# Patent

## Title
System, method, and computer program product for tracking user activity during a logon session

## Abstract

The present disclosure is directed to a system, method, and computer program for tracking user activity during a logon session, including tracking device access and any user account switches during the logon session. In response to receiving an event log for an IT event in the network, the system determines whether to filter the event, associate the event with an existing user logon session, or associate the event with a new user logon session. The system executes one or more rules to determine whether an event represents a user account switch or the continued use of an account by a user currently logged onto the network. If so, the event is associated with an existing logon session. If not, the system determines whether the event satisfies criteria for a new user logon session.

## Background
RELATED APPLICATIONS
This application is a continuation-in-part of U.S. patent application Ser. No. 14/507,585, titled “System, Method, and Computer Program Product for Detecting and Assessing Security Risks in a Network,” and filed on Oct. 6, 2014, the contents of which are incorporated by reference as if fully disclosed herein.


BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates generally to IT networks and, more particularly, to tracking user IT activity during a logon session, including device accesses and any account switches.
2. Description of the Background Art
Computer networks of companies, government agencies, institutions, and other entities are frequently under attack from hackers. Known systems require administrators to build queries against an IT database in order to determine security risks. For example, an administrator may run a query for user accounts in which a user tried to log in five times and failed. This approach requires the administrator to know the behavior patterns of attackers and predefine what is considered a risk.
A problem with this approach is that attackers' patterns vary and change, and, therefore, it is not always possible to know in advance what malicious behavior looks like. Also, attackers often impersonate a registered user in the network. Therefore, there is a need for a solution that can detect security risks for unknown attack patterns and that can detect attackers impersonating legitimate users of a network. There is particularly a need to be able to track user activity during a logon session, including tracking the user through account switches and device switches.
SUMMARY OF THE DISCLOSURE
The first part of the disclosure (i.e., the disclosure of the parent application) is directed to a system, method, and computer program for detecting and assessing security risks in an enterprise's computer network. In one embodiment, a computer system builds behavior models for users in the network (one for each user) based on the users' interactions with the network, wherein a behavior model for a user indicates client device(s), server(s), and resources (e.g., applications, data) used by the user. For each user that logs onto the network, the system compares a plurality of user events during a period of time (e.g., a logon session or a 12 or 24 hour day) to the user's behavior model, including comparing a client device used, server(s) accessed, and any resources accessed to the user's behavior model. The system calculates a risk assessment for the plurality of user events based at least in part on the comparison between the user events and the user's behavior model, wherein any one of certain anomalies between the user events and the user's behavior model increase the risk assessment.
In certain embodiments, the period of time is a user logon session (also referred to as a login session) in the network and the risk assessment is calculated for the user logon session, and wherein a user logon session begins at a user's log on to the network and ends at the subsequent log out of the network or a specified period of inactivity.
In certain embodiments, the user events are identified from raw data logs, wherein enhanced events logs are created from the raw data logs by adding additional context information related to the user events, wherein the enhanced event logs are grouped by user logon session to track user actions during a user logon session, and wherein the enhanced event logs are used to build user behavior models.
In certain embodiments, the additional context information comprises one or more of the following: additional user information, additional client device information, additional server information, and additional information about accessed data.
In certain embodiments, the user's behavior model includes the user's time logon patterns and comparing the plurality of user events to the user's behavior model also includes determining whether the user events are occurring or occurred at a time consistent with the time patterns in the user's behavior model.
In certain embodiments, the user's behavior model include the user's geo-location logon patterns and comparing the plurality of user events to the user's behavior model also includes determining whether a geo-location from which the user logged in is consistent with the geo-location patterns in the user's behavior model.
In certain embodiments, calculating the risk assessment comprises associating a sub-total risk score with each of certain anomalies in the user events and aggregating all sub-total risk scores to calculate the risk assessment for the plurality of user events.
In certain embodiments, the system stores rules that define types of anomalies associate with a positive risk score and calculating a risk score for each of the user events comprises determining whether the user event satisfies one of the rules. There may also be rules associated with a negative risk score.
In certain embodiments, one or more of the following are factored into the risk assessment: the user's access authorization level in the system, a value of the data accessed, and threat intelligence.
The second part of the disclosure (the continuation-in-part) relates to alternate or additional methods for tracking user activity during a logon session, including across device accesses and any user account switches. The session tracking methods disclosed herein may be used for detecting security risks, or they may be used in other IT contexts in which it may be desirable to tracker user activity across a logon session. In one embodiment, the method comprise the following steps:

    
    
        for each user logged onto the network, maintaining a logon session state for the user that reflects events, host devices, and user accounts associated with the user during the logon session, wherein a logon session begins at a user's logon to the network and ends at the user's logout or a specified period of inactivity;
        for each host device in the network, maintaining a device state that reflects all users logged onto the host device and the user accounts used to logon to the host device;
        receiving an event log for a user event, wherein the event log indicates that a user has accessed a host device in the network (“the destination device”);
        identifying from the event log a device from which the event originated (“the originating device”) and a user account used for the event (“the event user account”);
        determining whether to associate the event with a user with an open logon session by performing the following:
        
            identifying all users currently logged onto the originating device and all user accounts associated with the originating device;
            determining whether the event can be associated with a user currently logged onto the originating device by ascertaining whether the event represents (i) a user account switch from the originating device to the destination device or (ii) the continued use of the event user account from the originating device to the destination device, wherein:
            
                in response to only one user being currently logged onto the originating device and in response to said one user using a different user account on the originating device than the event user account, concluding that said one user switched user accounts in accessing the destination device and associating the event with said one user; and
                in response to one or more users being logged onto the originating device and in response to one and only one of said users using the event user account, concluding that said one user continued to use the event user account in accessing the destination device and associating the event with said one user;
            
            
            in response to not associating the event with a user currently logged onto the originating device, determining if the event user account is currently in use by only one user logged onto the network, and, in response to this determination evaluating to true, associating the event with said user;
        
        
        in response to associating the event with a user in any of the foregoing steps, (i) updating the logon session state of said user to reflect the event, including any user account switch, and (ii) updating the state of the destination device to associate said user and the user account with the destination device;
        in response to not being able to associate the event with a user with an open logon session, determine if the event meets criteria for a new logon session;
        in response to the event meeting criteria for a new logon session, (i) identifying a user associated with the user account used in the event, (ii) creating a new, open logon session for said identified user having a state that reflects the event and the use of the user account, and (iii) updating the state of the destination device to associate said one user and the user account with the destination device; and
        in response to neither associating the event with an open logon session, nor the event not meeting criteria for a new logon session, ignoring the event or performing additional event processing on the event.
    
