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首页> 外文期刊>Journal of software >Extracting Feature Sequences in Software Vulnerabilities Based on Closed Sequential Pattern Mining
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Extracting Feature Sequences in Software Vulnerabilities Based on Closed Sequential Pattern Mining

机译：基于封闭顺序模式挖掘的软件漏洞特征序列提取

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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.
机译：特征提取对于确定软件中的安全漏洞非常重要。挖掘封闭顺序模式可为非冗余频繁序列生成提供完整的压缩信息。在本文中，我们讨论了特征交互问题，并提出了一种有效的算法来提取脆弱性序列中的特征。每个封闭的顺序模式都代表软件漏洞的功能。我们探索如何有效地维护漏洞序列中的封闭顺序模式。紧凑的结构WClosedTree旨在保留闭合的连续模式，并且仔细研究了其良好的属性。开发了两种主要的修剪策略，即向后超模式条件和等效位置信息条件，以删除WClosedTree中频繁但非封闭的顺序模式。在维护WClosedTree的过程中，将计算每个功能序列的权重度量，以更好地满足决策者的需求。因此，提出的算法可以有效地从脆弱性序列中提取特征。实验结果表明，该算法显着提高了挖掘闭合序列模式的运行效率，并且特征交互框架实现了软件漏洞的特征提取。

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