User and Entity Behavior Analytics Solutions (UEBA)

What is User and Entity Behavior Analytics (UEBA)?

UEBA is a method that intercepts and identifies the behavior of users and equipment inside a network. UEBAs are a new category of security solutions that use innovative analytics technologies, including machine learning and deep learning, to detect abnormal and risky behavior by users, machines and objects on the network. This solution can be used a lot by network administrators. in this article we discuss User and Entity Behavior Analytics Solutions and its features.

In UEBA, like Arcon UBA license or Splunk license for UBA, security events and equipment are not tracked in traditional ways. Instead, users and equipment of a system are tracked. Also, UEBA systems are used to detect unknown attacks. It is possible to steal an employee’s username and password, but imitating a person’s normal behavior within the network is almost impossible.

Working method

Collect information from multiple sources

One of the strengths of UEBA systems is their ability to cross organizational boundaries, IT systems, data sources and analyze all available data for a specific user or entity. for example:

• Active Directory
• Human resources data
• Access systems such as VPNs and proxies
• Anti-malware and antivirus systems, firewalls, intrusion detection and prevention systems (IDPS)

Considering the risk number for each activity

To the extent that, in the example of user account theft, there is a deviation from the user’s registered behavior pattern, the Pentra system adds to the risk number of that user or machine. The more unusual the behavior, the higher the risk

behavior pattern

A hacker who has stolen someone’s password and username cannot act exactly like that person on the system unless they have done extensive research and training. So, when that person logs in with that username, and the hacker’s behavior is different from that user’s normal behavior, that’s when the system’s anomaly alerts start.

Identify cyber attacks and intra-organizational threats with Splunk UBA

The Splunk User Behavior Analytics technology, or Spunk UBA for short, which is used to analyze the behavior of Splunk users, has many features, some of the most important of which are mentioned below:

• Simple to use for SOC analysts, incident investigators and SEIM managers
• Increasing the effectiveness of security analysts by prioritizing threats and avoiding false positive evaluation
• Improving the process of identification and identification of cyber attacks and known, unknown and hidden internal threats

Simple threat workflow

This technology reduces billions of raw events to a few thousand anomalies and then to a few dozen threats, so that the process of reviewing and providing a solution can be done quickly. It uses algorithms based on security and semantics for machine learning, dynamic statistical methods as well as correlations to identify hidden threats without human analysis. Also, knowing the concepts, situations and content can reduce false positives.

Review and identify threats

This technology sorts threats visually and identifies abnormal and suspicious routes and frequencies. It identifies critical threats using advanced correlations in models and uses individual and adaptive learning algorithms (machine and statistical learning). Therefore, it will be able to actively investigate the threats and evidence related to them.

Splunk UBA Use Cases

Splunk continues to provide the most compelling solution for security analytics by describing the entire attack lifecycle (cyber attack or intra-organizational threat as well as providing a platform for detection, response and automation).

IP theft and unauthorized transfer of data (Data Exfiltration)

Identifies evidence of unauthorized data transfer within the organization from assets or users.

Cloud Asset and Virtual Container

It performs the process of determining behavioral principles, characterizing unusual cases, and identifying threats for virtual containers and cloud applications.

Detection and identification of fraud

Behavioral modeling is performed on transactions and automatic threat modeling is performed to identify fraud-related activities.

Suspicious behavior: users, equipment and applications

Identifying threats and anomalies related to an organization’s users and entities with User Entity Behavioral Analysis (UEBA)

Hacking user accounts and abusing special user accounts

It quickly identifies user accounts under threat and creates a complete view of threats related to privileged accounts.

User and Entity Behavior Analytics Solutions key features

• Identify internal threats
• Detect brute-force attacks
• Detection of Fileless attacks
• Detection of Zero-Day Attacks
• Identify compromised accounts
• Detection of Data Exfiltration attacks
• Detection of breaches in protected data
• Detection of telecommunication fraud and abuse
• Detect changes in permissions and create super users

The above-mentioned items are only part of the applications and features of using UEBA systems. According to published reports, over the next three years, efficient UEBA platforms will become popular systems for security operations and attack detection. Even now, detecting security events and analyzing attacks will be much easier with UEBA systems than with many current security monitoring systems.

The difference between UEBA and SIEM

Security Information and Event Management (SIEM) licensed systems are a complex set of tools and technologies that provide an overall view of an IT system’s security. Using event data and information, this system allows the organization to observe natural patterns and trends and warns you if there are abnormal trends and events. Interestingly, UEBA does the same thing, except that it uses user behavior information (and entities) to distinguish between normal and abnormal cases.

The bottom line is that SIEM is rule-based, and advanced hackers can easily exploit or bypass these rules. Furthermore, SIEM rules are designed to immediately detect threats occurring in real-time, whereas advanced attacks typically take months or years. However, rules-based UEBA does not include attack signatures. Instead, it uses advanced risk calculation techniques and algorithms to detect anomalies over time.

One of the best practices for IT security is to use SIEM and UEBA at the same time for greater security and the ability to better detect anomalies.