Patent Publication Number: US-10785318-B2

Title: Classification of website sessions using one-class labeling techniques

Description:
FIELD OF THE INVENTION 
     This disclosure relates generally to the field of artificial intelligence, and more specifically relates to classifying unlabeled network sessions with a network application. 
     BACKGROUND 
     Network traffic is generated by human and by non-human visitors to a network application. A network application may handle interactions that originate from a software construct, such as a “bot,” that is programmed to interact with the network application. A legitimate bot may interact with the network application in helpful ways, such as categorizing content for use in search results. A malicious bot may interact with the network application in harmful ways, such as generating repetitive traffic with the intent of causing failure of the network application, downloading content for illegal re-publication, or other malicious uses. In addition, interactions performed by a bot (including legitimate or malicious bots) may be included in analytics that are intended to represent user (e.g., human) interactions with the network application. Analytics including bot activity may misrepresent activities with the network application, and decisions that are based on such analytics may result in inappropriate business or operation decisions. Owners and operators of network applications may wish to prevent or identify traffic that is generated by bots. 
     An existing technique for identifying bot traffic is to include a robot exclusion protocol (e.g., “robots.txt”) in a network application. The robot exclusion protocol may indicate network application areas or interaction types that bots are prohibited from accessing on the network application. Legitimate bots cooperate with the indicated prohibitions. Malicious bots may ignore the prohibitions, and generate interactions that do not comply with the robot exclusion protocol of the network application. In other existing technique for identifying bot traffic is to require identification information for an incoming request to the network application. Legitimate bots may self-identify as bots, such as by providing appropriate information to the network application. Interactions by self-identified bots may be filtered or otherwise handled based on the provided information. However, malicious bots may provide identification information that misrepresents the bot, such as by falsely indicating that the bot is a session associated with a human. An additional technique to identify bot traffic is applying a Turing test (e.g., a “captcha”) to incoming requests, such as by requiring a requestor to identify words in an image. However, human visitors may be frustrated by the interruption of their activities, while a malicious bot may circumvent the test. 
     In addition, existing techniques may attempt to classify bot traffic by modeling behavioral patterns of humans or bots. Behavior modeling techniques often rely on domain knowledge describing multiple classes (e.g., human and non-human network traffic) to label the classes, and may not accurately classify network sessions if the labeling information is limited, or if labeling information is only available for one of the classes. In addition, behavioral modeling techniques may require manual interpretation of the domain knowledge to create multiple rules describing behavior of the classes. 
     It is desirable to develop techniques to accurately classify network traffic as human-generated or as non-human. In addition, it is desirable to accurately classify network traffic using limited labeling information about only one of the classes. 
     SUMMARY 
     According to certain embodiments, a session identification system receives a set of unlabeled sessions, such as sessions with a network application. The session identification system determines a labeled subset of the sessions, based on the presence of a session feature that is associated with human traffic. For example, a session feature associated with human-generated network traffic includes purchases performed on a website. Based on the labeled subset, the session identification system identifies an additional session feature that is included in the labeled subset of sessions. For example, the additional session feature includes a user identification that is used in sessions that include purchases on the website. 
     The session identification system determines a derived subset of the unlabeled sessions, based on the presence of the additional session feature in the derived subset. The set of unlabeled sessions, the labeled subset, and the derived subset are provided to trained classifiers. A first classifier is trained to generate a probability, based on the labeled subset, that an unlabeled session has the session feature associated with human network traffic. A second classifier is trained to generate a probability, based on the derived subset, that the unlabeled session has the session feature associated with human network traffic, given that the unlabeled session has the additional session feature. Based on a combination of the probabilities, the session identification system determines a probability that the unlabeled session is generated by a human. 
     These illustrative embodiments are mentioned not to limit or define the disclosure, but to provide examples to aid understanding thereof. Additional embodiments are discussed in the Detailed Description, and further description is provided there. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Features, embodiments, and advantages of the present disclosure are better understood when the following Detailed Description is read with reference to the accompanying drawings, where: 
         FIG. 1  is a block diagram depicting an example of a system for classifying network traffic as human-generated or non-human, according to certain embodiments; 
         FIG. 2  is a diagram depicting an example of a system in which unlabeled network traffic is classified based on outputs from multiple classifiers, according to certain embodiments; 
         FIG. 3  is a flow chart depicting an example of a process for classifying unlabeled network sessions, according to certain embodiments; 
         FIG. 4  is a diagram indicating relationships between one or more sets of network sessions, according to certain embodiments; and 
         FIG. 5  is a block diagram depicting an example of a computing system for implementing a session identification system, according to certain embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     As discussed above, prior techniques for identifying non-human network traffic do not accurately identify network sessions as generated by humans or non-humans. In addition, prior techniques for classifying sessions with a network application do not accurately classify the network sessions if labeling information is only available for one of the classes. Certain embodiments described herein provide for accurate classification of human and non-human sessions based on labeling information describing only one of the classes. For example, a subset of multiple unlabeled network sessions are labeled as belonging to one class, based on a single identified session feature. In addition, the remainder of the multiple unlabeled network sessions are classified based on the information describing the one labeled class. 
     The following examples are provided to introduce certain embodiments of the present disclosure. In this example, a session identification system receives a set of sessions, such as from a network application server that hosts a website. The sessions in the received set are unlabeled (e.g., are not identified as a session generated by a human or a non-human). In some cases, each session in the received set is associated with one or more session features that describe the respective session, such as features describing a computing device requesting the session or actions performed during the session. Additionally or alternatively, one or more of the session features is associated with network traffic that is generated by humans. For example, a “purchase feature,” including interactions indicating that a purchase was made during the respective session, is a session feature associated with human-generated network traffic. 
