Components, best practices, and next-gen capabilitiesRead More
UEBA solutions build profiles that model standard behavior for users and entities in an IT environment, such as servers, routers and data repositories. This is known as baselining. Using a variety of analytics techniques, UEBA technology can identify activity that is anomalous compared to the established baselines, discover threats and detect security incidents.
Gartner defines UEBA solutions across three dimensions:
There is a close relation between UEBA and SIEM technologies, because UEBA relies on cross-organizational security data to perform its analyses, and this data is typically collected and stored by a SIEM.
In Gartner’s vision of a next-generation SIEM solution, a SIEM should include built-in UEBA functionality. The report lists the following as critical capabilities of a modern SIEM:
Advanced analytics–applying sophisticated statistical and quantitative models, such as machine learning and deep learning, on security log and event data to detect anomalous activity. Advanced analytics should complement the traditional rule and correlation-based analytics available in traditional SIEMs.
Here are a two types of insider threats:
A malicious insider is an employee or contractor with privileged access to IT systems, who intends to perform a cyber attack against the organization. It is difficult to measure malicious intent or discover it through log files or regular security events. UEBA solutions help by establishing a baseline of a user’s typical behavior and detect abnormal activity.
It’s common for attackers to infiltrate an organization and compromise a privileged user account or trusted host on the network, and continue the attack from there. UEBA solutions can help rapidly detect and analyze bad activities that the attacker carries on via the compromised account.
Traditional security tools find it difficult to detect a compromised insider if the attack pattern or kill chain is not currently known(such as in a zero day attack), or if the attack moves laterally through an organization by changing credentials, IP addresses, or machines. UEBA technology, however, can detect these types of attacks, because they will almost always force assets to behave differently from established baselines.
A SIEM collects events and logs from multiple security tools and critical systems, and generates a large number of alerts that must be investigated by security staff. This leads to alert fatigue, a common challenge of Security Operations Centers (SOC).
UEBA solutions can help understand which incidents are particularly abnormal, suspicious or potentially dangerous in the context of your organization. UEBA can go beyond baselines and threat models by adding data about organizational structure–for example, the criticality of assets and the roles and access levels of specific organizational functions. A small deviation from norm for a critical protected system or a top-level administrator, might be worth a look for an investigator; for a run-of-the-mill employee only a major deviation would receive high priority.
Data Loss Prevention (DLP) tools are used to prevent data exfiltration, or the illicit transfer of data outside organizational boundaries. Traditional DLP tools report on any unusual activity carried out on sensitive data–they create a high volume of alerts which can be difficult for security teams to handle.
UEBA solutions can take DLP alerts, prioritize and consolidate them by understanding which events represent anomalous behavior compared to known baselines. This saves time for investigators and helps them discover real security incidents faster.
UEBA can be especially important in dealing with Internet of Things (IoT) security risks. Organizations deploy large fleets of connected devices, often with minimal or no security measures. Attackers can compromise IoT devices, use them to steal data or gain access to other IT systems, or worse–leverage them in DDoS or other attacks against third parties.
Two sensitive categories of IoT are medical devices and manufacturing equipment. Connected medical devices may contain critical data, and may be life threatening if used directly for patient care. Manufacturing equipment can cause large financial losses if disrupted, and in some cases may threaten employee safety.
UEBA can track an unlimited number of connected devices, establish a behavioral baseline for each device or group of similar devices, and immediately detect if a device is behaving outside its regular boundaries. For example:
Some UEBA solutions rely on traditional methods to identify suspicious activity. These can include manually-defined rules, correlations between security events and known attack patterns. The limitation of traditional techniques is that they are only as good as the rules defined by security administrators, and cannot adapt to new types of threats or system behavior.
Advanced analytics, which is the hallmark of UEBA tools, involves several modern technologies that can help identify abnormal behavior even in the absence of known patterns:
Traditional analytics techniques are deterministic, in the sense that if certain conditions were true, an alert was generated, and if not the system assumed “all is fine”. The advanced analytics methods listed above are different in that they are heuristic. They compute a risk score which is a probability that an event represents an anomaly or security incident. When the risk score exceeds a certain threshold, the system creates a security alert.
The true power of a UEBA solution is in its ability to cut across organizational boundaries, IT systems and data sources and analyze all the data available for a specific user or entity.
A UEBA should analyze as many data sources as possible, some example data sources include:
For example, a UEBA solution should be able to identify unusual login via Active Directory, cross reference it with the criticality of the device being logged onto, the sensitiveness of the files accessed, and recent unusual network or malware activity which may have enabled a compromise.
A UEBA solution learns normal behavior to identify abnormal behavior. It examines a broad set of data to determine a user’s baseline or behavioral profile.
For example, the system monitors a user and sees how they use a VPN, at what time they arrive to work and which systems they log into, what printer they use, how often and what size of files they send by email or load to a USB drive, and many other data points that define the user’s “normal behavior”. The same is done for servers, databases or any significant IT system.
When there is deviation from the baseline, the system adds to the risk score of that user or machine. The more unusual the behavior, the higher the risk score. As more and more suspicious behavior accumulates, the risk score increases until it hits a threshold, causing it to be escalated to an analyst for investigation.
This analytical approach has several advantages:
More context–traditional correlation rules defined by security administrators may have been correct for one set of users or systems, but not for others. For example, if a department starts employing shift workers or offshore workers, they will start logging in at unusual times, which would trigger a rule-based alert all the time. UEBA is smarter because it establishes a context-sensitive baseline for each user group. An offshore worker logging in at 3am local time would not be considered an abnormal event.
When analyzing security incidents, the timeline is a critical concept which can tie together seemingly unrelated activities. Modern attacks are processes, not isolated events.
Advanced UEBA solutions can “stitch” together data from different systems and event streams, to construct the complete timeline of a security incident.
For example, consider a user who logged in, performed suspicious activity and then disappeared from the logs. Was the same IP used to connect to other organizational systems shortly afterwards? If so, this could be part of the same incident, with the same user continuing their attempt to penetrate the system. An additional example could be an attacker logging in to the same machine multiple times using different credentials. This also requires “stitching” together data about the various login attempts and flagging them as a single incident.
Once a UEBA solution stitches together all relevant data, it can assign risk scores to any activity along the event timeline.
Gartner’s vision of an integrated SIEM and UEBA solution is today a reality. Several systems are deployed in the field which combine the breadth of data in a SIEM with the deep analytics made possible by cutting-edge UEBA engines.
One example of an integrated system is Exabeam’s Security Intelligence Platform. Exabeam is a full SIEM solution based on modern data lake technology. In addition, it provides the following UEBA capabilities:
Components, best practices, and next-gen capabilitiesRead More
How SIEMs are built, how they generate insights, and how they are changingRead More
SIEM under the hood - the anatomy of security events and system logsRead More
User and Entity Behavioral Analytics detects threats other tools can’t seeRead More
Beyond alerting and compliance - SIEMs for insider threats, threat hunting and IoTRead More
From correlation rules and attack signatures to automated detection via machine learningRead More
Security Automation and Orchestration (SOAR) - the future of incident responseRead More
A comprehensive guide to the modern SOC - SecOps and next-gen techRead More
Evaluation criteria, build vs. buy, cost considerations and complianceRead More
SIEM Essentials QuizRead More