Patent Grant
11178168
This invention relates generally to security analytics in computer networks, and, more specifically, to a self-learning threat detection system for multi-domain data.
Organizations are faced with the ever-increasing risks from security threats. Some cyberattacks are perpetrated by outsiders, while others involve insiders. The insider threats are particularly hard to guard against, whether they are from someone using their legitimate access to data for nefarious purposes or conducting network activities in ways that violate acceptable user policies. Since the malicious activities and methods from an insider or an external adversary are always changing, traditional means of detecting threats that match data against blacklisted signatures or hand-crafted correlation rules are largely ineffective and irrelevant. In recent years, behavior-based anomaly detection systems have emerged to address these security threats. In particular user and entity behavior analytics (UEBA) provide a viable approach to detecting behavior anomalies. An example of a UBA/UEBA cybersecurity monitoring system is described in U.S. Pat. No. 9,798,883 issued on Oct. 24, 2017 and titled “System, Method, and Computer Program for Detecting and Assessing Security Risks in a Network,” the contents of which are incorporated by reference herein.
Current UEBA systems rely on risk rules to identify cybersecurity threats. A session is evaluated to see whether the criteria of any rules are satisfied using current session data and behavior models that encompass historical network data. In most cases, a rule is triggered when an anomaly specified by the rule is detected in a user session. Each rule is associated with expert-assigned risk points, and, when a rule is triggered in a user session, the risk points associated with the rule are added to the user's session risk score. The user's session risk score is based on the cumulative sum of the risk points.
While this is can be effective way to detect cybersecurity threats, the number of rules in a UEBA system tends to grow over time as new types of threats and ways to engage in malicious activity are discovered. The increasing number of rules results in risk score inflation and an increased number of alerts. To counter this, the system often requires manual tuning and adjusting of risk scores. Furthermore, while-rule based systems use behavior models to determine whether an event is anomaly, they require a human to assign risk points to detected anomalies. These points must be updated over time.
Moreover, enterprise security products generate a large variety and volume of entity or user activity event data of different categories or “domains.” Example activity domains include asset logon and access, account management, virtual private network (VPN), email, proxy/firewall, and physical access, etc. What may be activity of high interest in one domain may be of low interest in another domain with respect to risk assessment.
Therefore, in certain environments, there is demand for a UEBA system where the risk score is not based on a cumulation of human-assigned risk points associated with triggered rules, but is instead a probability-based model based on behavior profiles that factors in the domain and context in which an event occurs and that enables the system to self-learn the risks associated with observed events.
The present disclosure describes a self-learning system, method, and computer program for detecting cybersecurity threats in a computer network based on anomalous user behavior and multi-domain data.
A computer system tracks user behavior during a user session across multiple data domains. For each domain observed in a user session, a domain risk score is calculated. The user's session risk score is then calculated as the weighted sum of the domain risk scores.
A domain risk score is based on learned individual event-level risk probabilities and a learned session-level risk probability from the domain. The individual event-level risk probabilities and a session-level risk probability for a domain are derived from user events of the domain during the session and from historical event and session data for the domain. Consequently, unlike most of rule-based systems, the user's session risk score is not cumulative sum of expert-assigned risk points associated with individual triggered rules, but instead it is based on cumulative learned risk probabilities calculated on a per-domain basis.
An alert is generated for sessions with risk scores above a threshold. In certain embodiments, a domain diversity factor is used to preference, in terms of risk ranking, sessions in which rarer domains or rarer combinations of domains are observed.
In certain embodiments, each domain is associated with a unique set of event-feature indicators comprising event-level anomaly indicators and event-level context indicators. In such embodiments, the event-level risk probability for an event of a domain is a Bayes risk probability assessed based on the evaluated values of the domain's event-feature indicators for the event, as well as on historical event data for the domain. The historical event data for the domain includes an individual user's historical event data in the domain and historical event data in the domain for the global (network-wide) user population.
Likewise, each domain may be associated with a unique set of session-feature indicators comprising session-level anomaly indicators and session-level context indicators. In such embodiments, the session-level risk probability for a domain is a Bayes risk probability assessed based on the evaluated values of the domain's session-feature indicators as a result of events of the domain during a user session. The session-level Bayes risk probability is also based on historical individual and global session data for the domain.
