Advertisement
Published in:
2005 | OriginalPaper | Chapter
Masquerade Detection System Based on Principal Component Analysis and Radial Basics Function
Authors : Zhanchun Li, Zhitang Li, Yao Li, Bin Liu
Published in: Computational Intelligence and Security
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
This article presents a masquerade detection system based on principal component analysis (PCA) and radial basics function (RBF) neural network. The system first creates a profile defining a normal user’s behavior, and then compares the similarity of a current behavior with the created profile to decide whether the input instance is valid user or masquerader. In order to avoid overfitting and reduce the computational burden, user behavior principal features are extracted by the PCA method. RBF neural network is used to distinguish valid user or masquerader after training procedure has been completed by unsupervised learning and supervised learning. In the experiments for performance evaluation the system achieved a correct detection rate equal to 74.6% and a false detection rate equal to 2.9%, which is consistent with the best results reports in the literature for the same data set and testing paradigm.