Visible to the public Insider Threat Identification System Model Based on Rough Set Dimensionality Reduction

TitleInsider Threat Identification System Model Based on Rough Set Dimensionality Reduction
Publication TypeConference Paper
Year of Publication2010
AuthorsZhang, T., Zhao, P.
Conference Name2010 Second World Congress on Software Engineering
Date Publisheddec
KeywordsAccuracy, attribute information database, Bayes methods, Bayes network, Classification algorithms, demensionality reduction, feedback tree, Human Behavior, Identification, impersonation detection, information system security, Information systems, insider threat, Insider Threat Detection, insider threat identification system model, Metrics, minimum risk Bayes decision, policy-based governance, probability, pubcrawl, resilience, Resiliency, rough set, rough set dimensionality reduction, rough set theory, security, security of data, set theory, Training, trees (mathematics), user behavior
AbstractInsider threat makes great damage to the security of information system, traditional security methods are extremely difficult to work. Insider attack identification plays an important role in insider threat detection. Monitoring user's abnormal behavior is an effective method to detect impersonation, this method is applied to insider threat identification, to built user's behavior attribute information database based on weights changeable feedback tree augmented Bayes network, but data is massive, using the dimensionality reduction based on rough set, to establish the process information model of user's behavior attribute. Using the minimum risk Bayes decision can effectively identify the real identity of the user when user's behavior departs from the characteristic model.
Citation Keyzhang_insider_2010