Detecting and Stopping Famous Insider Attacks through Behavioral Analytics
This paper is a theoretical study looking at three famous cases of insider attacks that occurred over the past six years. The paper defines the facts of the attacks and information on the attackers. It then theorizes how a behavioral analytics engine could have been used to detect and prevent these attacks. Although theoretical, the analysis is focused on technical facts rather than on opinions, so that objectivity is maintained. All facts referenced in this document are publically available. No confidential or classified data is used in this document.