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Proactive Insider Threat Detection through Graph Learning and Psychological Context
2012
2012 IEEE Symposium on Security and Privacy Workshops
The annual incidence of insider attacks continues to grow, and there are indications this trend will continue. While there are a number of existing tools that can accurately identify known attacks, these are reactive (as opposed to proactive) in their enforcement, and may be eluded by previously unseen, adversarial behaviors. This paper proposes an approach that combines Structural Anomaly Detection (SA) from social and information networks and Psychological Profiling (PP) of individuals. SA
doi:10.1109/spw.2012.29
dblp:conf/sp/BrdiczkaLPSPCBD12
fatcat:4uszlerbizco5kiylzw3varm2e