The User and Entity Behavior Analytics (UEBA) tools measure the behavior of people and hardware within a network to identify abnormal behaviors and alert security personnel.
With machine learning, these tools are used to identify patterns and monitor the behavior of users and machines, notifying stakeholders of abnormal behavior, malicious behavior, or performance problems that might occur due to mistakes or inordinate actions.
UEBA tools were developed because malicious activities and attack techniques are becoming increasingly complex. Protecting information and networks cannot be entirely achieved with prevention.
There must be another method of detecting an intrusion after an attacker has slipped in. A change in user behavior can be detected by user behavior analytics, which can pinpoint compromises accurately.
What are the features of UEBA tools?
UEBA utilizes modern technology like machine learning, algorithms, and statistical analysis to create a baseline level of normal behavior for each user.
The system continuously analyzes and learns about different types of behavior and compares it to what is normal for that kind of behavior. The anomaly is detected when it detects deviations from the normal and is notified to the administrator.
Here are the 5 best UEBA Tools for 2022.
1. Microsoft ATA
The Azure Advanced Threat Analytics (ATA) application provides learning-based analytics monitoring capabilities for users, entities, and activities.
Active Directory is used to protect user credentials and identities. Furthermore, the software allows for the fast triaging and remediation of incidents, including identifying and investigating suspicious user activity.
A leader in data protection, edge protection, and user protection, Forcepoint creates security solutions that keep your data, edge, and users safe.
These products integrate with most existing environments and can be used together. Users are identified and stopped when their activity is tracked in the cloud by dynamic user protection.
With behavioral analytics, entity risk can be identified and investigated rapidly based on context-rich risk scores.
It is a software program that enables data collection and forensics for workforce protection and monitoring visibility into user actions.
Teramind monitors and prevents data loss by tracking user activity, collecting data about user behavior, and analyzing user behavior.
Compliance with policies and regulations can be ensured by monitoring and controlling user activity. Users can detect behavior anomalies from their user behavior analytics tools in real-time and uncover potential threats.
Varonis provides software that detects insider threats and data security. The solution provides tools that protect data, document compliance, detect threats, and respond to them.
As a threat detection tool, UEBA plays a critical role. This tool analyzes behavior across multiple platforms using predictive threat models. These processes are all automated, and a potential attack is alerted to the admin.
On on-premises and cloud platforms such as AD, Windows, SharePoint, Exchange, Office365, Unix/Linux, Dell EMC, HPE, and Box, it can detect CryptoLocker infections compromise service accounts, and disgruntled employee behavior.
5. IBM QRadar
QRadar User Behavior Analytics from IBM detects malicious insiders by analyzing user activity. When the software detects compromised credentials, it alerts administrators.
Security analysts can identify risky users and strange behavior by using this QRadar Security Intelligence Platform component and drilling down to access flow data and log data logging.
User context is added to the network, log, vulnerability, and threat data through behavioral rules and machine learning models to help display and detect attacks quickly.
Organizations use behavioral analytics tools for data protection, analytics, and statistics. Businesses need to strongly consider behavioral analytic tools in light of the increasing number of hacking incidents.
The primary purpose of behavior analytics tools is to identify potential threats based on detected anomalies in user behavior and data usage, network events, and typical behavior patterns.
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