XSS Attack Detection Technology Based on SVM Classifier

Abstract

Abstract: A large number of security vulnerabilities appeare with the development of Web applications,XSS is one of the most harmful Web vulnerabilities.To deal with the unknown XSS,a XSS detection scheme based on support vector machine (SVM) classifier was proposed.The most representative five dimensional features are extracted to support the training of machine algorithms based on a large number of analysis of XSS attack samples.The feasibility of the SVM classifier was verified based on accuracy,recall and false alarm rate.In addition,the characteristics of deformed XSS samples were added to optimize the performance of the classifier.The improved SVM classifier has better performance compared with traditional tools and ordinary SVM.

Key words: Feature vectorization, SVM classifier, XSS attack

CLC Number:

TP393

ZHAO Cheng, CHEN Jun-xin, YAO Ming-hai. XSS Attack Detection Technology Based on SVM Classifier[J].Computer Science, 2018, 45(11A): 356-360.

References

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