide the technical advantage of reducing the load on the processor, network components, and other elements of web server  130 , while also safeguarding the information of users of the web service. In this manner, web server  130  can positively identify potentially malicious clients and effectively thwart attacks on the web service from automation tools and other malware. 
     Referring now to  FIG. 3 , exemplary User-Agent header fields are shown for various web browsers. Every well-known web browser exposes the Browser Name, Version, and Platform combination through the User-Agent header field.  FIG. 3  provides a few examples of this User-Agent field from various browsers. Attack tools often copy and use the User-Agent string from prevalent web browsers in their own HTTP request headers to disguise themselves as those browsers. However, such attack tools typically fail to replicate all of the behavioral characteristics of the real web browsers they are trying to emulate, and these inconsistencies can be used to detect applications, scripts, and other malicious automation tools that are trying to hold themselves out as prevalent browsers. 
     In some examples, additionally or alternatively to comparing attributes for unknown clients in an uncontrolled environment, a trusted learning environment may be employed where the traffic being sent between the computing devices and the web server is controlled. This environment can be used to automatically learn all of the subtle behavioral differences for every well-known web browser and its various incremental version releases. In particular, a known web browser to be analyzed is loaded onto a computing device and controlled traffic is exchanged with the web server while monitoring all of the default behavior of the browser. The information monitored includes the various fields in the HTTP request headers sent by the browser, including which fields are provided, the order that the fields are presented, which protocols, languages, tools, and other features the browser supports, and any other information in the HTTP headers that may be uniquely associated with the web browser. Other behavior is also tested and observed, such as the protocol version (i.e., HTTP version) that the browser uses to perform the initial handshake with the web server, or the manner in which the web browser responds to a request from the web server to fall back to an older protocol version than the browser used initially. 
     The connection behavior of the browser is also recorded, such as whether the browser sends multiple HTTP requests in the same connection or opens a new connection for each request. Other connectivity behavior that could be tested is whether the browser sends multiple requests in the same connection before ever receiving a response from the web server, or whether the browser waits for a response to an initial request before sending subsequent requests. The length of time that the connection or connections persist is also measured, which may remain open for some period of time even though no data is flowing between the endpoints. The manner and order in which elements of a web page (i.e., text, hyperlinks, images, videos, advertisements, JavaScript, and other page content) are fetched by the browser when parsing the HTML code of a web page are also tracked, including whether or not the browser creates new connections to fetch each of the various different page elements. 
     In this manner, all HTTP request and response traffic is passively monitored, and these static and dynamic behaviors are then mapped back to the actual web browsers under their respective User-Agent string and stored as attribute data points for later comparison. Unique fingerprints can then be generated for each web browser type by encoding the attribute data points associated with a particular browser into a high-dimensional numerical vector. New behaviors of new versions of prevalent web browsers are continuously learned in this environment as they are released, ensuring the database remains current, relevant, and effective. New behaviors of web browsers can also be added to their attribute data points as they are learned, which can be observed from the browsers&#39; behavior as they access different websites, different pages and file types (i.e., HTML, images, text, scripts, and others), and make different types of HTTP requests (i.e., GET, HEAD, POST, and the like). 
     In addition to observing the various different web browser interactions, known attack tools may also run in the trusted test environment. In this case, even though the attack tools may be fraudulently manipulating the User-Agent string, this controlled test environment provides for tracking the traffic from the attack tools to learn their behavior and observe how it differs from the genuine web browsers they are imitating. This information can aid in identifying when a particular attack tool is being used, which helps strengthen the determination that the traffic is not coming from a genuine web browser. After amassing the data as described above for all well-known web browsers and their various release versions, the system can operate in an untrusted environment with a mixture of real and forged browsers and monitor the web traffic of the clients. By passively monitoring the web traffic between the clients and the web server to generate attribute data points for each of the clients, the system is then able to compare the attribute data points among the clients to define groups of different clients based on similarities in the attribute data points among each individual client. Potentially malicious clients can then be discovered by identifying clients that belong to more than one group. 
     Referring now to  FIG. 4 , exemplary security parameters are shown from a modern web browser.  FIG. 4  provides an example of security parameters from a web browser that may be included in the ‘Client Hello’ message sent to the server during secure session establishment. Some examples of modern web browsers that may present such security parameters include Firefox 27, Chrome 22, Opera 14, Safari 7, and Internet Explorer 11. However, note that the list of cipher suites supported by each browser will be different, along with the order that each browser presents the cipher suites in the list. For example, there may be a greater number of cipher suites included in the security parameters for older web browser versions than those shown in  FIG. 4  for a more modern browser, because the older browser version may include older cipher suites that are no longer supported by the more modern browser shown in the example of  FIG. 4 . 