     Based on the presence of the session feature associated with human-generated network traffic, the session identification system identifies a labeled subset of the unlabeled sessions. Additionally or alternatively, the session identification system determines one or more additional session features that are associated with the labeled subset of sessions. For example, the session identification system determines a user identification (e.g., a login name or password) that is associated with a respective session included in the labeled subset. Based on the additional session feature, the session identification system determines a derived subset of the unlabeled sessions. For example, the derived subset includes sessions generated by a user login associated with at least one purchase session. 
     In the session identification system, the unlabeled, labeled, and derived sessions are provided to multiple classifiers. The classifiers are each trained to generate, based on analysis of features associated with a set or subset of sessions, a probability describing each respective session in the set of unlabeled sessions. A first classifier is trained to generate a probability, based on analysis of features associated with sessions in the labeled subset, that an unlabeled session has the session feature associated with human network traffic. A second classifier is trained to generate a probability, based on analysis of features associated with sessions in the derived subset, that the unlabeled session has the session feature associated with human network traffic, given that the unlabeled session has the additional session feature. Based on a combination of the probabilities, the session identification system determines a probability that the unlabeled session is generated by a human. The session identification system provides the determined probability for the unlabeled session, such as to the network application server hosting the website. 
     As used herein, the term “session” refers to a group of interactions between computing systems, such as between client and a server. In some cases, the server is a computing system that is configured to receive requests from other computing systems. Additionally or alternatively, the client is a computing system that provides a request to establish a session with the server. For example, a given computing system receives session requests as a server and also requests sessions as a client. In some embodiments, the session is conducted via one or more of a computer network or computer communication protocols. Interactions in a session can include one or more of a request to establish a connection, an authentication, a transmission of data, a request to verify another interaction, or any other suitable interaction between computing systems. Interactions that are included in a session are determined, for example, based on suitable session features, such as a time duration of the session, a client computing system&#39;s identification information, or a specific type of interaction (e.g., a “keepalive” request). In some cases, a computing system (including a client or a server) has multiple sessions, including multiple sessions simultaneously or multiple sessions with a particular additional computing system. For example, a client establishes multiple sessions with a server, such that each of the multiple sessions is distinguished based on an application, on the client, that established the session, or any other suitable session feature. 
     As used herein, the term “session feature” refers to an attribute describing an aspect of a particular session. Session features include, without limitation, one or more of attributes of a computing system associated with the session (including the client or the server), attributes of a software application associated with the session, attributes of interactions included in the session, attributes describing how the session is conducted, or any other suitable attribute. For example, attributes include one or more of an internet protocol address (e.g., “IP address”) associated with the session, a computing system type (e.g., a personal computer, a virtual device, a smartphone), a browser type, user authentication information, one or more interactions performed during the session, an elapsed time since a previous interaction, a type of interaction (e.g., a user conversion action, a “keepalive” request), a duration of the session, an application (e.g., on the client) that requests the session, a communication protocol that is used, a geographical location (e.g., of the client or server), or any other suitable attribute. In some cases, a session feature is based on a combination of two or more additional session features. For example, an identification attribute is based on a user login combined with an IP address associated with the user login. 
     As used herein, the terms “robot” and “bot” refer to a software construct that is capable of interacting with a network application. In some cases, a bot is a software program that is programmed to request a network session with the network application. Additionally or alternatively, the bot is programmed to interact with the network application without direction from a human user of the bot. For example, a bot that operates on a client computing system contacts the network application, requests a session, interacts with the network application, and closes the session without receiving input from a human using the client computing system. In some cases, the interactions of the bot include interactions with other users of the network application. 
     A bot that is programmed to interact with the network application in helpful ways is referred to herein as a legitimate bot. For example, a legitimate bot may interact with a network application by “crawling” a website to include the website in search engine results, or by providing information about the network application to other users (e.g., a customer service “chatbot”). A bot that is programmed to interact with a network application in harmful ways is referred to herein as a malicious bot. For example, a malicious bot may interact with a network application by downloading (e.g., “scraping”) proprietary content, including creative content or business content (e.g., pricing information), from the application for illegal use. Additionally or alternatively, a malicious bot may generate repeated requests to the network application, such as to reduce performance of the application (e.g., denial of service attacks) or to generate false information about interactions with the application (e.g., fraudulent advertising impressions). In some cases, a malicious bot interacts with the network application in ways that are harmful to other users of the application, such as by encouraging users of a chat service to post to personal login information. In some cases, a bot, including a legitimate or a malicious bot, can reduce accuracy of information gathered by the network application. For example, if the bot does not self-identify as a bot (e.g., via a user-agent string), interactions by the bot can be inappropriately included in performance analytics for the network application. 
     As used herein, the term “classifier” refers to a software module capable of determining a classification of an input, such as an unlabeled session with a network application. In some cases, the classifier is included in a neural network, such as an algorithm performed by a neural network. Examples of a neural network including a classifier include, without limitation, a recursive neural tensor network, or any other suitable type of neural network. Additionally or alternatively, the classifier is capable of improving its determined classifications based on previous classifications or additional inputs. A classifier is trained, for example, using a body of data that is provided, during one or more training phases, to the classifier or to a neural network including the classifier. In some cases, training of a classifier is accomplished by supervised training, or by a combination of supervised and unsupervised training. In some cases, the training data includes sessions that are generated by a known source (e.g., from a known human or known non-human requestor). 
     As used herein, the term “label” refers to an identification of whether an input to a classifier belongs to a class. For example, a particular session with a network application has a label identifying whether the session belongs to a class of human-generated network sessions. The label is determined, for example, based on one or more features of the particular session. For example, a session having a feature indicating that a purchase was performed on a website has a label identifying that the session is associated with human-generated network traffic. In some cases, a classifier determines a label for an unlabeled session. Additionally or alternatively, a classifier analyzes a labeled session and determines a label for an unlabeled session based on the analysis. 