One of the advantages of this method is that the system does not require humans to determine and periodically update the level of risk associated with an event or an anomaly (i.e., no expert-assigned risk points for triggered rules). Rather, since event-level and session-level risks are calculated as a probability based on the user's current and historical observed behavior in a domain, as well as the global population's historical observed behavior for the domain, the system is able to learn the risks itself and improve its understanding of risks as it receives more data over time. In other words, network data drives risk assessments, not human-assigned risk points. This often leads to more accurate risk calculations for user sessions.
Another advantage of this method is that, since a user's session risk score is based on individual event-level and session-level risk probabilities for domains and not risk points associated with triggered rules, increasing the number of event-feature indicators and session-feature indicators over time does not create risk score inflation, unlike in a rule-based system where the session score would increase with more rules added over time. Instead, it likely increases the accuracy of the event-level risk probabilities and session-level probabilities.
In certain embodiments, the Bayes risk probabilities may be calculated more computationally efficiently by dividing event-feature indicators for a domain into a plurality of groups to obtain a subset of event-feature indicators for each group and then calculating probabilities associated with a Bayes risk for each of the groups. The same may be done with respect to session-feature indicators.
The present disclosure describes a self-learning system, method, and computer program for detecting cybersecurity threats in a computer network based on anomalous user behavior, where user events are tracked across multiple network domains. The method is performed by a computer system that detects cyber threats in a network and performs a risk assessment of user network activity.
1. Method for Detecting Cybersecurity Risks Using Multi-Domain Data
Data domains are denoted herein as Cv, where v is the domain index, 1≤v≤V, and V is the total number of domains from which user activity is monitored. A user session comprises a collection of a user's observable events {etv}, where t is the event index (1≤t≤T), T is the total number of events observed in the session, and v is the domain to which the event belongs.
For each domain observed in a user session (i.e., for each domain in which at least one user event is observed in the session), the system calculates a domain risk score as the sum of the individual event-level risk probabilities (“event-level risks”) and a session-level risk probability (a “session-level risk”) from the domain (step 120). The individual event-level risks {Re
Where α allows some flexibility in tuning the relative contributions of event-level and session-level risk. RC
The system then calculates a preliminary session risk score for the user session by summing the domain risk score(s) for the domain(s) observed in the user session (step 130). The preliminary session risk score Rprelim_total may be expressed mathematically as follows:
The system weights the preliminary risk score by a domain diversity factor WD to calculate a final risk score for the user session (step 140). This final risk score may be expressed mathematically as:
Rtotal=Rprelim_total*WD
In certain embodiments, the session risk score is updated in substantially real time throughout a session as the system receives information about user events. In one embodiment, the domain diversity factor is used to preference, in terms of risk ranking, sessions in which rarer domains or rarer combinations of domains are observed. Occurrences of domains that are statistically dependent upon each other are less surprising than domains that are independent from one another. Therefore, all other factors being equal, a combination of independent domains provides more information and is of higher interest than a combination of domains that are relatively dependent on each other.
The system generates an alert if the session risk score exceeds a threshold (step 150). For example, the system may generate an alert for sessions with the highest 5% risk scores. The alert may be displayed in a user interface for the system, typically for the benefit of IT personnel. The alert display may include a list of the events in the applicable user session or a summary of the events most responsible for the elevated risk. The alert display may include a timeline of key events in the user session.
2. Calculating an Event-Level Risk Score for an Event of a Domain
2.1 Event-Feature Indicators
In one embodiment, the individual event-level risk Re
2.1.1 Anomaly Indicators
An event-level anomaly indicator indicates whether an aspect or a data element of an event is unusual with respected to a tracked entity behavior profile. A behavior profile is a historical description of the tracked data that is continuously updated. For example, a behavior profile may be in form of a histogram.
Where the tracked data is categorical (e.g., types of assets accessed by a user), the behavior profile may be time-based with the most recently accessed times tracked for the values of the data. For example, a behavior profile tracking a user's accessed assets comprises a list of tuples of an asset and the timestamp associated with the most recent access of the asset.