     The various security parameters shown in  FIG. 4  demonstrate the different cipher suites supported by the browser, the number of cipher suites, the order of the cipher suites, and the other security information that may be part of a ‘Client Hello’ message sent during secure session establishment, such as the transport layer security (TLS) or SSL protocol versions, RSA key size, Diffie-Hellman (DH) parameter size, elliptical curves, certificate signature, HTTP Strict Transport Security (HSTS) values, and others. These various different security attributes that may be sent by a client to establish a secure session with a web server can be used along with any other information to generate a unique fingerprint for identifying a particular browser. These signatures are unique for each different type of web browser. 
     Most malicious attackers use automated tools to target web servers. When a web server is HTTPS enabled, the attackers are forced to use HTTPS as well. The security signature of an attack tool will be very different from the security signature of a real web browser. The techniques disclosed herein to determine attribute data points for various clients can utilize these differences in the security signatures of the clients as part of determining an overall fingerprint for each client, which can then be compared with the fingerprints of other clients to determine the group or groups to which the client belongs. When a client is a member of more than one group, this is evidence of tampering with parameters and malicious intent, thereby enabling the system to identify and block the malicious traffic before it can cause any harm. An example of this grouping mechanism will now be discussed with respect to  FIGS. 5-7 . 
       FIG. 5  is a block diagram that illustrates an exemplary representation of user fingerprints having various attributes. The lists of attributes for each user provide an abstract representation of the unique fingerprint of each user. In this example, each fingerprint contains N attributes. The attributes for users 1 and 3 are represented abstractly as triangle shapes, whereas the attributes for users 2 and 4 are represented as squares, and these similarities in attributes may be used to group the users accordingly. Note that the shapes shown for the attributes in  FIG. 5  are merely provided as an abstraction to represent the similarities or differences between the various attributes for the purpose of facilitating this discussion. 
       FIG. 6  is a block diagram that illustrates an exemplary representation of a user fingerprint having a modified attribute. In this example, the attributes observed for ‘User X’ are shown, who has tampered with attribute 1. The other attributes have not been modified. Thus, although the first attribute has been altered to appear like the “square” attributes of User 2 shown in  FIG. 5 , the other attributes reveal the true identity of the client, which are similar to the triangular-shaped attributes of User 1 of  FIG. 5 . The different attributes appearing in the fingerprint of “User X’ suggest that the user is using the same or similar client application as User 1 since most of their attributes are represented abstractly as triangles, but with attribute 1 having been modified to imitate the client application of User 2 having the square-shaped attributes. An example of how these users may be grouped together will now be discussed with respect to  FIG. 7 . 
       FIG. 7  is a block diagram that illustrates an exemplary representation of grouping user fingerprints having similar attributes. In this example, users 1 and 3 are grouped together into ‘Group 1’ due to their having similar triangle-shaped attributes, and users 2 and 4 are in ‘Group 2’ based on their sharing similar squared-shaped attributes. However, ‘User X’, who has modified attribute 1, will now belong to both groups. In particular, because attributes 2 through N shown for User X are similar to the triangular attributes of Group 1, User X will be included in Group 1. However, User X will also be included in Group 2 based on having the modified attribute 1 which is similar to the square attributes of Group 2. The inclusion of User X in both groups is indicative of malicious intent, and appropriate security precautions can then be taken to safeguard the web service from any malicious actions that may be attempted by User X. 
     The security techniques described above provide for unsupervised classification of web traffic users into groups to facilitate identification of malicious users. By grouping different users based on the similarity of their web traffic attributes, the system can passively identify attempts to access the web service with attack tools masquerading as real browsers, thereby ensuring that a web service is only accessed by legitimate web browsers. Any suspicious activity that deviates from the typical behavior of a legitimate web browser will be exposed through the inclusion of that client in multiple groups and can be flagged and blocked, thereby providing improved defenses against malicious users. 
     Now referring back to  FIG. 1 , client computing systems  101 - 103  each individually comprise a processing system and communication transceiver. Computing systems  101 - 103  may also include other components such as a user interface, data storage system, and power supply. Computing systems  101 - 103  may reside in a single device or may be distributed across multiple devices. Examples of computing systems  101 - 103  include mobile computing devices, such as cell phones, tablet computers, laptop computers, notebook computers, and gaming devices, as well as any other type of mobile computing devices and any combination or variation thereof. Examples of computing systems  101 - 103  also include desktop computers, server computers, and virtual machines, as well as any other type of computing system, variation, or combination thereof. 
