Profiling Users in GUI Based Systems for Masquerade Detection
作者： A. Garg , R. Rahalkar , S. Upadhyaya , K. Kwiat
关键词:
摘要: Masquerading or impersonation attack refers to the illegitimate activity on a computer system when one user impersonates another user. Masquerade attacks are serious in nature due fact that they mostly carried by insiders and thus extremely difficult detect. Detection of these is done monitoring significant changes user's behavior based his/her profile. Currently, such profiles command line data do not represent complete graphical interface (GUI) hence sufficient quickly detect masquerade attacks. In this paper, we present new framework for creating unique feature set GUI systems. We have collected real from live systems extracted parameters construct vectors. These vectors contain information as mouse speed, distance, angles amount clicks during session. model our technique identification detection binary classification problem use support vector machine (SVM) learn classify show can provide rates up 96% with few false positives tested various conclude comprehensive powerful enough masqueraders
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