    





BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a flowchart that illustrates a method for detecting and assessing security risk in an enterprise's computer network.
FIG. 2 is a flowchart that illustrates an example implementation of the method of FIG. 1.
FIG. 3 illustrates an example of an enriched log.
FIG. 4a is an example of the fields in a session database.
FIG. 4b is an example of fields in an asset database.
FIGS. 5a-b are flowcharts that illustrate an example method for tracking user movement across a network during a logon session.
FIG. 6 illustrates an example histogram.
FIG. 7 illustrates an example of a numeric histogram.
FIG. 8 illustrates examples of the type of data in user and department behavior models.
FIGS. 9a-b illustrate example histograms.
FIGS. 10a-b illustrate example histograms.
FIGS. 11a-b are flowcharts that illustrate an example of calculating a risk score.
FIG. 12 is a table of example rules.
FIG. 13 is a flowchart that illustrates an example of a method for calculating a risk transfer score.
FIG. 14 illustrates a conceptual example of the type of behavior comparisons made in a behavior analysis.
FIG. 15 illustrates an example architecture for a system for detecting and assessing security risks.
FIGS. 16a-b are flowcharts that illustrate a method that illustrates the functions performed by software modules in the system.
FIG. 17 is a screenshot that illustrates an example administrative interface.
FIGS. 18a-18b, 19a-19b, and 20a-20b are flowcharts that illustrate a method for processing event logs and tracking user activity across a logon session in accordance with one embodiment of the invention.
FIG. 21 illustrates a logon sequence flow in an example network scenario.
FIG. 22 is a block diagram that illustrates an example data structure for maintaining device state.



DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
FIG. 1 illustrates a method, performed by a computer system, for detecting and assessing security risks in an enterprise's computer network/system according to one embodiment of the invention. The system builds a behavior model for authorized users in an enterprise's computer network (step 110). An enterprise may be a company, institution, government agency, or other entity with multiple users. The behavior model is a data model that reflects the user's behavior with respect to the IT infrastructure in the network, such as the client device(s) used to access the network, VPNs used to access the network, the server(s) accessed in the network, applications used in the network, data accessed in the network, time/day logon patterns, and geo-location logon patterns. The data in a behavior model indicates a user's “normal” behavior in that it indicates the devices, applications, data, and patterns most often used, accessed, or otherwise associated with the user.
In response to a user accessing the enterprise's network, the system compares the user's current behavior in the network over a period of time to the user's behavior model and determines if there are any anomalies between the user's behavior during the period of time and his/her behavior model (step 120). Specifically, in one embodiment, the system compares data related to a plurality of user events during a period of time, such as network logon, server access, data access, etc., to the behavior model. The period of time may be of defined length or variable length. An example of a defined-length period of time is a 12 or 24-hour day. An example of a variable-length period of time is a user logon session, which begins when a user logs in to the network and ends either at log out or after a certain period of inactivity (e.g., 5 hours of inactivity).
The system calculates a risk score, or otherwise assesses a risk, associated with the user events during the period of time (step 130). The risk score is based at least in part on the comparison between the user events during the period of time and the user's behavior model. As discussed further below, other factors may also be included in the risk score calculation, such as the user's access authorization level in the system, the value associated with data accessed, and threat intelligence information.
In certain embodiments, the system may aggregate data from users that share a common characteristic (e.g., same role or department) and create a group behavior model. In such embodiments, a user's behavior in a session may be compared to both the user's behavior model and the group's behavior model. Also, in certain embodiments, the system may build behavior models for assets, such as devices. The use of an asset in a session may be compared to the behavior model for the asset. For example, a behavior model for a device may track the users who have accessed the device (and how many times), and the system may be configured such that a risk-score-rule triggers when an unexpected user accesses the device. Behavior for other entities can be modeled too and analyzed in accordance with the methods described herein.
FIG. 2 illustrates an example implementation of the above method, once initial behavior models have been created for users in the system. The system parses raw data logs to identify applicable user events in the system (step 210). In one embodiment, after an initial training period in which past data logs (e.g., the past 90 days) are parsed for the purpose of creating the initial behavior models, this is done on an on-going basis as raw data logs are received from one or more log systems. The system then creates enriched event logs with additional data that provides further context for the user events (discussed below) (step 220). The system uses the enriched event logs to track users through a “session,” which may be a logon session or a defined period of time (step 230). Each user session is then analyzed to determine whether the user behavior is consistent with past behavior for the user and/or user's peers/department or is considered an anomalous behavior (step 240). A risk score is calculated for each user session based at least in part on the analysis in step 240 (step 250). Other factors that may also influence the risk assessment include the user's access authorization level in the system, the value of the data accessed, and threat intelligence information.
If the behavior in a user session is considered normal (e.g., risk score is low), then the session data is used to update the user's behavior model (and, in some cases, the applicable department/peer/asset behavior model) (step 260). If the behavior is considered abnormal (e.g., risk score exceeds a threshold), an alert or notice is displayed in an administrative interface for the risk assessment system (step 260), and the session data is not used to update the behavior model.
Each of the functions performed in FIG. 2, as well as creating behavior models, is discussed in further detail below.
Log Extraction and Context Enrichment
In one embodiment, the system obtains raw data logs related to a user's interactions with the IT infrastructure, such as user logon events, server access events, application access events, and data access events. The raw data logs may be obtained from third party systems.
The system identifies applicable user events (i.e., those user events used in a risk assessment, such as the aforementioned events) from the raw data logs and creates event logs. An event may span multiple raw data logs, and, therefore, there may be a 1 to n ratio between event logs and raw data logs.
The event logs are then supplemented with additional data that provides further context for the user events. In one embodiment, the context includes the identity of the user (e.g., the full name of the user) and his/her role within the enterprise. For example, if the original event log only includes a user logon ID, the system may enrich the event log with the user's full name, role, and department. Examples of the type of questions context information will enable the risk engine to answer are: “Is the user an administrator?” or “Is the user an executive?” This is important because the risk to the system may depend on who the user is. Abnormal behavior by a user with access to highly sensitive data may be more of a threat than abnormal behavior by a user with only limited access privileges. Other types of context information added to an event log may include information pertaining to the client and/or server devices involved in the event, such as the location of the devices.
In order to enrich the event logs with additional data, the system may look up information in other data sources, such as Active Directories, human resources databases, threat intelligence databases, and asset databases. For example, the system may access an Active Directory database to determine to identify the full name of a user associated with a user logon and password. Similarly, the system may access an asset database in order to determine a device name and geographical zone associated with an IP address. In one embodiment, the system parses risk rule expressions to see what additional context data is needed in order to evaluate the rule expression. The rules may specify the data required for the rule and the data source(s) from which the information is to be retrieved. Thus, the type of data used for context enrichment need not be hardcoded into the software of the system, but may be obtained from the rules and therefore the system can dynamically change its context enrichment as rules change.
FIG. 3 illustrates an example of an enriched log 320. In this case, the original event log 310 is supplemented with the logon type, privilege level of the user, full name of the user, the user's title, the user's department, the user's normal work location, the user's employment status, the user's phone number, the user's manager, the workstation name from which the user logon, the geographical zone in which the workstation resides, the user to whom the workstation is assigned, the name of the server to which the user logged in, and the geographical zone in which the server resides.
Session Tracking
The enriched event logs are used to track user behavior through a session (fixed length or variable length). The system evaluates the event logs and groups them by session (i.e., maps event logs to sessions). Grouping events by logon session enables the system to determine whether the behavior over the course of an entire logon session is normal, such as whether the duration of the session is normal or the number of servers accessed within a session is normal.
In evaluating an event log, the system determines whether the event belongs to an existing, open session or whether the event is a new logon session. In one embodiment, the system maintains a session database, with an entry for each user logon session. When the system determines that a new logon session has started, it adds an entry to the session database for the session and adds information from the applicable event log to the session entry. If the system determines that an event belongs to an existing session, it will add information from the event log to the existing session.
FIG. 4a illustrates an example of the fields in a session database. The session ID 405 is a unique ID for the session. The start times and end times 415, 420 are the start and ends of the session. The user name 410 is the name of the user associated with the account used for the first logon event for the session. The events 425 are the event logs associated with the logon session, the accounts 430 are the accounts used by the user during the logon session, and the assets 435 are the assets (i.e., devices and, in some cases, resources) accessed during the logon session For purposes of this disclosure, a “user” is a person (e.g., Steve Donahue) and an “account” is an account via which a user (or group of users) can log onto the system (e.g., an account with name “sdonahue” and password “ABC123”). Those skilled in the art will appreciate that the actual database may include additional or other fields.
The data in the session database enables the system to maintain a state for each user in the system. In one embodiment, maintaining a state for a user comprises tracking whether the user is logged into the system and, if so, tracking the user accounts used and assets (e.g., devices, resources (in some embodiments) accessed during the session, including keeping track of which device was last accessed by the user.
During a security attack, the attacker may logon to the system using one identity and switch identities (i.e., switch user accounts), hopping from one device to another. Thus, maintaining a user state throughout a logon session includes tracking a user's movements across a network, including if he/she switches identities in moving from one device to another. Via the session information stored in the session database, the system knows which device a user last accessed and, if it sees a new device-access event originating from the device the user last accessed, it associates the new device-access event with the user, even if different user accounts were used to access the two devices.
FIG. 5 illustrates an example method for tracking user movements (i.e., sequential device-accesses) across a network during a logon session, as well as tracking user accounts used during a logon session. The system receives an event log for a device-access event (step 510). From the event log, the system identifies the device from which the access event originated (step 520) and searches open sessions (i.e., logon sessions that have not yet ended) in the session database to see if the originating device is in use in a single, open, logon session (step 530). If the originating device is use by an open logon session (and only one open logon session), the system associates the event with the open logon session (step 540), and updates the session data for the session with information from the event, including adding the device and account used (if not already in the session) to the session data (step 550). If not, the system determines if the user account used in the device-access event is in use in an open logon session (step 560). If the user account has been used in an open logon session, the event is associated with that open logon session (step 570), and the session data for the session is updated with information from the event log (step 550). In one embodiment, the determination in step 560 only evaluates to a “yes” if the user account in question was used to logon to the network in another open session (i.e., the account was used in the event that started the logon session). If determination in step 560 evaluates to a “no,” the system then determines whether the device-access event meets criteria for a new logon session (i.e., whether the event can be considered the start of a logon session) (step 585). An example, in one embodiment, of an event that satisfies the criteria is if a user who is not logged on anywhere logs onto a computer where nobody else is currently logged on. In response to the event satisfying the criteria, the system creates a new session entry in the session database (step 590). Otherwise, the event is ignored or subject to additional processing.
The way in which devices are identified in raw data logs can vary. For example, some raw data logs may reference device by IP address and others by host name. Therefore, in one embodiment, the system maintains a device or asset database in which IP addresses and host names are mapped to unique device IDs. FIG. 4b illustrates an example of the fields in an asset database, which in this case store the following information for an asset: object ID 440 (a unique identifier for the device/object), asset type 445 (e.g., Windows server, Linux server, etc.), asset name 450, IP address 455, time first seen 460, time last seen 465, geographical zone 470, risk state 475 (e.g., compromised or uncompromised), compromised time 480 (if compromised), and risk session 485 (if compromised, the session ID in which the asset was first compromised). An asset may be marked compromised in the asset database if an administrator designates it as compromised or if information associated with the session (e.g., risk score) meet certain criteria, such as the risk score exceeding a specified threshold. The asset database may also be used to store information for and track the state of other types of assets, such as resources (e.g., applications, files, etc.).
As an example of session tracking, assume the system receives raw data logs indicating the following events in the following order:

