Insider threat detection using characterizing user behavior

X Wang, Q Tan, J Shi, S Su… - 2018 IEEE Third …, 2018 - ieeexplore.ieee.org
X Wang, Q Tan, J Shi, S Su, M Wang
2018 IEEE Third International Conference on Data Science in …, 2018ieeexplore.ieee.org
With the rapid development of information technology, office automation is continuously
improving. The data leakage problem resulting from insider threats is getting worse,
legitimate users may abuse privileges or masquerade as other users, which may result in
the loss of data. In this paper, a new data-centric approach is proposed to detect insider
threat, which based on characterizing user behavior by extracting the features of user
interaction behavior including keystroke dynamics and consecutive queries to model users' …
With the rapid development of information technology, office automation is continuously improving. The data leakage problem resulting from insider threats is getting worse, legitimate users may abuse privileges or masquerade as other users, which may result in the loss of data. In this paper, a new data-centric approach is proposed to detect insider threat, which based on characterizing user behavior by extracting the features of user interaction behavior including keystroke dynamics and consecutive queries to model users' access patterns. Statistical learning algorithms are trained and tested from opening dataset to predict abnormal behavior patterns; experimental results indicate that the approach is very effective and accurate.
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