     As used herein, the term “network traffic” refers to communications between two or more computing systems via a computer network. For example, network traffic includes a session between a client and a server, or a request to establish a session, or any other suitable communication. In some cases, network traffic includes human-generated traffic, such as traffic originating from a computing system under the direction of a human being. Additionally or alternatively, network traffic includes non-human traffic, such as traffic originating from a computing system without direction from a human being. Network traffic includes, for example, communications relating to a network application, such as websites, games, applications operating on a personal computing system (e.g., “apps”), single-purpose applications, multi-purpose applications, or any other suitable network application. Additionally or alternatively, network traffic includes communications originating from one or more computing systems, such as a personal computer (e.g., a desktop, a laptop), a server device, a mobile computing device (e.g., a tablet computer, a smartphone), a wearable computing device, an Internet-of-Things device, a virtual device (e.g., a “virtual machine,” a cloud-based computing system), or any other suitable computing system. 
     Referring now to the drawings,  FIG. 1  is a diagram depicting a system  100  in which network traffic is classified as human-generated or non-human. In the system  100 , a network application server  130  participates in one or more network sessions  125  with one or more client computing systems  120 . In some cases, one or more of the network sessions  125  are unlabeled (e.g., as human-generated or non-human). The network application server  130  provides a network application  131 , such as a website, that is accessible via one or more computing networks. A client, such as a client included in the client computing systems  120 , requests access to the network application  131 , such as by providing to the network application server  130  a request to establish a session. In some cases, the network application server  130  responds to the request by establishing a session with the requesting client, such as a session included in the sessions  125 . 
     The system  100  includes a session identification system  110 . The session identification system  110  includes, for example, one or more classifiers, such as classifier  111 . In some cases, the classifier  111  is trained to generate a probability describing an input session. For example, the classifier  111  generates a probability based on session features associated with the input session. In some cases, an additional classifier generates an additional probability describing the input session. 
     In the system  100 , the network application server  130  provides session data  133  to the session identification system  110 . The session data  133  describes one or more unlabeled network sessions. The session data  133  includes, for example, session features that are associated respectively with one or more of the sessions  125 . In some cases, the session data  133  includes one or more session features that are associated with human-generated network traffic. For example, the session data  133  includes session features indicating purchases made via the network application  131 . Additionally or alternatively, a subset of the sessions  125  are associated with the session features associated with human-generated network traffic. 
     In an embodiment, the classifier  111  receives the session data  133 . Based on analysis of the session data  133 , the classifier  111  generates one or more probabilities respectively describing the sessions  125 . A particular probability describes, for example, a likelihood that a respective particular session has the feature associated with human-generated network traffic. Additionally or alternatively, the session identification system  110  determines one or more session classifications  135 , which classify respectively each session  125  that is associated with the session data  133 . In some cases, the session classification  135  indicates whether a given session of sessions  125  is human-generated or non-human network traffic. Additionally or alternatively, the session classification  135  includes a label indicating a class of a previously unlabeled session of sessions  125 . 
     The network application server  130  receives the session classification  135 . In some cases, the network application server  130  performs an operation related to one or more of the sessions  125 , based on the information included in the session classification  135 . For example, responsive to determining that a particular one of sessions  125  is non-human, the network application server  130  terminates the non-human session, or filters (e.g., from analytics) interactions performed in the non-human session, or any other suitable action. Termination of a non-human session, for example, prevents potentially harmful interactions by a malicious bot. Filtering interactions from a non-human session improves, for example, accuracy of analytics describing interactions with a network application. Additionally or alternatively, responsive to determining that another one of sessions  125  is human-generated, the network application server  130  prioritizes interactions included in the human-generated session, or allocates additional computing or networking resources to the human-generated session, or any other suitable action. Prioritizing a human-generated session improves, for example, responsiveness of the network application to a user interacting with the network application (e.g., the user may have a better user experience with the application). 
     In some cases, the session identification system  110  provides a classification of a session in real time or substantially real time. For example, the session identification system  110  provides session classification  135  to network application server  130  while sessions  125  are extant, or within a relatively short period of time (e.g., 1-60 seconds) after one or more of sessions  125  are terminated. Additionally or alternatively, the session identification system  110  provides a classification of a historical session, such as based on data that is included in analytics data. In system  100 , an analytics system  150  includes information describing one or more historical sessions  151 . The historical session includes, for example, terminated sessions with a network application, including, but not limited to, the network application  131 . In some cases, the analytics system  150  is associated with an owner or operator of network application server  130 . Additionally or alternatively, the analytics system  150  is associated with an owner or operator of session identification system  110 . 
     In an embodiment, the analytics system  150  provides historical session data  153  to the session identification system  110 . In some cases, the historical session data  153  is provided in response to an analysis request, such as related to a network application associated with the historical session data  153 . Additionally or alternatively, the historical session data  153  is provided in response to a training request, such as related to the session identification system  110 . For example, the classifier  111  is trained or retrained based on the historical session data  153 . 
     In some cases, the historical session data  153  includes one or more session features that are associated with human-generated network traffic, such as session features indicating purchases made via a network application. The classifier  111  receives the historical session data  153 . Based on analysis of the historical session data  153 , the classifier  111  generates one or more probabilities describing, for example, a likelihood that a respective historical session  151  has the feature associated with human-generated network traffic. Additionally or alternatively, the session identification system  110  determines one or more historical session classifications  155 . In some cases, the historical session classification  155  indicates whether a given historical session  151  is human-generated or non-human network traffic. The analytics system  150  receives the historical session classification  155 . In some cases, the analytics system  150  performs an operation related to one or more of the historical sessions  151 , based on the historical session classification  155 . For example, responsive to determining that a particular one of historical sessions  151  is non-human, the analytics system  150  filters (e.g., from an analysis request) interactions performed in the non-human session, or any other suitable action. 