    
    
        Event 1: sdonahue logs onto Workstation 1 with IP Address 111.111.11.111
        Event 2: jmiller logs onto Server 2 from IP Address 111.111.11.111
        Event 3: jmiller logs onto 111.111.11.113 from 111.111.11.112

The system will then create context-enriched event logs that indicate the following (among other things):

        Context-enriched Event 1: Steve Donahue logged onto Workstation 1 with IP address 111.111.11.111 using the “sdonahue” account. This is the start of a logon session for Steve Donahue.
        
            The system mapped the “sdonahue” account to Steve Donahue using a user account database.
            The system will associate Workstation 1 with IP address 111.111.11.111 in the asset database.
        
        
        Context-enriched Event 2: John Miller logged onto Server 2 from Workstation 1 using the “jmiller” account.
        
            The system used the asset database to identify that IP address 111.111.11.111 is Workstation 1.
            The system mapped “jmiller” to John Miller
            For purposes of this example, assume that at the time of Event 2, “sdonahue” is the only user logged onto Workstation 1.
        
        
        Context-Enriched Event 3: John Miller logged into Sever 3 from Server 2 using the “jmiller” account.
        
            The system mapped “jmiller” to John Miller
            The system used the asset database to identify that IP address 111.111.11.112 is Server 2 and that IP address 111.111.11.113 is server 3.
        
        
    
    


Using the method of FIG. 5, the system will associate all three of the above events with the same session and infer the following:

    
    
        Steve Donahue logged onto Workstation 1 using the “sdonahue” account.
        Steve Donahue used the “jmiller” account to log onto Server 2 from Workstation 1.
        Steve Donahue used the “jmiller” account to log onto Server 3 from Server 2.
    
    


