Modeling User Search Behavior for Masquerade Detection

Masquerade attacks are a common security problem that is a consequence of identity theft. Masquerade detection may serve as a means of building more secure and dependable systems that authenticate legitimate users by their behavior. Prior work has focused on user command modeling to identify abnormal behavior indicative of impersonation. This paper extends prior work by modeling user search behavior to detect deviations indicating a masquerade attack. We hypothesize that each individual user knows their own file system well enough to search in a limited, targeted and unique fashion in order to find information germane to their current task. Masqueraders, on the other hand, will likely not know the file system and layout of another user’s desktop, and would likely search more extensively and broadly in a manner that is different than the victim user being impersonated. We devise a taxonomy of Windows applications and user commands that are used to abstract sequences of user actions and identify actions linked to search activities. The experimental results show that modeling search behavior reliably detects all masqueraders with a very low false positive rate of 1.1%, far better than prior published results. The limited set of features used for search behavior modeling also results in large performance gains over the same modeling techniques that use larger sets of features.