Abstract:

The shilling attack detection method based on traditional Support Vector Machine (SVM) can not reflect the influence of the great difference of misclassification cost on classification effect. The paper proposes a novel shilling attack detection method based on costsensitive SVM. The method uses SVM as the classification tool under the costsensitive learning mechanism, and models the probabilistic outputs of SVM, presents dynamic misclassification cost function based on class membership value, thus accurately reflecting the misclassification cost of different samples. Hence, we design the classifier of cost-sensitive SVM and apply it in shilling attacks detection. Experimental results show that, compared with traditional SVM and different classes of costsensitive SVM (CSSVM), our proposed method can more precisely control the cost of misclassification, improve the precision of the minority class samples, and decrease the total cost of misclassification.

Key words: support vector machine (SVM);shilling attacks detection;costsensitive;class membership