The logon session data for Steve Donahue will indicate that Steve Donahue accessed Workstation 1, Server 2, and Server 3 in that order. It will also indicate that he used both the “sdonahue” and “jmiller” accounts during the session. In this example, the system will not create a new logon session for Jeff Miller in response to receiving Event 2. This is because the system will recognize that the originating computer (Workstation 1) in Event 2 is already in use by Steve Donahue (or a person using Steve Donahue's account). Therefore, the “jmiller” account will be associated with Steve Donahue for this session, even if Steve Donahue has never used that account before and even though the account is normally associated with Jeff Miller. Event 3 will be associated with Steve Donahue because the session database will indicate that he logged onto Server 2 with the “jmiller” account and that the “jmiller” account was used to logon onto Server 3 from Server 2.
Behavior Models
Select session data is recorded in behavior models. In one embodiment, the system has an initial training period with x number of days (e.g., 90 days) in which x days of session data are recorded in behavior models before behavior analysis begins. Subsequently, after a risk assessment has been made for a session, the applicable behavior models are updated with data from the session, provided the risk score for the session does not exceed a threshold.
In one embodiment, a behavior model comprises a plurality of histograms, where there is a histogram for each category of data in the behavior model. For example, there may be a histogram for each of the following: client devices from which the user logs in, servers accessed, data accessed, applications accessed, session duration, logon time of day, logon day of week, and geo-location of logon origination. In a histogram, values for a category are along one axis (e.g., the x axis) and the number of times the value is received for the category is along another axis (e.g., the y axis). Each time an event occurs that correspond to a histogram, the system updates the histogram in the applicable user's/group's/asset's behavior model(s), unless the behavior is in a session with a risk score that exceeds a threshold (i.e., the session is considered high risk). FIG. 6 illustrates an example of a categorical histogram in which the values for the categories are strings. In this example, the category is the computer from which the user has logged on in the past.
On the x-axis 610 are the “bins” that represent the string values received for the category (i.e., computer from which the user logged in), and the values on the y-axis 620 are the number of times the value has been received. In the illustrated example, the histogram shows that the user logged in from his MAC twenty times, his DELL ten times, and his ASUS two times. Therefore, if the user logs in with a LENOVO the next time, the system may detect an anomaly. Those skilled in the art will appreciate that devices are identified by a unique ID, but brand names were used in FIG. 6 for simplicity.
Numerical histograms may be used for categories where the values are numbers, times, days of week (e.g., the seven days are represented by the numbers 1-7), or schedule-related categories. In such cases, each bin represents a number or a number range. FIG. 10a, which is discussed below, is an example of a numerical histogram in which the bin values are ranges of numbers representing the number of server accesses in a single logon session.
FIG. 7 illustrates an example of a numerical, fixed-width histogram, wherein the category is the hour of the day in which the user logs in. The bins on the x-axis 710 each represent an hour-long time range. In the depicted histogram, the user logged in between 7:00-8:00 am fifteen times, between 8:00-9:00 am twenty times, between 9:00 am-10:00 am twenty-three times, and between 9:00 pm-10:00 pm fifteen times.
In one embodiment, the risk assessment rules executed by the rules engine (discussed below) dictate the type of behavior recorded in a histogram. The behavior models are created such that they can be used to evaluate the rules. In other words, in one embodiment, the data recorded in the behavior models correspond to variables in the rule expressions.
In order for a behavior model to truly represent a user's normal behavior, there must be sufficient data in the behavior model. In one embodiment, a behavior will only be considered in the risk assessment if there is sufficient data for the behavior to determine whether a new data point for the behavior is anomalous. In one embodiment, the system calculates a confidence score for each histogram each time data is added to the histogram, wherein only histograms having a confidence level above a threshold are used in the risk calculation score. In one embodiment, the confidence value is calculated as follows:




  
    
      Confidence
      ⁢
      
        
      
      ⁢
      factor
    
    =
    
      
        [
        
          
            N
            -
            C
          
          N
        
        ]
      
      ∝
    
  
  ,




where N=the number of observed events, and C=the number of category values received (e.g., the number of bars or bins in the histogram). The coefficient ∝ affects how quickly the confidence factor converges to 1, and it can be modified to adjust the speed at which the confidence factor changes. In certain embodiments, ∝ ranges from 1-3.

In the above formula, the higher the number of observed event (N) and the lower the number of category values received (C), the closer the confidence factor is to 1. Conversely, the closer the number of category values (C) is to the number of observed events (N), the closer the confidence factor is to zero. In one embodiment, certain categories must have a confidence factor of 0.7 or 0.8 before they are used for anomaly detection. The threshold confidence factor required may vary from rule to rule and may be specified in the rule expression.
FIG. 8 illustrates examples of the type of data in user and department behavior models. In one embodiment, the data in user behavior models 810 are aggregated and used to create a group behavior model 820 for a group of users sharing at least one common characteristic (e.g., department, job, location, etc.). Behavior models may also be created for assets, such as devices.
Behavior Analysis
For each user logon session, the system compares the user's behavior in the session to the behavior model of the user or to an aggregated behavior model of people similar to the user in role, location, department, or other grouping criteria. Objects or information in the session compared to a behavior model may include client device used, location from which logon originates, servers accessed, number of servers accessed, data accessed, applications accessed, and time/duration of session. Data in the session may also be compared to an asset behavior model.
In one embodiment, the rule expressions used to calculate a risk score for a session (discussed below) define which behaviors are recorded in histograms and analyzed for anomalies. A rule expression may specify how an anomaly is to be calculated or determined. Some rule expressions may require the system to determine whether a data point is anomalous, and others may require the system to quantify how different or similar a data point is compared to a behavior model. The system is able dynamically change its behavior models and anomaly detection calculations as rules are added, deleted, or modified. The system may apply machine learning and/or statistical algorithms on the data to determine whether a data point is anomalous or to quantify how different or similar a data point is relative to other the majority of data points in a behavior model. The algorithms may use parameters specified in the rule expression. The system may perform anomaly detection on a per feature level or may use clustering algorithms to look jointly at multi-dimensional features.
An example of how a system determines whether a data point is anomalous is described with respect to FIGS. 9a and 9b. FIG. 9a illustrates a histogram that records the servers a user has accessed and the number of times each server was accessed. To determine whether a particular server access is anomalous, the system determines the total number of servers accessed by the user. In this example, the total is 91. The system calculates an anomaly threshold by multiplying the total number of server accesses by a percentile threshold parameter, which 10% in this example. The result is 9.1. The percentile threshold parameter may be specified by the applicable rule expression and may vary from one rule to another.
The system then finds the lowest-value bin with a cumulative sum greater or equal than the anomaly threshold (9.1). FIG. 9b illustrates the cumulative sum for each bin in FIG. 9a. The cumulative sum for a bin is the bin's count plus the count of all the bins with a lower value. In this example, the first bin with a cumulative sum greater than or equal to the anomaly threshold (9.1) is the bin for the ABC$ server, which has a cumulative sum of 11. The value (not the cumulative sum) of this bin serves as the dividing line from what is considered normal and what is considered an anomaly. In this example, this means that a data point with a value less than 6 (the value of the ABC$ bin in FIG. 9a) is considered anomalous.
An example of how a system quantifies how different a data value is described with respect to FIGS. 10a-10b. In this example, the distance between a data point and a threshold data point in the model is calculated. FIG. 10a illustrates a histogram that records the number of server logons for a user in a single logon session. The system calculates the total number of logons in the model (i.e., the cumulative sum of all the bin values). In this example, the total is 91. The system calculates an anomaly threshold by multiplying the total number of server accesses by a percentile threshold parameter, which 10% in this example. The result is 9.1. As discussed above, the percentile threshold parameter may be specified by the applicable rule expression and may vary from one rule to another.
The system then finds the lowest-value bin with a cumulative sum greater or equal than the anomaly threshold (9.1). FIG. 10b illustrates the cumulative sum for each bin in FIG. 10a. In this example, the first bin with a cumulative sum greater than or equal to the anomaly threshold (9.1) is the “6-10” bin, which has a cumulative sum of 11. The distance between the data point and this closest edge of the bin (i.e., 10) is the measure of how different/similar the data point is relative to the data in the model. In this example, the distance is calculated as the normalized log value of the distance between the data point (100) to the closest edge of the bin (10). In this example, the calculation is log 10 [(100−10)/10+1)+1] *5. Although log value is used in this example, the normal distance may be used in other models, such as a Day of the Week histogram where the x values range from 1-7 (one number for each day of the week).
Calculating a Risk Score
The system calculates a risk score for each user logon session based at least in part on the comparison between the user's session behavior and the user′ behavior model, wherein select abnormalities between session behavior and the behavior model are associated with a higher risk score. The risk score may be calculated once the session is complete (i.e., user logs out or has a certain period of inactivity), or, while the session is open, the system may calculate a running risk score in substantially real time, wherein the score is updated as applicable user events for the session come into the system
FIG. 11 illustrates an example of calculating a risk score using rules that map certain behaviors to risk points. When a session begins, the system calculates a risk transfer score from previous session scores (step 1110). The purpose behind the risk transfer score is to ensure that users who recently have had one or more recent sessions with high-risk scores do not start the session with a non-zero score. An example of how to calculate the risk transfer score is described below with respect to FIG. 13. The system sets the initial session score to the risk transfer score (step 1115), and retrieves session data (e.g., the enriched event logs, values for variables in the rule expressions) (step 1120). In some cases, the system may also retrieve risk intelligence information from third party data sources. The system then analyzes the first event in the system and determines if there are any rules applicable to the logged event (steps 1125, 1130). If not, the system proceeds to the next event in the logon session. If there are rules applicable to the event, the system evaluates the applicable rules and determines if any of them are triggered (i.e., the expressions evaluate to true) (steps 1140, 1145). If so, the system adds points associated with the triggered rule(s) to the session score (step 1150). If there are additional events in the session, the system returns to step 1125 (step 1155). Otherwise, the system outputs a total score (step 1160). The total score may be adjusted based on third-party intelligence information.
FIG. 12 is a table of example rules. Column 1210 provides a summary of the criteria that would trigger the rule, column 1220 lists the points added to a risk score if a rule is triggered, column 1230 illustrates the confidence threshold the applicable data for the rule must have in order for the rule to trigger, and column 1240 lists list the events that cause the rule to be evaluated. Table 12 does not list actual rule expressions, but provides a summary of the rules for simplicity. The rules in table 12 are all associated with positive risk score points, but there may be rules that have negative points (i.e., rules that, if triggered, would lower the risk score). Not all the risk rules necessarily relate to anomaly detection. Some rules may trigger based on third party intelligence information (e.g., if the source IP address is a known risk threat), the sensitivity of the data or resource accessed, or the access level/role of the user. For example, a risk rule may specify that points be added to a risk score simply because the data accessed is designated as sensitive or because of the user has a high position in the enterprise (e.g., a CEO or vice-president).
FIG. 13 illustrates an example of a method for calculating a risk transfer score. The system retrieves the risk scores for the last n sessions (e.g., the last seven sessions) (step 1310) and, for each of the n sessions, assigns an effective risk score to the session based on whether the actual score for the session (excluding the risk transfer score for the session) is below or above a threshold (step 1320). Specifically, for sessions with actual risk scores less than a threshold, the system assigns an effective risk score of zero to the session for the purpose of calculating the risk transfer score. For sessions with risk scores above the threshold, the effective risk score is equated to the actual risk score. The system then calculates a weighted average score of the last n sessions using the effective scores, and multiples the weighted average by a transfer percentage to obtain a risk transfer score (steps 1330 and 1340). In one embodiment, the most recent sessions are assigned a higher weight than earlier sessions, with the last session having a significantly higher weight than the previous ones (e.g., two to three times higher than the next highest weight). If the transfer percentage is set to less than 100%, only a portion of the weighed average is assigned to the current session score.
An example of a risk score calculation is described below with respect to the session data illustrated in Table 1.