       FIG. 2  is a diagram depicting a system  200  in which network traffic, including unlabeled network traffic, is classified as human-generated or non-human based on outputs from multiple classifiers. In system  200 , a session identification system  210  includes a first classifier  211  and a second classifier  215 . The session identification system  210  determines a classification of network traffic based in part on information provided by the classifiers  211  and  215 . 
     In system  200 , the session identification system  210  receives session data  233 . The session data  233  includes information describing multiple network sessions, such as current sessions with a network application or historical sessions, as described in regards to  FIG. 1 . In some cases, each of the network sessions represented by the session data  233  is a human-generated session or a non-human session. Additionally or alternatively, the network sessions represented by the session data  233  are unlabeled network sessions (e.g., it is unknown if a particular session is human-generated or non-human). The session data  233  includes one or more session features  234 . In some cases, each network session represented by the session data  233  is associated with one or more of the session features  234 . For example, a particular network session represented by the data  233  is associated with a vector of session features that are included in the session features  234 . 
     In an embodiment, the session identification system  210  identifies one or more of the session features  234  that are associated with human-generated network sessions. In some cases, the identified session feature  241  has an attribute that results from a human interaction with a network application. For example, the identified session feature  241  indicates that a product or service was purchased via the network application. In some cases, the identified session feature  241  indicates that a threshold of purchases were made (e.g., a monetary threshold, a quantity). Additionally or alternatively, the identified session feature  241  includes a combination of attributes that result from human interactions with the network application. For example, the identified session feature  241  indicates that a purchase was made via the network application, and that the purchase was associated with a location (e.g., a product shipped to an address). In some cases, the identified session feature  241  is determined based on characteristics of the network application associated with the network sessions. For example, a network application that receives donations to a nonprofit organization may have an identified session feature  241  indicating a contribution to the organization. Additionally or alternatively, a network application for an online gaming environment may have an identified session feature  241  indicating a purchase of in-game content, or interactions with representatives of the online gaming environment (e.g., contacting customer support). 
     In system  200 , the session identification system  210  determines one or more subsets of network sessions based on the session data  233 . In some cases, the session identification system  210  determines that one or more network sessions represented by the session data  233  has the identified session feature  241  associated with human-generated network sessions. Additionally or alternatively, the session identification system  210  determines that the network sessions including the identified session feature  241  are included in a labeled subset  240  of the network sessions. For example, responsive to determining that a particular network session is associated with the identified session feature  241  indicating a purchase, the particular network session is included in the labeled subset  240 . In some cases, each network session included in the labeled subset is labeled (e.g., by the session identification system  210 ) as belonging to a class of human-generated network traffic. Additionally or alternatively, the labeled subset  240  includes portions of the session data  233  associated with the sessions having the identified session feature  241 . 
     Additionally or alternatively, the session identification system  210  determines that one or more additional network sessions represented by the session data  233  are included in a derived subset  245  of the network sessions. In some cases, the additional network sessions are associated with an additional session feature  246  that is associated with one or more of the network sessions included in the labeled subset  240 . For example, a network session included in the labeled subset  240  is associated with the identified session feature  241  and also with one or more features identifying the originator of the session (e.g., a user that initiated a purchase). The additional session feature  246  includes information indicating the session originator, such as one or more of an IP address, a user login, or any other suitable information. The derived subset  245  includes network sessions having the additional session feature  246 , such as sessions generated by the originator of one or more of the sessions in the labeled subset  240 . In some cases, the derived subset  245  includes sessions having each of the additional session feature  246  and the identified session feature  241 . Additionally or alternatively, the derived subset  245  includes sessions having the additional session feature  246  but not the identified session feature  241 . For example, the derived subset  245  includes sessions generated by a purchaser, but which do not include a purchase. In some cases, the derived subset  245  includes portions of the session data  233  associated with the sessions having the additional session feature  246 . 
     In an embodiment, the classifier  211  receives the labeled subset  240  and the session data  233 . Based on the information included in the labeled subset  240  and the session data  233 , the classifier  211  generates a probability  212 . The probability  212  indicates a probability that a particular unlabeled session represented by the session data  233  has the identified session feature  241 . 
     Additionally or alternatively, the classifier  215  receives the derived subset  245  and the session data  233 . Based on the information included in the derived subset  245  and the session data  233 , the classifier  215  generates a probability  216 . The probability  216  indicates a probability that the particular unlabeled session represented by the session data  233  has the identified session feature  241  given that the particular unlabeled session has the additional session feature  246 . 
     In the system  200 , the session identification system  210  combines the probability  212  and the probability  216 . For example, the session identification system  210  generates a combination result  219  based on one or more mathematical operations based on the probabilities  212  and  216 . In some cases, the combination result  219  indicates a probability that the particular unlabeled session represented by the session data  233  is a human-generated network session. For example, the combination result  219  indicates a probability that the particular unlabeled session was originated by a human user. Additionally or alternatively, the combination result  219  includes a probability that the particular unlabeled session was originated by a non-human (e.g., a bot). Based on the combination result  219 , the session identification system  210  generates a session classification  235 . In some cases, the session classification  235  includes a label generated for the particular unlabeled session. 
     In an embodiment, the classifiers  211  and  215  generate a respective probability for each of the unlabeled network sessions represented by the session data  233 . Additionally or alternatively, the session identification system  210  generates one or more of a respective combination result and a respective classification for each of the unlabeled network sessions. In some cases, the session classification  235  includes label information for each of the unlabeled network sessions represented by session data  233 . Additionally or alternatively, the session classification  235  is provided to another computing system, such as an analytical system or a network application server as described in regards to  FIG. 1 . 
