In this four-part series, Combating threats with UEBA, we explore hypothetical cyberattacks inspired by real-life events in four different industries: healthcare, finance, manufacturing, and education. We’ll take a look at unforeseen security attack scenarios, and discover how user and entity behavior analytics (UEBA) can be leveraged to safeguard organizations.
Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. This week, we delve into the increasing adoption of XaaS models across enterprises to achieve agility, pervasive automation, and digitization of business verticals.
User and entity behavior analytics (UEBA) is a relatively new category of cybersecurity tools that utilize machine learning (ML) algorithms to detect abnormalities in the behavior of the users and entities that belong to an enterprise network. UEBA monitors and continuously learns from the behavior of various user accounts and devices in the network, and establishes a baseline behavioral profile for each using statistical and probability models.
Among the different types of cyberattacks, insider threats are the hardest to track and have the highest rate of success. This can be attributed to their use, or rather misuse, of legitimate credentials, machines, and access privileges. Traditional SIEM solutions use simple rule-based alerting to detect potential insider threats, which cannot analyze user behavior or detect any anomalies therein.