TABLE 1







Effective Score





Transfer after 
Session 


Session Start-End
Score
40 Threshold
Weight


















Oct 11th 9 am-5 pm
10
0
5


Oct 12th 9 am-5 pm
10
0
10


Oct 12th 7 pm-9 pm
90
90
15


Oct 13th 2 am-7 am
15
0
20


Oct 13th 9 am-5 pm
0
0
25


Oct 14th 9 am-5 pm
20
0
30


Oct 15th
85
85
90









Table 1 illustrates a user's past seven sessions, corresponding actual risk scores for each of the sessions (after subtracting the risk transfer score for the session), corresponding effective scores, and the weight applied to each of the sessions. In this example, any session with an actual score below 40 points is assigned an effective score of zero for the purpose of calculating the risk transfer score. If the risk transfer percentage is set to seventy percent, then the risk transfer score for the above session data using the method of FIG. 13 would be:

Risk Transfer Score=(0×5)+(0×10)+(90×15)+(0×20)+(0×30)+(85×90)/195*0.70=32

FIG. 14 illustrates a simplified conceptual example of the type of comparisons made by the system. In this example, a user, “Jerry,” has connected to his enterprises' network over the weekend from Romania using a VPN. In this case, the rules executed by the system in calculating the risk assessment score, effectively ask the questions 1420 illustrated in FIG. 14 and assign a corresponding risk subtotal score 1440 depending on the answer 1430. The timeline 1410 illustrates that the system is tracking the user's state throughout a logon session and the total risk score 1450 is adjusted as applicable events occur.
Example Architecture
FIG. 15 illustrates an example architecture for a system that performs the methods described herein. A person skilled in the art would understand that a system for performing such methods may be constructed in other ways. The methods described herein may be implemented in other systems and are not limited to system 1500.
The system includes a Raw Log Retriever module 1510, an Event Detector module 1520, a Session Manager module 1530, a Modeling and Anomaly Detection module 1540, a Rules Engine 1550, and a Rule Session Manager module 1560. Modules 1510-1560 are software modules that are executed on one or more computer systems. The system 1510 stores event logs, rules, behavior models, and session scores in one or more databases 1570 and may also access one or more third party systems 1580 for raw data logs, context data, or threat intelligence.
FIG. 16 is a flowchart that illustrates the functions performed by the various modules in system 1500. The Raw Data Log Retrieve module 1510 obtains raw data logs related to a user's interactions with the IT infrastructure, such as user logon events, server access events, application access events, and data access events (step 1610). The raw data logs may be obtained from third party systems, such as SPLUNK, ARCSIGHT/HP, LOGLOGIC, HADOOP, SUMO LOGIC, LOGGLY, etc.
The Event Detector 1520 identifies applicable user events from the raw data logs and creates event logs (step 1620). The event logs are provided to the Session Manager 1530, which then supplements the event logs with additional data that provide further context for user events, as discussed above (step 1630). The Session Manager 1530 may obtain context information from local, on-site data sources (e.g., Active Directory), and may also obtain data from external data sources via the Internet or other network.
The Session Manager 1530 also tracks user behavior through a session (e.g., logon session) by grouping the enriched event logs by user and session (step 1640). Throughout a session, the Session Manager 1530 records the current state of a user in a session database. The Session Manager 1530 maintains the session and asset databases discussed above.
The Modeling and Detection module 1540 uses the session information to perform anomaly calculations needed by the Rules Engine (step 1650). In one embodiment, the Modeling and Detection module 1540 parses the rule expressions to determine the anomaly and confidence data required by the rule expressions. It performs the anomaly calculations (i.e., the values for the rule expressions), such as whether a data point is anomalous or the distance calculation between a data point and other data in a model. For each anomaly calculation, it also calculates a confidence factor for the behavior model data used in the anomaly calculations. In an alternate embodiment, the anomaly calculations are performed by the Rules Engine 1550.
The Modeling and Detection Module 1540 makes the anomaly and confidence factor calculation results available to the Rules Engine 1550 either by passing the results directly to the engine or storing them in a database accessible to the engine. The Rules Engine 1550 executes the risk score rules and determines which rules are triggered (step 1660). The Rule Session Manager 1560 keeps track of a risk score for each logon session (step 1670). If the risk score and session meet certain criteria, the Rule Session Manager 1560 displays an alert/notice regarding the session in the user interface (step 1680). In one embodiment, an alert is displayed if a session meets one of the following criteria:

    
    
        1. The session is a current session (i.e. it's on-going), and the risk score exceeds a threshold (e.g., a score of 90) for a duration longer than a specified period of time (e.g., 1 hour); OR
        2. The session has ended, and the final risk score for the session is above a threshold (e.g., 90)
    
    


The Session Manager 1560 may rank alerts displayed in the administrative interface based on risk scores.
If the risk score is below a threshold or otherwise considered normal, the Modeling and Anomaly Detection Module 1530 updates applicable user and group behavior models with the session data (step 1680).
In one embodiment, the Modeling and Anomaly Detection module 1540 parses the rules in the rules database to identify the type of data (e.g., the type of histograms) needed in the behavior model. As rules change, the Modeling and Anomaly Detection module 1540 updates the behavior models so that data for the rule expressions can be calculated. Also, the Session Manager 1530 may parse the rules to identify the context data to add to the event logs and determine where to retrieve such data (i.e., from local or external sources).
FIG. 17 illustrates an example of a user interface for the system, which would typically be viewed by an administrator, IT personnel, or other enterprise personnel responsible for monitoring security risks to the enterprise. For a session meeting certain criteria for an alert (e.g., the risk score exceeds a threshold), the user interface illustrates the events (e.g., 1710, 1720, 1730) in the session and a summary 1760 of the rules triggered by the events. The user interface includes a timeline 1740 that illustrates when applicable user events occurred.
Additional Session Tracking Methods
FIGS. 18-20 illustrate additional or alternate session-tracking methods. Specifically, these figures illustrate a method for associating network Information Technology (IT) events with user logon sessions in order to track user activity during logon sessions. As is the case with the session-tracking methods disclosed above, these methods enable the system to track user activity across device and account switches (if any). These methods may be used in conjunction with the security detection/assessment system and methods described above, but are not limited to such uses. They may be used in any context in which it is desirable to track user activity throughout a logon session.
A computer system for tracking user activity, such as Session Manager 1530, receives event logs corresponding to user activity in the network. In order to track user activity across a logon session, the system determines whether the event in the event log should be discarded, associated with an existing, open logon session, or associated with a new logon session, as illustrated in FIGS. 18a-18b. 
Referring to FIGS. 18a-18b, the session-managing system maintains a logon session state for each user logged onto the network, for instance as described above with respect to FIG. 4a (step 1810). A logon session state reflects the user, user accounts, host devices, and events associated with the logon session. The system also maintains a device state for each host device in the network (step 1830). The device state reflects all the users currently logged onto the host device and the user accounts used to logon to the host device. Concurrently, the system also monitors for new event logs (step 1820). Steps 1810, 1820, and 1830 are performed in parallel and continuously.
In response to receiving an event log, the system determines whether the subject event satisfies filter criteria (step 1835). In one embodiment, events in which the “user” is a computer are filtered out, such as a computer logon event or a domain controller remote access event. The purpose of these types of events is typically to inform the system of a change in IP address of a host or to provide a mapping of a server to an IP address. In response to the event satisfying the filter criteria, the system extracts any applicable information (e.g., IP address) and then discards the event (step 1840). If the event log includes IP address information for a device, the system updates the device state with the IP address. In one embodiment, all events, except those that indicate a security alert or a user accessing a host device are filtered out.
If event does not satisfy the filter criteria, the system identifies the following from the event log: (i) the device from which the event originated (“the originating device”), (ii) the host device accessed in the event (“the destination device”), and (iii) the user account used in the event (“the event user account”) (step 1845). The system then determines whether the event satisfies criteria for merging the event with an existing user logon session (step 1850). The merge criteria are discussed below with respect to FIGS. 19a-19b. If the event satisfies the merge criteria, the event is added to the applicable existing user logon session (step 1855). The destination device state is also updated to associate the applicable user and the user account with the device.
If the event does not satisfy the criteria for merging with an existing user logon session, the system determines whether the event satisfies criteria for a new logon session (step 1860). In one embodiment, an event satisfies the criteria for a new logon session if a user account that is not currently being used to logon anywhere is used to log onto a computer where nobody else is currently logged on. If the event satisfies the criteria for a new logon session, the system (i) identifies the user associated with the user account used in the event (from a mapping of user accounts to account owners), (ii) creates a new, open logon session for the identified user having a state that reflects the event and the use of the user account, and (iii) updates the state of the destination device to associate the identified user and the event user account with the destination device (step 1865).
If the event does not satisfy the filter criteria, the merge criteria, or the criteria for a new logon session, the system ignores the event or, alternatively, performs further processing on the event (step 1870).
FIGS. 19a-19b illustrate a method for determining whether the event should be merged into an existing user logon session. The system determines if the event log is a security alert (step 1902). In one embodiment, the system receives security alerts from third party security detection systems, such as FIREEYE alerts. If the event is a security alert, the system determines whether the security alert can be associated with a user (step 1903). A method for making this determination is described with respect to FIGS. 20a and 20b. If the event is a security alert that can be associated with a user, a merge condition is satisfied, and the system associates the alert with the existing logon session of the identified user (step 1904).
If the event is not a security alert (and was not filtered out earlier in step 1835), the event is a user-access event (e.g., the user accessing a host device), and the system determines whether the event can be associate with a user currently logged onto the originating device, as illustrated in steps 1910, 1915, and 1925. The event log for a user-access event will specify an originating device, if any, for the event, either by name, IP address, or other device-identifying information. For example, if a user logs onto Host Device B from Host Device A, the event log will indicate that Host Device A is the originating device and that Host Device B is the destination device.
The system identifies, the users currently logged onto the originating device and the user account(s) associated with the originating device (step 1910). This information is obtained from the state information for the originating device in the event. In step 1915, the system determines if the user account used in the event is also being used by one (and only one) user on the originating device. If so, a merge condition is satisfied and the event is associated with the identified user, as the system concludes that the same user continued to user the user account. If step 1915 evaluates to false, the system determines if only one user is currently logged onto the originating device with a different user account (step 1925). If so, a merge condition is satisfied and the event is associated with the identified user, as the system concludes that the user on the originating device switched user accounts to access the destination device (step 1930). For example, in the scenario illustrated in FIG. 21, the system will assume that “John” used the “Mary” account to access Host Device B (2120), as John is the only user logged on to the originating device (i.e., Host Device A (2110)). The system will also assume that John then used the “Mary” account to access Host Device C, as the “Mary” account is used by only one user (namely, John) on originating Host Device B (2120). Those skilled in the art will appreciate that the order of steps 1915 and 1925 can be switched, or the steps may be performed in parallel.
If steps 1915 and 1925 both evaluate to false, the system determines whether the event user account is being user anywhere else in the network by only one user (step 1935). If so, a merge condition is satisfied, as the system concludes the user is the same (step 1940), even though the state of the originating device does not indicate the user is logged onto the originating device (i.e., the rule assumes that such logon was missed by the system). Otherwise, no merge conditions are satisfied. Steps 1915, 1925, and 1935 effectively represent rules executed by the system to determine if the event represent a user account switch (1925) or continued user of an account by an existing user (1915, 1935).
FIGS. 20a-20b illustrate a method for determining whether a security alert can be associated with a user with an open logon session. A security alert event log may identify a user account associated with the alert and/or a host device on which the alert was triggered. If the security alert identifies a user account associated with an open logon session (step 2010), the system adds the security alert notice to the identified, open logon session (step 2020). If not, the system then determines whether the security alert includes identifying information for a host device, such as a host device name or IP address (step 2030). If so, the system determines if any users are logged onto the identified host device (step 2050). If either of steps 2030 and 2050 evaluates to false, the system discards security alert (at least with respect to session tracking) (step 2040 and 2060). In response to there being only one user logged onto the identified host device (step 2065), the security alert is added to that user's logon session state (step 2068). If there is more than one user logged onto the identified host device, the system randomly selects one of the users and adds the security alert notice to the user's logon session state (step 2070).
FIG. 22 is a block diagram that illustrates an example data structure for maintaining device state. This example illustrates device states with respect to the scenario illustrated in FIG. 21.
Device information is represented in a table or data object 2210a-c (“device object”). Each device object (or device row in a database table) points to or references the user accounts and users present on the device. Each user account and user is associated with a data object (2220a-c, 2230a-b) or other data structure that provides information about that account and user. In this example, John is logged into Host Device A using the “John” account. Therefore device object 2210a points to the “John” user account 2220a and associates the user account with user John 2230a. This association is for the purpose of Host Device A only, as the user account may be associated with another user on another device. The data object for each user account specifies the user(s) that are normally associated with the user account.
Device object 2210b for Host Device B points to the “Mary” and “Ted” user accounts (2220b, 2220c) as these user accounts were used to log into Host Device B. Although Mary is the user normally associated with the “Mary” account, in this case John 2230a is associated with the “Mary” account because the system determined that John switched user accounts in moving from Host Device A to Host Device B. Likewise, device object 2210c for Host Device C points to the “Mary” account 2220b, but associates the account with John 2230a, as the system concluded that John continued to use the “Mary” account in moving from Host Device B to Host Device C.
The system has access to a mapping of user accounts to names of account owners. Unless the system determines that there has been an account switch, the system will associate use of a user account with the owner(s) of the account. For example, if a user new logon session is started on Device X using the “Mary” account and Mary Smith is the owner of the account, the system will open a new logon session for Mary Smith, and the event will be added to the logon session. Likewise, the state of Device X will reflect that Mary Smith is logged onto the device using the “Mary” account.
The system adds user accounts and users to the device state in accordance with the methods described with respect to FIGS. 18a-b and 19a-b. A user account and corresponding user are removed from a device state if the user logs out or is inactive on the applicable device for a certain amount of time, such as, for example, five hours.
The methods described herein are embodied in software and performed by a computer system (comprising one or more computing devices) executing the software. A person skilled in the art would understand that a computer system has one or more memory units, disks, or other physical, computer-readable storage media for storing software instructions, as well as one or more processors for executing the software instructions.
As will be understood by those familiar with the art, the invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. Accordingly, the above disclosure is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the claims.