       FIG. 3  is a flow chart depicting an example of a process  300  for classifying unlabeled network sessions. In some embodiments, such as described in regards to  FIGS. 1-2 , a computing device executing a session identification system implements operations described in  FIG. 3 , by executing suitable program code. For illustrative purposes, the process  300  is described with reference to the examples depicted in  FIGS. 1-2 . Other implementations, however, are possible. 
     At block  310 , the process  300  involves receiving one or more unlabeled network sessions. In some cases, a session identification system receives data describing the unlabeled network sessions. For example, the session identification system  110  receives session data  133  describing an unlabeled network session  125  between the client computing system  120  and the network application server  130 , such as described in regards to  FIG. 1 . In some cases, the data describing the unlabeled network sessions includes session features, or data describing session features, that are associated with respective ones of the unlabeled network sessions. 
     At block  320 , the process  300  involves identifying a first session feature that is associated with human-generated network sessions. In some cases, the first session feature is identified based on data describing the received unlabeled network sessions. For example, an identified session feature is based on features included in session data  133 . Additionally or alternatively, the first session feature is based on information indicating a type of feature associated with human-generated network sessions. For example, the session identification system  110  receives information indicating that network sessions including a purchase are associated with human-generated network traffic. The information is received, for example, via a user interface associated with the session identification system  110 , the network application server  130 , or another suitable computing system. Based on the received information, the session application system  110  identifies that “purchase” session features are associated with human-generated network sessions. 
     At block  330 , the process  300  involves determining a first subset of the unlabeled network sessions that have the first feature associated with human-generated sessions. In some cases, the sessions included in the first subset are labeled as human-generated sessions. For example, the session identification system  210  determines a labeled subset  240  that includes network sessions having the identified session feature  241 . In some cases, the first subset includes sessions that included a purchase. 
     At block  340 , the process  300  involves determining a second session feature that is associated with sessions included in the first subset. In some cases, the second session feature identifies an originator of a session having the first session feature. For example, the session identification system  210  determines an additional session feature  246  that is associated with sessions included in the labeled subset  240 . 
     At block  350 , the process  300  involves determining a second subset of the unlabeled network sessions that have the second feature. For example, the session identification system  210  determines the derived subset  245  that includes sessions having the additional session feature  246 . In some cases, the second subset includes network sessions (including sessions that do not include a purchase) from an originator that has made a purchase in another session. 
     At block  360 , the process  300  involves providing the unlabeled network sessions and the first subset of sessions to a first classifier. For example, the classifier  211  receives session data  233  describing unlabeled network sessions and the labeled subset  240 . At block  365 , the process  300  involves generating a first probability that a particular unlabeled session has the first session feature. For example, the classifier  211  generates the probability  212 , indicating a probability that a particular unlabeled session represented by the session data  233  has the identified session feature  241 . 
     At block  370 , the process  300  involves providing the unlabeled network sessions and the second subset of sessions to a second classifier. For example, the classifier  215  receives session data  233  describing unlabeled network sessions and the derived subset  245 . At block  375 , the process  300  involves generating a second probability that a particular unlabeled session, which has the second session feature, has the first session feature. For example, the classifier  215  generates the probability  216 , indicating a probability that the particular unlabeled session represented by the session data  233  has the identified session feature  241  given that the particular unlabeled session has the additional session feature  246 . 
     At block  380 , the process  300  involves determining a third probability that a particular unlabeled session is a human-generated network session. In some cases, the third probability is determined based on the first and second probabilities described in regards to blocks  365  and  375 . For example, the session identification system  210  generates a combination result  219  based on the probabilities  212  and  216 . Additionally or alternatively, the combination result  219  indicates a probability that a particular unlabeled session represented by the session data  233  is a human-generated network session. 
     Determining Probabilities 
     In some embodiments, a session identification system, such as described in regards to  FIGS. 1 and 2 , classifies network sessions as human-generated or as non-human. In some cases, the classification is based on one or more probabilities describing a given network session in relation to a set of sessions.  FIG. 4  depicts a diagram  400  (e.g., a Venn diagram) indicating some relationships between one or more sets of network sessions. The diagram  400  includes a set of unlabeled network sessions  401 . The unlabeled sessions  401  are network sessions between client computing systems and one or more of a network application server or a network application. In some cases, the unlabeled sessions  401  are network sessions with a particular application or server. Additionally or alternatively, the unlabeled sessions  401  are with multiple applications or servers. Furthermore, the unlabeled sessions  401  are associated with one or more of historical network sessions or current network sessions. 
     In an embodiment, the set of unlabeled sessions  401  includes sessions requested by multiple client computing systems, such as the client computing systems  120  as described in regards to  FIG. 1 . The client computing systems request one or more sessions under the direction of human users or non-human users (e.g., a bot). The sessions include, for example, one or more features that are associated with the originators of respective sessions, such as features indicating a computing device, a software application (e.g., a browser, a background process), or a time duration between interactions with the network application. 
     In some cases, the total number of sessions included in the set of unlabeled sessions  401  is known. Additionally or alternatively, each of the sessions included in the set of unlabeled sessions  401  is associated with one or more session features. For convenience, and not by way of limitation, the session features for a given session are represented by the feature vector X. For example, a first session in set  401  has a feature vector X 1 , a second session has the feature vector X 2 , and an nth session has the feature vector X n . 
     In the set of unlabeled sessions  401 , the feature vector X i  for a particular session i includes indications of whether or not the session i has a particular session feature. For example, a respective feature vector X includes an origination feature indicating that the respective session originated from a human or from a non-human. For convenience, and not by way of limitation, the origination feature is represented by y, and has a value of 1 for a session originating from a human and a value of 0 for a session originating from a non-human, as indicated by Equation 1.
 