     Communication network  120  could comprise multiple network elements such as routers, gateways, telecommunication switches, servers, processing systems, or other communication equipment and systems for providing communication and data services. In some examples, communication network  120  could comprise wireless communication nodes, telephony switches, Internet routers, network gateways, computer systems, communication links, or some other type of communication equipment, including combinations thereof. Communication network  120  may also comprise optical networks, asynchronous transfer mode (ATM) networks, packet networks, local area networks (LAN), metropolitan area networks (MAN), wide area networks (WAN), or other network topologies, equipment, or systems—including combinations thereof. Communication network  120  may be configured to communicate over metallic, wireless, or optical links. Communication network  120  may be configured to use time-division multiplexing (TDM), Internet Protocol (IP), Ethernet, optical networking, wireless protocols, communication signaling, or some other communication format, including combinations thereof. In some examples, communication network  120  includes further access nodes and associated equipment for providing communication services to several computer systems across a large geographic region. 
     Web server computing system  130  may be representative of any computing apparatus, system, or systems on which the techniques disclosed herein or variations thereof may be suitably implemented. Web server computing system  130  comprises a processing system and communication transceiver. Web server computing system  130  may also include other components such as a router, server, data storage system, and power supply. Web server computing system  130  may reside in a single device or may be distributed across multiple devices. Web server computing system  130  may be a discrete system or may be integrated within other systems, including other systems within communication system  100 . Some examples of web server computing system  130  include desktop computers, server computers, cloud computing platforms, and virtual machines, as well as any other type of computing system, variation, or combination thereof. In some examples, web server computing system  130  could comprise a network switch, router, switching system, packet gateway, network gateway system, Internet access node, application server, database system, service node, firewall, or some other communication system, including combinations thereof. 
     Communication links  121 - 123  and  131  use metal, air, space, optical fiber such as glass or plastic, or some other material as the transport medium—including combinations thereof. Communication links  121 - 123  and  131  could use various communication protocols, such as TDM, IP, Ethernet, telephony, optical networking, hybrid fiber coax (HFC), communication signaling, wireless protocols, or some other communication format, including combinations thereof. Communication links  121 - 123  and  131  could be direct links or may include intermediate networks, systems, or devices. 
     Referring now to  FIG. 8 , a block diagram that illustrates computing system  800  in an exemplary implementation is shown. Computing system  800  provides an example of web server  130 , although server  130  could use alternative configurations. Computing system  800  could also provide an example of client computing systems  101 - 103 , although systems  101 - 103  could use alternative configurations. Computing system  800  includes processing system  801 , storage system  803 , software  805 , communication interface  807 , and user interface  809 . Software  805  includes application  806  which itself includes classification process  200 . Classification process  200  may optionally be implemented separately from application  806 . 
     Computing system  800  may be representative of any computing apparatus, system, or systems on which application  806  and classification process  200  or variations thereof may be suitably implemented. Examples of computing system  800  include mobile computing devices, such as cell phones, tablet computers, laptop computers, notebook computers, and gaming devices, as well as any other type of mobile computing devices and any combination or variation thereof. Note that the features and functionality of computing system  800  may apply as well to desktop computers, server computers, and virtual machines, as well as any other type of computing system, variation, or combination thereof. 
     Computing system  800  includes processing system  801 , storage system  803 , software  805 , communication interface  807 , and user interface  809 . Processing system  801  is operatively coupled with storage system  803 , communication interface  807 , and user interface  809 . Processing system  801  loads and executes software  805  from storage system  803 . When executed by computing system  800  in general, and processing system  801  in particular, software  805  directs computing system  800  to operate as described herein for web server  130  for execution of classification process  200  or variations thereof. Computing system  800  may optionally include additional devices, features, or functionality not discussed herein for purposes of brevity. 
     Referring still to  FIG. 8 , processing system  801  may comprise a microprocessor and other circuitry that retrieves and executes software  805  from storage system  803 . Processing system  801  may be implemented within a single processing device but may also be distributed across multiple processing devices or sub-systems that cooperate in executing program instructions. Examples of processing system  801  include general purpose central processing units, application specific processors, and logic devices, as well as any other type of processing device, combinations, or variations thereof. 
     Storage system  803  may comprise any computer-readable media or storage media readable by processing system  801  and capable of storing software  805 . Storage system  803  may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. Storage system  803  may be implemented as a single storage device but may also be implemented across multiple storage devices or sub-systems co-located or distributed relative to each other. Storage system  803  may comprise additional elements, such as a controller, capable of communicating with processing system  801 . Examples of storage media include random-access memory, read-only memory, magnetic disks, optical disks, flash memory, virtual memory and non-virtual memory, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and that may be accessed by an instruction execution system, as well as any combination or variation thereof, or any other type of storage media. In no case is the storage media a propagated signal. 