## Claims

1. A method, performed by a computer system comprising one or more computing devices, for associating IT activity events with user logon sessions in order to track user activity during a logon session, including device accesses and user account switches, the method comprising:
for each user logged onto the network, maintaining on a storage device a logon session state for the user that reflects events, host devices, and user accounts associated with the user during the logon session, wherein a logon session begins at a user's logon to the network and ends at the user's logout or a specified period of inactivity;
for each host device in the network, maintaining on a storage device a device state that reflects all users logged onto the host device and the user accounts used to logon to the host device;
performing the following steps on the one or more computing devices:
receiving an event log for a user event, wherein the event log indicates that a user has accessed a host device in the network (“the destination device”) from another host device in the network (“the originating device”);
identifying from the event log the destination device, the originating device, and a user account used for the event (“the event user account”); and
determining whether to associate the event with a user having an open logon session by performing the following:
identifying all users currently logged onto the originating device and all user accounts associated with the originating device;
determining whether the event represents a switch that has already occurred from a user account on the originating device to the event user account on the destination device by identifying whether only one user is currently logged onto the originating device and said one user is using a different user account on the originating device than the event user account;
in response to determining that the event represents a switch that has already occurred from a user account on the originating device to the event user account on the destination device, (i) associating the event with said one user, (ii) updating the logon session state of said one user to reflect the event, including the user account switch, and (iii) updating the state of the destination device to associate said one user and the user account with the destination device.

2. The method of claim 1, wherein the system also receives security alert events from external applications, and the system adds the security alert events to applicable logon session states.

3. The method of claim 2, wherein the system determines to which open logon session to add a security alert event by performing the following:
in response to the security alert event identifying a user with an open logon session, adding the security alert event to the identified user's logon session;
in response to the security alert event not identifying a user, performing the following:
in response to the security event including identifying information for a host device, identifying the host device and identifying the user(s) currently logged on to the host device;
in response to only one user being logged on to the host device, adding the security alert event to said one user's logon session; and
in response to more than one user being logged on to the host device, randomly selecting one user and adding the security alert event to the selected user's logon session.

4. The method of claim 1, wherein a risk assessment is calculated for each user logon session by executing rules on the state information for the user logon session.

5. One or more non-transitory computer-readable media comprising computer program software, that, when executed on one or more computing devices of a computer system, enables the computer system to perform the following method for associating IT activity events with user logon sessions in order to track user activity during a logon session, including device accesses and user account switches, the method comprising:
for each user logged onto the network, maintaining on a storage device a logon session state for the user that reflects events, host devices, and user accounts associated with the user during the logon session, wherein a logon session begins at a user's logon to the network and ends at the user's logout or a specified period of inactivity;
for each host device in the network, maintaining on a storage device a device state that reflects all users logged onto the host device and the user accounts used to logon to the host device;
performing the following steps on the one or more computing devices:
receiving an event log for a user event, wherein the event log indicates that a user has accessed a host device in the network (“the destination device”) from another host device in the network (“the originating device”);
identifying from the event log the destination device, the originating device, and a user account used for the event (“the event user account”); and
determining whether to associate the event with a user having an open logon session by performing the following:
identifying all users currently logged onto the originating device and all user accounts associated with the originating device;
determining whether the event represents a switch that has already occurred from a user account on the originating device to the event user account on the destination device by identifying whether only one user is currently logged onto the originating device and said one user is using a different user account on the originating device than the event user account;
in response to determining that the event represents a switch that has already occurred from a user account on the originating device to the event user account on the destination device, (i) associating the event with said one user, (ii) updating the logon session state of said one user to reflect the event, including the user account switch, and (iii) updating the state of the destination device to associate said one user and the user account with the destination device.

6. The one or more non-transitory computer-readable media of claim 5, wherein the system also receives security alert events from external applications, and the system adds the security alert events to applicable logon session states.

