ABCs of UEBA Archives - Page 2 of 3

ABCs of UEBA

ABCs of UEBA: N is for NETWORK

When it comes to detecting & responding to cyber threats, the network is the first line of defense. Network threats are best monitored with ML on netflows.

ABCs of UEBA: M is for Machine Learning

There are two main classes of machine learning algorithms: unsupervised learning algorithms and supervised learning algorithms.

ABCs of UEBA: L is for LOGS

Logs are the lifeblood of User and Entity Behavior Analytics platforms. The more relevant log data ingested, the better the efficacy of the analytics.

ABCs of UEBA: K is for Known

One of the most valuable capabilities of a UEBA solution is its ability to predict, detect and stop known threats and unknown threats.

ABCs of UEBA: J is for JSON

Gurucul offers open analytics on open choice of big data using open and standard REST JSON APIs for data ingestion and integration.

ABCs of UEBA: I is for Insider Threat

Insider threat detection is a top use case for UEBA. You detect malicious insiders by monitoring their behavior – to notice when it becomes anomalous.

ABCs of UEBA: H is for Hijacking

Account compromise is on the rise. When an email address gets hijacked by a criminal, the hacker uses the compromised email account to impersonate the user.

ABCs of UEBA: G is for Gurucul

Gurucul was developing User Behavior Analytics technology long before Gartner coined the term UBA in 2014 and then updated it to UEBA in 2015.

ABCs of UEBA: F is for Fraud

UEBA powered by machine learning can be used to analyze millions of datapoints from a variety of siloed, cross-channel sources to detect and prevent fraud.

ABCs of UEBA: E is for Entity

The “E” in UEBA stands for “Entity” – User and Entity Behavior Analytics. What do we mean by Entity? And, what do we mean by Entity Behavior Analytics?

ABCs of UEBA: D is for Data

The volume of security data UEBA solutions must contend with is mind blowing. Learn the V's of Big Data and how it's leveraged by UEBA.

ABCs of UEBA: C is for Context

The biggest challenge that enterprises face today is that analysts get too many alerts with very little context which results in no actionable intelligence.