     In operation, processing system  801  loads and executes portions of software  805 , such as application  806  and/or classification process  200 , to facilitate web traffic classification. Software  805  may be implemented in program instructions and among other functions may, when executed by computing system  800  in general or processing system  801  in particular, direct computing system  800  or processing system  801  to monitor web traffic between a plurality of clients and at least one web server, and analyze the web traffic to determine attribute data points associated with each individual client of the plurality of clients. Software  805  further directs computing system  800  or processing system  801  to compare the attribute data points associated with each individual client to define a plurality of client groups based on similarities in the attribute data points among each individual client. Software  805  may also direct computing system  800  or processing system  801  to identify a client of the plurality of clients as malicious when the client is included in more than one of the client groups. 
     Software  805  may include additional processes, programs, or components, such as operating system software or other application software. Examples of operating systems include Windows®, iOS®, and Android®, as well as any other suitable operating system. Software  805  may also comprise firmware or some other form of machine-readable processing instructions executable by processing system  801 . 
     In general, software  805  may, when loaded into processing system  801  and executed, transform computing system  800  overall from a general-purpose computing system into a special-purpose computing system customized to facilitate web traffic classification as described herein for each implementation. For example, encoding software  805  on storage system  803  may transform the physical structure of storage system  803 . The specific transformation of the physical structure may depend on various factors in different implementations of this description. Examples of such factors may include, but are not limited to the technology used to implement the storage media of storage system  803  and whether the computer-storage media are characterized as primary or secondary storage. 
     In some examples, if the computer-storage media are implemented as semiconductor-based memory, software  805  may transform the physical state of the semiconductor memory when the program is encoded therein. For example, software  805  may transform the state of transistors, capacitors, or other discrete circuit elements constituting the semiconductor memory. A similar transformation may occur with respect to magnetic or optical media. Other transformations of physical media are possible without departing from the scope of the present description, with the foregoing examples provided only to facilitate this discussion. 
     It should be understood that computing system  800  is generally intended to represent a computing system with which software  805  is deployed and executed in order to implement application  806 , classification process  200 , and variations thereof. However, computing system  800  may also represent any computing system on which software  805  may be staged and from where software  805  may be distributed, transported, downloaded, or otherwise provided to yet another computing system for deployment and execution, or yet additional distribution. For example, computing system  800  could be configured to deploy software  805  over the internet to one or more client computing systems for execution thereon, such as in a cloud-based deployment scenario. 
     Communication interface  807  may include communication connections and devices that allow for communication between computing system  800  and other computing systems (not shown) or services, over a communication network  811  or collection of networks. In some implementations, communication interface  807  receives dynamic data  821  over communication network  811 . Examples of connections and devices that together allow for inter-system communication may include network interface cards, antennas, power amplifiers, RF circuitry, transceivers, and other communication circuitry. The aforementioned network, connections, and devices are well known and need not be discussed at length here. 
     User interface  809  may include a voice input device, a touch input device for receiving a gesture from a user, a motion input device for detecting non-touch gestures and other motions by a user, and other comparable input devices and associated processing elements capable of receiving user input from a user. Output devices such as a display, speakers, haptic devices, and other types of output devices may also be included in user interface  809 . In some examples, user interface  809  could include a touch screen capable of displaying a graphical user interface that also accepts user inputs via touches on its surface. The aforementioned user input devices are well known in the art and need not be discussed at length here. User interface  809  may also include associated user interface software executable by processing system  801  in support of the various user input and output devices discussed above. Separately or in conjunction with each other and other hardware and software elements, the user interface software and devices may provide a graphical user interface, a natural user interface, or any other kind of user interface. User interface  809  may be omitted in some implementations. 
     The functional block diagrams, operational sequences, and flow diagrams provided in the Figures are representative of exemplary architectures, environments, and methodologies for performing novel aspects of the disclosure. While, for purposes of simplicity of explanation, methods included herein may be in the form of a functional diagram, operational sequence, or flow diagram, and may be described as a series of acts, it is to be understood and appreciated that the methods are not limited by the order of acts, as some acts may, in accordance therewith, occur in a different order and/or concurrently with other acts from that shown and described herein. For example, those skilled in the art will understand and appreciate that a method could alternatively be represented as a series of interrelated states or events, such as in a state diagram. Moreover, not all acts illustrated in a methodology may be required for a novel implementation. 
     The above description and associated figures teach the best mode of the invention. The following claims specify the scope of the invention. Note that some aspects of the best mode may not fall within the scope of the invention as specified by the claims. Those skilled in the art will appreciate that the features described above can be combined in various ways to form multiple variations of the invention. As a result, the invention is not limited to the specific embodiments described above, but only by the following claims and their equivalents.