7. The one or more non-transitory computer-readable media of claim 6, wherein the system determines to which open logon session to add a security alert event by performing the following:
in response to the security alert event identifying a user with an open logon session, adding the security alert event to the identified user's logon session;
in response to the security alert event not identifying a user, performing the following:
in response to the security event including identifying information for a host device, identifying the host device and identifying the user(s) currently logged on to the host device;
in response to only one user being logged on to the host device, adding the security alert event to said one user's logon session; and
in response to more than one user being logged on to the host device, randomly selecting one user and adding the security alert event to the selected user's logon session.

8. The one or more non-transitory computer-readable media of claim 5, wherein a risk assessment is calculated for each user logon session by executing rules on the state information for the user logon session.

9. A computer system for associating IT activity events with user logon sessions in order to track user activity during a logon session, including device accesses and user account switches, the system comprising:
one or more computing devices further comprising:
one or more processors;
one or more memory units coupled to the one or more processors, wherein the one or more memory units store instructions that, when executed by the one or more processors, cause the system to perform the operations of:
for each user logged onto the network, maintaining on a storage device a logon session state for the user that reflects events, host devices, and user accounts associated with the user during the logon session, wherein a logon session begins at a user's logon to the network and ends at the user's logout or a specified period of inactivity;
for each host device in the network, maintaining on a storage device a device state that reflects all users logged onto the host device and the user accounts used to logon to the host device;
receiving an event log for a user event, wherein the event log indicates that a user has accessed a host device in the network (“the destination device”) from another host device in the network (“the originating device”);
identifying from the event log a destination device, an originating device, and a user account used for the event (“the event user account”); and
determining whether to associate the event with a user having an open logon session by performing the following:
identifying all users currently logged onto the originating device and all user accounts associated with the originating device;
determining whether the event represents a switch that has already occurred from a user account on the originating device to the event user account on the destination device by identifying whether only one user is currently logged onto the originating device and said one user is using a different user account on the originating device than the event user account;
in response to determining that the event represents a switch that has already occurred from a user account on the originating device to the event user account on the destination device, (i) associating the event with said one user, (ii) updating the logon session state of said one user to reflect the event, including the user account switch, and (iii) updating the state of the destination device to associate said one user and the user account with the destination device.

10. The system of claim 9, wherein the system also receives security alert events from external applications, and the system adds the security alert events to applicable logon session states.

11. The system of claim 10, wherein the system determines to which open logon session to add a security alert event by performing the following:
in response to the security alert event identifying a user with an open logon session, adding the security alert event to the identified user's logon session;
in response to the security alert event not identifying a user, performing the following:
in response to the security event including identifying information for a host device, identifying the host device and identifying the user(s) currently logged on to the host device;
in response to only one user being logged on to the host device, adding the security alert event to said one user's logon session; and
in response to more than one user being logged on to the host device, randomly selecting one user and adding the security alert event to the selected user's logon session.

12. The system of claim 9, wherein a risk assessment is calculated for each user logon session by executing rules on the state information for the user logon session.

13. The method of claim 1, wherein determining whether to associate the event with a user having an open logon session also comprises determining whether the event represents the continued use of the event user account from the originating device to the destination device by identifying whether one or more users are currently logged onto the originating device and only one of said users is using the event user account and in response to determining that the event represents the continued use of the event user account from the originating device to the destination device, performing the following steps:
associating the event with said one user,
updating the logon session state of said one user to reflect the event, and
updating the state of the destination device to associate said one user and the user account with the destination device.

14. The method of claim 13, wherein in response to not associating the event with a user currently logged onto the originating device, determining if the event user account is currently in use by only one user logged onto the network, and, in response to this determination evaluating to true, performing the following steps:
associating the event with said one user,
updating the logon session state of said one user to reflect the event, and
updating the state of the destination device to associate said one user and the user account with the destination device.

15. The method of claim 14, wherein in response to not being able to associate the event with a user with an open logon session, determining if the event meets criteria for a new logon session, wherein in response to the event meeting criteria for a new logon session, performing the following steps:
identifying a user associated with the user account used in the event,
creating a new, open logon session for said identified user having a state that reflects the event and the use of the user account, and
updating the state of the destination device to associate said one user and the user account with the destination device.

16. The method of claim 15, wherein in response to neither associating the event with an open logon session, nor the event not meeting criteria for a new logon session, ignoring the event or performing additional event processing on the event.

17. The one or more non-transitory computer-readable media of claim 5, wherein determining whether to associate the event with a user having an open logon session also comprises determining whether the event represents the continued use of the event user account from the originating device to the destination device by identifying whether one or more users are currently logged onto the originating device and only one of said users is using the event user account and in response to determining that the event represents the continued use of the event user account from the originating device to the destination device, performing the following steps:
associating the event with said one user,
updating the logon session state of said one user to reflect the event, and
updating the state of the destination device to associate said one user and the user account with the destination device.

18. The one or more non-transitory computer-readable media of claim 17, wherein in response to not associating the event with a user currently logged onto the originating device, determining if the event user account is currently in use by only one user logged onto the network, and, in response to this determination evaluating to true, performing the following steps:
associating the event with said one user,
updating the logon session state of said one user to reflect the event, and
updating the state of the destination device to associate said one user and the user account with the destination device.

19. The one or more non-transitory computer-readable media of claim 18, wherein in response to not being able to associate the event with a user with an open logon session, determining if the event meets criteria for a new logon session, wherein in response to the event meeting criteria for a new logon session, performing the following steps:
identifying a user associated with the user account used in the event,
creating a new, open logon session for said identified user having a state that reflects the event and the use of the user account, and
updating the state of the destination device to associate said one user and the user account with the destination device.

20. The one or more non-transitory computer-readable media of claim 19, wherein in response to neither associating the event with an open logon session, nor the event not meeting criteria for a new logon session, ignoring the event or performing additional event processing on the event.

21. The system of claim 9, wherein determining whether to associate the event with a user having an open logon session also comprises determining whether the event represents the continued use of the event user account from the originating device to the destination device by identifying whether one or more users are currently logged onto the originating device and only one of said users is using the event user account and in response to determining that the event represents the continued use of the event user account from the originating device to the destination device, performing the following steps:
associating the event with said one user,
updating the logon session state of said one user to reflect the event, and
updating the state of the destination device to associate said one user and the user account with the destination device.

22. The system of claim 21, wherein in response to not associating the event with a user currently logged onto the originating device, determining if the event user account is currently in use by only one user logged onto the network, and, in response to this determination evaluating to true, performing the following steps:
associating the event with said one user,
updating the logon session state of said one user to reflect the event, and
updating the state of the destination device to associate said one user and the user account with the destination device.

23. The system of claim 22, wherein in response to not being able to associate the event with a user with an open logon session, determining if the event meets criteria for a new logon session, wherein in response to the event meeting criteria for a new logon session, performing the following steps:
identifying a user associated with the user account used in the event,
creating a new, open logon session for said identified user having a state that reflects the event and the use of the user account, and
updating the state of the destination device to associate said one user and the user account with the destination device.

24. The system of claim 23, wherein in response to neither associating the event with an open logon session, nor the event not meeting criteria for a new logon session, ignoring the event or performing additional event processing on the event.