Where the tracked data is numerical (e.g., email size), the behavior profile may be a description of past observed values via a boxplot method or a parametric probability function. For example, a behavior profile tracking the size of emails a user sends may use a boxplot to describe the interquartile range of the sample.
Behaviors tracked by profiles are simple to understand and useful for interpretability. In certain embodiments, the system builds a comprehensive set of behavior profiles for all domains per user, as well as per user's peer group and per the entire organization. A user may belong to multiple peer groups. For example, he/she may belong to a department peer group, a title peer group, a geo-location peer group, a security-access peer group, etc.
In one embodiment, the value of an event-level anomaly indicator is either TRUE (1) or FALSE (0), depending on whether the current data is anomalous. The value of an anomaly indicator may be determined by an anomaly detector, which is a software routine that checks whether a current data item is considered new, unusual, or an outlier based on the applicable behavior profile. In the case of categorical data, an anomaly detector returns TRUE if the data is new to the profile or was last seen more than N days ago, where a TRUE value means the current data (i.e., the event data) is anomalous. In certain embodiments, N=30. However, if the data in question is new to the entire user population (e.g., a newly-deployed asset in the enterprise is accessed for the first time), the anomaly detector does not consider this an anomaly. The data in a behavior profile must be matured or “converged” before it is used for anomaly evaluations. In certain embodiments, the data has converged if it has aged more than M days (e.g., M=5).
In the case of numerical data, the anomaly detector returns TRUE if the data is considered an outlier that exceeds a certain threshold in a profile distribution.
2.1.2 Context Indicators
In addition to the event-level anomaly indicators, static and factual context data serve as features of an event for risk scoring. Context data is available from various IT data sources, such as a Lightweight Directory Access Protocol (LDAP) database or other IT operation database. Examples of context data include a user's privileged access status over certain assets and whether an asset is a workstation or a server.
An event-level context indicator indicates whether or not a certain fact is true for the event. In assessing the risk of an event, the role of context indicators is to provide context to calibrate the degree of risk due to positive anomaly indicators. For example, the risk due to a user running an unusual process is best calibrated by whether the machine on which the process is run is a critical server or a workstation.
2.1.3 Using Anomaly Indicators and Context Indicators to Assess an Event-Level Risk
3. Calculating a Session-Level Risk Score Based Events of a Domain
In one embodiment, for each domain observed in a user session, the session-level risk Rsv for the domain Cv is a Bayes risk based on a vector of session-feature indicators derived from events of the domain Cv in the user session and from historical session data for the user and the global network population in the domain. Each domain is associated with a unique set of session-feature indicators. Similar to event-feature indicators, session-level feature indicators comprise session-level anomaly indicators and session-level context indicators. An example of session-level context indicator is whether the session is a VPN session. Events from a session are aggregated to build session-based behavior profiles and session-level anomaly indicators for each domain in the network. Examples of session-level behaviors tracked and assessed for anomalies are the number of bytes transferred into a user session or the number of assets accessed in a session. The value of a session-level anomaly indicator is determined by an anomaly detector, which is a software routine that checks whether a current data item is considered new, unusual, or an outlier in accordance with the applicable session-level behavior profile.
4. Bayes Modeling
This section sets forth a method for calculating a Bayes risk. The method applies to both event-level and session-level risks.
With respect to the Bayes formulas below, let R denote either an event-level risk Re
R is defined as the user's posterior probability that the event or session is malicious or of high interest given the feature vector. “Malicious or of high interest” is denoted as M, and “legitimate or of no interest” is denoted as L. Therefore, R is computed as follows:
Where:
P(M|{right arrow over (f)}) is the posterior probability that the event or session is malicious or of high interest given the feature vector {right arrow over (f)};
P({right arrow over (f)}|M) is the likelihood of observing the feature vector in a malicious/high interest event or session.
P({right arrow over (f)}|L) is the likelihood of observing the feature vector in a legitimate/low interest event or session.
P(M) is the likelihood of a malicious/high interest event or session;
P(L) is the likelihood of a legitimate/low interest event or session.
In one embodiment, P(L) and P(M) are assumed to be of equal likelihood and are set to 0.5.
In one embodiment, in computing P({right arrow over (f)}|L) in training, it is assumed that the observed historical data is largely normal and legitimate and that the volume of any present malicious or anomalous events is negligible and statistically insignificant for the purposes of the risk calculation.