 y= 1,human session
 
 y= 0,non-human session  Eq. 1
 
     The set of unlabeled sessions  401  includes a subset of sessions originating from humans and a subset of sessions originating from non-humans. For example, the set of human-generated sessions  402  is included in the set of unlabeled sessions  401 . In some cases, the number of sessions included in the set of human sessions  402  is unknown. The session i in the set of unlabeled sessions  401  has a probability of being included in the set of human sessions  402 . Additionally or alternatively, the feature vector X i  for the session i has a probability of having the origination feature y with a value of 1, as indicated by Equation 2. In some cases, the probability that the given session in the set of unlabeled sessions  401  is also included in the set of human sessions  402  is provided by Equation 2.
 
 P ( y= 1| X   i )  Eq. 2
 
     In some cases, the set of unlabeled sessions  401  includes a subset of sessions that have a session feature associated with human-generated network traffic. For example, the set of unlabeled sessions  401  includes a subset  404  of network sessions that include one or more purchases. In diagram  400 , the session feature associated with human-generated network traffic is the inclusion of one or more purchases in a session. However, other human-associated session features are envisioned. 
     Additionally or alternatively, a respective feature vector X includes a purchase feature indicating that the respective session included one or more purchases. For convenience, and not by way of limitation, the purchase feature is represented by b, and has a value of 1 for a session including at least one purchase and a value of 0 for a session including no purchases, as indicated by Equation 3.
 
 b= 1,purchase session
 
 b= 0,non-purchase session  Eq. 3
 
     In some cases, the session identification system determines the subset  404  of purchase sessions, such as based on the presence of the purchase feature b. Additionally or alternatively, the session identification system determines a set  403  of sessions generated by a purchaser, such as by identifying an additional session feature that is included in the subset  404  of purchase sessions. In addition, the session identification system determines a subset  405  of sessions that are generated by a purchaser but which do not include a purchase. In some cases, the number of sessions included in the set  403  and the subset  405  are known. 
     In some embodiments, the session i in the set of unlabeled sessions  401  has a probability of being included in the subset  404  of purchase sessions. Additionally or alternatively, the feature vector X i  for the session i has a probability of having the purchase feature b with a value of 1, as indicated by Equation 4. In some cases, the probability that the session i in the set of unlabeled sessions  401  is also included in the subset  404  of purchase sessions is provided by Equation 4.
 
