What is User and Entity Behavior Analytics?

In this article we are going to tell about the UEBA systems and reveal some their peculiarities. 

Before we proceed to examination of UEBA systems a short background overview is required. The predecessors of UEBA systems were UBA systems. UBA class system is the specific type of software, required for revelation and mitigation of insider threats UBA (User Behavior Analytics). The technology basically relies on machine learning and analytics. UBA focuses on identification and tracking of threat actors’ behaviors as intruders make their way through enterprise environments. This is implemented by putting data through a series of algorithms in order to identify activities that deviate from "normal" user behavior (User Behavior).

Data has yet turned into one of the most crucial and valuable asset in any organization and company worldwide. Implementation of adequate protection of sensitive and crucial data is one of crucial importance for any organization. It is worth mentioning, that insider related threats are often considered among the most significant ones. There are a few basic prerequisites for that: first of all, they are quite difficult to detect. What’s more, such threats often turn out to be the most destructive ones.  UBA is a useful tool, which helps to detect suspicious patterns which may indicate identity theft, fraud and other malicious activity.

Now let us focus on UEBA systems. UEBA (User and Entity Behavior Analytics) systems are in many terms similar to the UBA ones, however, there are some differences. UEBA class solutions are also aimed at monitoring of the way of operating, but, apart from UBA, they not only take into consideration user behavior patterns but also analyze "entities" operating peculiarities. Thus, the UEBA systems malicious behavior of both users and devices, applications and networks. 

In case with insider threats as well as in other cases UEBA systems help to mitigate the cybersecurity risks. User and entity behavior analytics (UEBA) is particularly reliable for identifying unknown and internal threats.

The basic principle of UEBA systems is the usage of machine learning algorithms, which identify a general line of normal user behavior and gathering data from numerous sources. This list often includes, but is not limited to:

• Servers, workstations and other devices’ logs
• Users’ messages in social networks, messengers, e-mail 
• Data, retrieved from other sources, such as browsers, anti-viruses, SIEM and DLP-class solutions

How do UEBA systems implement analytics? They use machine learning algorithms and statistical analysis to identify abnormal network activity. Basing on the gathered data and with the help of mentioned earlier methods the system can automatically compare actions with the forecasted normal behavior. Deviations are considered as a threat and an alert should be generated. 

Illustrative examples of UEBA use cases: an employee tries to access some files, which he/she has never interacted before. For instance, the files contain data, which is irrelevant for employee’s work duties. UEBA tool identifies the violation. Detecting security breaches, policy violations, abuse of privileges and other insider threats helps organizations to mitigate the damage from attacks faster and more efficiently.

Another use-case does not correlate with malicious insider activity, however, it is connected with a very common and crucial threat. It is typical if a hacker somehow obtains account credentials and uses them and misuses the access. In which way can UEBA help in this situation? As it monitors all ongoing activity and detects subtle differences in the behavior of employees, it will also monitor the new password owner’s behavior. To succeed, the intruder has to successfully imitate the employee’s behavior patterns.

Key benefits of UEBA: 

Although each solution, including cybersecurity ones, have its weaknesses and they should not be considered as panacea, there are some significant advantages, which this solution offers:

1. Automated threat detection: with the help of machine learning and behavioral analytics, the risks related to a very serious problem of skills shortage can be significantly mitigated. This means, for instance, dealing with such issues as:  detecting compromised accounts and brute force attacks / privilege changes / privileged account creation / data breaches.
2. Risks reduction: the technology helps to detect cases of credentials compromise at early stage. It is crucial for minimizing risks and prevention of data loss incidents.
3. Response time reduction: UEBA uses very accurate risk assessment to shorten response time to attacks. The faster a security team detects intrusion attempts, the more effective the response is.
4. Opportunity to avoid false positives: behavioral analytics also help avoid false alarms. Against the backdrop of an increasing threat workload, false alarms can be overwhelming for a security team. Dealing with a backlog of alerts is an ongoing challenge for many security operations centers. With the support of machine learning, security teams have more time and ability to focus on detecting activities that pose the greatest risk and give top priority to responding to these critical threats.