An Improved Non-negative Matrix Factorization Method for Masquerade Detection
نویسندگان
چکیده
A local-knowledge method for masquerade detection that uses a Non-negative Matrix Factorization (NMF) algorithm is here proposed. This method does not consider training data from other users to build a specific user profile but his own. It is used a normalization phase that helps improve a previous NMF-based method by Wang et.al. Comparisons with other local-knowledge methods like Wang’s, Hidden Markov Models (HMM) and Eigen Co-occurrence Matrix (ECM) are presented. From results shown by the Receiver Operating Characteristic (ROC) curves we conclude that our proposal is better than Wang’s et. al. and it has a better performance in desirable regions of operation than ECM and HMM.
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تاریخ انتشار 2006