 P ( b= 1| X   i )  Eq. 4
 
     In addition, the Equation 4 is marginalized over y. For example, the probability that a feature vector X has a purchase feature b with a value of 1 is equivalent to the probability that X has b with a value of 1 and y with a value of 1 summed with the probability that X has b with a value of 1 and y with a value of 0, as indicated by Equation 5. In some cases, Equation 5 indicates that the probability of the session i being a purchase session (e.g., b=1) is equal to the probability of session i being a purchase, human session (e.g., b=1, y=1) summed with the probability of session i being a purchase, non-human session (e.g., b=1, y=0).
 
 P ( b= 1| X   i )= P ( b= 1, y= 1| X   i )+ P ( b= 1, y= 0| X   i )  Eq. 5
 
     In some cases, the total number of sessions included in the subset of purchase sessions  404  is known. For example, a session identification system, as described in regards to  FIGS. 1 and 2 , determines a number of purchase sessions in the subset  404  based on the analysis of session data describing the set of unlabeled sessions  401 . Additionally or alternatively, the feature vector X i  for a particular session i in the subset  404  has the purchase feature b with a value of 1. Based on the determination that b=1 (e.g., a purchase session) is associated with human-generated network traffic, the subset  404  is determined to be included in the set of human-generated sessions  402  (e.g., goods or services are not purchased by bots). Additionally or alternatively, the determination that b=1 is associated with human-generated network traffic indicates that the probability of the session i included in subset  404  being a purchase, non-human session is zero, as indicated by Equation 6.
 
 P ( b= 1| X   i )= P ( b= 1, y= 1| X   i )+0  Eq. 6
 
     In some cases, the Equation 6 is expanded using the conditional probability expansion, as indicated by Equation 7. Additionally or alternatively, the Equation 7 indicates that the probability that the session i is a purchase session is equal to the probability that the session i is a human session multiplied by the probability that the session i is a purchase session given that session i is a human session.
 
 P ( b= 1| X   i )= P ( y= 1| X   i ) P ( b= 1| y= 1, X   i )  Eq. 7
 
     Additionally or alternatively, the Equation 7 is rearranged to provide Equation 8, indicating that the probability that the session i is a human session is equal to the probability that the session i is a purchase session divided by the probability that session i is a purchase session given that session i is a human session. 
     
       
         
           
             
               
                 
