User and Entity Behavior Analytics (UEBA) is a new category of security solutions that use innovative analytics technology, including machine learning and deep learning, to discover abnormal and risky behavior by users, machines and other entities on the corporate network.
UEBA can detect security incidents that traditional tools do not see, because they do not conform to predefined correlation rules or attack patterns, or because they span multiple organizational systems and data sources.
What is User and Entity Behavior Analytics (UEBA)?
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.
Below are some Cases in which UEBA can help keep you safe:
An employee or contractor who has privileged access to the IT network who intends to do a cyberattack on the organization he is working for. These kinds malicious attacks are hard to predict or discover through log files and regular security events.
When Cybercriminals attack an organizations IT infrastructure, they usually try to compromise certain users with high privileges to continue their attacks on the network. This is all the more difficult to detect from traditional security tools from threats that are currently unknown, such as zero-day attacks.
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).
Data Loss Prevention (DLP) and Data Leakage Prevention
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.
Entity Analytics (IoT)
Organizations are now taking advantage of todays mobility by deploying large fleets of connected devices, but they don’t consider the security risks it can bring. Attackers can use these devices in multiple ways such as to steal data, gain access to other parts of your network or even use them in their next attacks. This situation is even more so in the healthcare and manufacturing industry as their machines contain valuable data and if compromised can cause critical machines to fail.
Exabeam’s Advance analytics fully integrates with your SIEM to help you solve the cases above using the following features:
Timeline Analysis and Session Stitching
Timelines are a crucial thing when you are analyzing security incidents, this is so that you can tie together seemingly unrelated activities to complete a story. Modern attacks are a process now, not an isolated event that is black and white. Advance Analytics can “stich” together the different events from all the logs of each of your tools and infrastructure to help you get a complete picture of your security incidents.
Rule and signature-free incident detection
Exabeam uses advanced analytics to identify abnormal and risky activity without predefined correlation rules or threat patterns. It provides meaningful alerts without requiring heavy setup and fine tuning, and with lower false positives.
Automatic timelines for security incidents
Exabeam can stitch together related security events into a timeline that shows a security incident, spanning multiple users, IP addresses and IT systems.
Customizable Case Management Designed for Security Teams
Maintaining a SOC operation can be costly, it involves allocating resources and knowing which incidents need to be prioritized while being able to investigate and mitigate those that could interrupt business operations. Exabeam’s UEBA security solution, you’ll be able to automate these tasks leaving more time for your staff to do more with less time. This effectively helps you decrease your mean time to resolution (MTTR).
Dynamic Peer Grouping
Exabeam not only performs behavioral baselining of individual entities, it also dynamically groups similar entities (such as users from the same department, or IoT devices of the same class), to analyze normal collective behavior across the entire group and detect individuals who exhibit risky behavior.
Lateral Movement Detection
Exabeam detects attackers as they move through a network using different IP addresses, credentials and machines, in search of sensitive data or key assets. It ties together data from multiple sources to connect the dots and view the attacker’s journey through the network.