文章基本信息

标题：Extracting Feature Sequences in Software Vulnerabilities Based on Closed Sequential Pattern Mining
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作者：Wu, Qunhui ; Ma, Shilong ; Wang, Hao 等
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2013
卷号：8
期号：8
页码：1809-1817
DOI：10.4304/jsw.8.8.1809-1817
语种：English
出版社：Academy Publisher
摘要：Feature Extraction is significant for determining security vulnerabilities in software. Mining closed sequential patterns provides complete and condensed information for non-redundant frequent sequences generation. In this paper, we discuss the feature interaction problem and propose an efficient algorithm to extract features in vulnerability sequences. Each closed sequential pattern represents a feature in software vulnerabilities. We explore how to efficiently maintain closed sequential patterns in vulnerability sequences. A compact structure WClosedTree is designed to keep closed sequential patterns, and its nice properties are carefully studied. Two main pruning strategies, backwards super pattern condition and equivalent position information condition, are developed to remove frequent but non-closed sequential patterns in WClosedTree . During the process of maintaining WClosedTree , the weight metric of each feature sequence is calculated to better meet the needs of decision makers. Thus, the proposed algorithm can efficiently extract features from vulnerability sequences. The experimental results show that the proposed algorithm significantly improves the runtime efficiency for mining closed sequential patterns, and the feature interaction framework implements feature extraction in software vulnerabilities.
关键词：feature extraction;closed sequential pattern;vulnerability sequence;software vulnerability