Automatic classification for vulnerability based on machine learning
Bo Shuai, Haifeng Li, Mengjun Li, Quan Zhang, Chaojing Tang · 2013
In order to solve the problems of traditional machine learning methods for automatic classification of vulnerability, this paper presents a novel machine learning method based on LDA model and SVM. Firstly, word location information is introduced into LDA model called WL-LDA (Weighted Location LDA), which could acquire better effect through generating vector space on themes other than on words. Secondly, a multi-class classifier called HT-SVM (Huffman Tree SVM) is constructed, which could make a faster and more stable classification by making good use of the prior knowledge about distribution of the number of vulnerabilities. Experiments show that the method could obtain higher classification accuracy and efficiency.