Research on cost-sensitive learning in one-class anomaly detection algorithms

Authors:
Jun Luo;Li Ding;Zhisong Pan;Guiqiang Ni;Guyu Hu
Affiliations:
Institute of Command Automation, PLA University of Science and Technology, Nanjing, China;Institute of Command Automation, PLA University of Science and Technology, Nanjing, China;Institute of Command Automation, PLA University of Science and Technology, Nanjing, China;Institute of Command Automation, PLA University of Science and Technology, Nanjing, China;Institute of Command Automation, PLA University of Science and Technology, Nanjing, China
Venue:
ATC'07 Proceedings of the 4th international conference on Autonomic and Trusted Computing
Year:
2007

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Abstract

According to the Cost-Sensitive Learning Method, two improved One-Class Anomaly Detection Models using Support Vector Data Description (SVDD) are put forward in this paper. Improved Algorithm is included in the Frequency-Based SVDD (F-SVDD) Model while Input data division method is used in the Write-Related SVDD (W-SVDD) Model. Experimental results show that both of the two new models have a low false positive rate compared with the traditional one. The true positives increased by 22% and 23% while the False Positives decreased by 58% and 94%, which reaches nearly 100% and 0% respectively. And hence, adjusting some parameters can make the false positive rate better. So using Cost-Sensitive method in One-Class Problems may be a future orientation in Trusted Computing area.