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.
Support by the National Natural Science Foundation of China Under Grant No.60603029; the Natural Science Foundation of Jiangsu Province of China Under Grant No.BK2005009.
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Luo, J., Ding, L., Pan, Z., Ni, G., Hu, G. (2007). Research on Cost-Sensitive Learning in One-Class Anomaly Detection Algorithms. In: Xiao, B., Yang, L.T., Ma, J., Muller-Schloer, C., Hua, Y. (eds) Autonomic and Trusted Computing. ATC 2007. Lecture Notes in Computer Science, vol 4610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73547-2_27
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DOI: https://doi.org/10.1007/978-3-540-73547-2_27
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