The data used to computer P({right arrow over (f)}|L) is the user's local sufficient statistics smoothed by the population's global sufficient statistics:
Countu({right arrow over (f)}) is the count of sessions from the user's own historical session in which the feature vector {right arrow over (f)} is observed.
Σ{right arrow over (f′)}Countu({right arrow over (f)}′) is the sum of all such counts from all possible observed feature vectors with the same user's history.
Countg({right arrow over (f)}) is the the count of sessions from all users in which the feature vector {right arrow over (f)} is observed.
Σ{right arrow over (f′)}Countg({right arrow over (f)}′) is the sum of all Countg({right arrow over (f)}) counts from all possible observed feature vectors across the user population.
C0 is a scaling factor so that the mass of local sufficient statistics has 1-to-1 equal to that of the global sufficient statistics.
β is a parameter between 0 and 1 controlling the smoothing in the population-global vs. user-local sufficient statistics.
As seen from the above formula, a domain risk is learned from both the user's current and historical observed events in the domain and the historical activity of the wider (global) user population in the domain.
The likelihood P({right arrow over (f)}|M) is assumed to be uniform across all observed featured vectors, unless preferred otherwise by expert assignment or analyst feedback. Indeed, the Bayes interference scheme allows expert opinion to adjust, and user feedback to update, P({right arrow over (f)}|M) in a production setting. In absence of expert knowledge or analyst feedback, the risk in the above equation simply reflects the degree of rarity or anomalousness of the feature vector in predicting M.
5. Computationally Efficient Method for Calculating P({right arrow over (f)}|L) and P({right arrow over (f)}|M)
As indicated above, to compute a Bayes event-level risk, the system evaluates a likelihood of observing the applicable event in a legitimate session. In other words, the system computes P({right arrow over (f)}|L)) where {right arrow over (f)} is an event-feature vector. While it is possible to calculate the likelihood directly regardless of the size of {right arrow over (f)}, it is more computationally efficient to exploit the conditional independence of event-feature indicators.
The event-feature indicators are divided into a plurality of groups to obtain a subset of event-feature indicators for each of the groups (step 410).
Where there are J conditional independent groups {right arrow over (g)}i, each consisting of a non-overlapping subset of features {right arrow over (f)}.
The benefit of organizing event-feature indicators into independent groups are at least twofold. First, because each group has a manageable smaller number of feature indicators, the data fragmentation that comes with jointly estimating the likelihood of a large number of features is reduced. Second, structuring the features to conditionally independent groups improves the interpretability of the outcome. Given a high scoring session, rather than presenting the entire feature f, where size l may be large, the system can selectively present only the critical subset of event-feature indicators that significantly contributed to the session score, improving the readability of the outcome.
In one embodiment, the following guidelines are followed in dividing event-feature indicators into groups:
The method described in this section can also be applied to computing P({right arrow over (f)}|L)) for session-level risks too by dividing session-level indicators into groups.
To compute a Bayes event-level risk, the system also evaluates a likelihood of observing the applicable event in a malicious session. The method described in this section also can be applied to calculating P({right arrow over (f)}|M)) for both event-level risks and session-level risks.
6. Precomputing Event-Level and Session-Level Risks in a Training Phase
In one embodiment, during a training phase, an event-level Bayes risk Re
7. Domain Diversity Factor
In one embodiment, the domain diversity factor is used to preference, in terms of risk ranking, sessions in which rarer domains or a rarer combination of domains are observed. For example, the domain diversity factor may be computed as follows:
Where Cv is the vth domain in which an of the anomaly indicators from the domain are TRUE in a user session. Under this calculation, WD has the property that when some domains are dependent to one another, it will be less than 1.0, decreasing the session risk score. Otherwise, when domains observed in the session are truly independent such that they provide maximum information in a session, WD, will be 1.0, preserving the session score.
8. General
The methods described with respect to
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 following claims.
This application claims the benefit of U.S. Provisional Application No. 62/782,824, filed on Dec. 20, 2018, and titled “BEAM: An Anomaly-Based Threat Detection System for Enterprise Multi-Domain Data,” the contents of which are incorporated by reference herein as if fully disclosed herein.
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