                   
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     In diagram  400 , the probability that the particular session i in the set of unlabeled sessions  401  is also included in the set of human sessions  402  is provided by Equation 8. In some embodiments, a session identification system, such as session identification system  210 , identifies the probability of each session in an unlabeled set being a human session, based on a combination, such as via Equation 8, of the probability that each session is a purchaser session and the probability that each session is a purchaser session given that the respective session is a human session. For example, the session identification system  210  receives session data  233  representing the set of unlabeled sessions  401 , and determines the labeled subset  240  including the subset  404  of sessions including a purchase. The classifier  211  included in the session identification system  210  generates, for example, a first probability  212  indicating that the ith session represented by the session data  233  is a purchase session, based on the set  401  and the subset  404 . Additionally or alternatively, the session identification system  210  determines the derived subset  245  including one or more of set  403  of purchaser sessions or subset  405  of purchaser sessions that do not include a purchase. The classifier  215  included in the session identification system  210  generates, for example, a second probability  216  indicating that the ith session represented by the session data  233  is a purchase session given that the ith session is generated by human, based on one or more of the sets  401  and  403  and the subsets  404  and  405 . Based on a combination of the first and second probabilities  212  and  216 , the session identification system  210  determines, via Equation 8, the probability that the ith session represented by the session data  233  is generated by a human. 
     In an embodiment, a classifier included in a session identification system generates a probability describing the ith session based on the feature vector X i  describing the session i. For example, the session data  233  includes a respective feature vector X i  describing the ith session represented by the session data  233 . In some cases, the classifier  211  generates the probability  212  for the ith session based on a classification of the feature vector Xi included in the session data  233 . The feature vector Xi is classified, for example, based on a similarity with the feature vectors included in the labeled subset  240 . Additionally or alternatively, the classifier  215  generates the probability  216  for the ith session based on a similarity of the feature vector Xi with the feature vectors included in the derived subset  245 . In some embodiments, a respective probability  212  and probability  216  are generated for each respective session represented by the session data  233 . Additionally or alternatively, the session identification system  210  determines a respective combination of the probabilities  212  and  216  for each respective session represented by the session data  233 . 
     Example of a Computing System for Classifying Network Traffic 
     Any suitable computing system or group of computing systems can be used for performing the operations described herein. For example,  FIG. 5  is a block diagram depicting a system for classifying network traffic as human-generated or non-human, according to certain embodiments. In some embodiments, one or more of the computing systems depicted in  FIG. 5  are included in a given computing device, such as a personal computer or a server device. Additionally or alternatively, one or more of the computing systems depicted in  FIG. 5  are included in a distributed or virtual computing system, such as a group of computing devices operating together or a cloud-based computing system. 
     The depicted example of a session identification system  210  includes one or more processors  502  communicatively coupled to one or more memory devices  504 . The processor  502  executes computer-executable program code or accesses information stored in the memory device  504 . Examples of processor  502  include a microprocessor, an application-specific integrated circuit (“ASIC”), a field-programmable gate array (“FPGA”), or other suitable processing device. The processor  502  can include any number of processing devices, including one. 
     The memory device  504  includes any suitable non-transitory computer-readable medium for storing the session data  233 , the classifiers  211  or  215 , the labeled subset  240 , the derived subset  245 , and other received or determined values or data objects. The computer-readable medium can include any electronic, optical, magnetic, or other storage device capable of providing a processor with computer-readable instructions or other program code. Non-limiting examples of a computer-readable medium include a magnetic disk, a memory chip, a ROM, a RAM, an ASIC, optical storage, magnetic tape or other magnetic storage, or any other medium from which a processing device can read instructions. The instructions may include processor-specific instructions generated by a compiler or an interpreter from code written in any suitable computer-programming language, including, for example, C, C++, C #, Visual Basic, Java, Python, Perl, JavaScript, and ActionScript. 
     The session identification system  210  may also include a number of external or internal devices such as input or output devices. For example, the session identification system  210  is shown with an input/output (“I/O”) interface  508  that can receive input from input devices or provide output to output devices. A bus  506  can also be included in the session identification system  210 . The bus  506  can communicatively couple one or more components of the session identification system  210 . 
     The session identification system  210  executes program code that configures the processor  502  to perform one or more of the operations described above with respect to FIGS. 1-4. The program code includes operations related to, for example, one or more of the session data  233 , the classifiers  211  or  215 , the labeled subset  240 , the derived subset  245 , or other suitable applications or memory structures that perform one or more operations described herein. The program code may be resident in the memory device  504  or any suitable computer-readable medium and may be executed by the processor  502  or any other suitable processor. In some embodiments, the program code described above, the session data  233 , the classifiers  211  and  215 , the labeled subset  240 , or the derived subset  245  are stored in the memory device  504 , as depicted in  FIG. 5 . In additional or alternative embodiments, one or more of the session data  233 , the classifiers  211  and  215 , the labeled subset  240 , the derived subset  245 , and the program code described above are stored in one or more memory devices accessible via a data network, such as a memory device accessible via a cloud service. 
     The session identification system  210  depicted in  FIG. 5  also includes at least one network interface  510 . The network interface  510  includes any device or group of devices suitable for establishing a wired or wireless data connection to one or more data networks  512 . Non-limiting examples of the network interface  510  include an Ethernet network adapter, a modem, and/or the like. The network application server  130  is connected to the session identification system  210  via network  512 , and the network application server  130  can perform some of the operations described herein, such as providing session data  233 . The session identification system  210  is able to communicate with one or more of the network application server  130  and the analytics system  150  using the network interface  510 . In some cases, the client computing systems  120  are able to communicate with one or more of the network application server  130 , the analytics system  150 , or the session identification system  210  using the network  512 . 
     General Considerations 
     Numerous specific details are set forth herein to provide a thorough understanding of the claimed subject matter. However, those skilled in the art will understand that the claimed subject matter may be practiced without these specific details. In other instances, methods, apparatuses, or systems that would be known by one of ordinary skill have not been described in detail so as not to obscure claimed subject matter. 
     Unless specifically stated otherwise, it is appreciated that throughout this specification discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining,” and “identifying” or the like refer to actions or processes of a computing device, such as one or more computers or a similar electronic computing device or devices, that manipulate or transform data represented as physical electronic or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the computing platform. 
     The system or systems discussed herein are not limited to any particular hardware architecture or configuration. A computing device can include any suitable arrangement of components that provides a result conditioned on one or more inputs. Suitable computing devices include multipurpose microprocessor-based computer systems accessing stored software that programs or configures the computing system from a general purpose computing apparatus to a specialized computing apparatus implementing one or more embodiments of the present subject matter. Any suitable programming, scripting, or other type of language or combinations of languages may be used to implement the teachings contained herein in software to be used in programming or configuring a computing device. 
     Embodiments of the methods disclosed herein may be performed in the operation of such computing devices. The order of the blocks presented in the examples above can be varied—for example, blocks can be re-ordered, combined, and/or broken into sub-blocks. Certain blocks or processes can be performed in parallel. 
     The use of “adapted to” or “configured to” herein is meant as open and inclusive language that does not foreclose devices adapted to or configured to perform additional tasks or steps. Additionally, the use of “based on” is meant to be open and inclusive, in that a process, step, calculation, or other action “based on” one or more recited conditions or values may, in practice, be based on additional conditions or values beyond those recited. Headings, lists, and numbering included herein are for ease of explanation only and are not meant to be limiting. 
     While the present subject matter has been described in detail with respect to specific embodiments thereof, it will be appreciated that those skilled in the art, upon attaining an understanding of the foregoing, may readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, it should be understood that the present disclosure has been presented for purposes of example rather than limitation, and does not preclude inclusion of such modifications, variations, and/or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art.