Modeling User Search Behavior for Masquerade Detection

Masquerade attacks are a common security problem that is a consequence of identity theft. 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 identify actions linked to search and information access activities, and use them to build user models. 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

Exception Handling in Goal-Oriented Multi-Agent Systems

9th International Workshop on Engineering Societies in the Agents World -- SEP 24-26, 2008 -- St Etienne, FRANCEWOS: 000268328400007Cooperative, autonomous and distributed properties of multi-agent systems deduce the dynamic capabilities of multi-agent system applications. On the other hand, these suitable features increase the error proneness of these applications. In this paper, we propose an exception handling approach to make multi-agent system applications more reliable and robust. And also we classify multi-agent exceptions and have implemented our approach on SEAGENT goal-oriented multi-agent development framework.Ecole Natl Superieure Mines, General Council Loire, Saint Etienne Metropole, GEM, Upetec C