Software Vulnerability Analysis Method Based on Adaptive-K Sequence Clustering
نویسندگان
چکیده
Software vulnerability analysis has become a hot topic recently. However, the traditional methods for analyzing software vulnerability have higher false positive rate. In this paper, adaptive K function is defined, and SVAAKSC (Software vulnerability analysis method based on adaptive-K sequence clustering) is presented. The collected objects in software vulnerability sequence database SVSD are pretreated to equal length vectors. Moreover, according to adaptive-K based sequence clustering algorithm, all software vulnerabilities in SVSD are clustered into K clustering. Afterwards, by matching the similarities between detected vulnerability from software and each clustering center, whether the detected vulnerability is a real software vulnerability can be judged. Finally, the corresponding analysis report is obtained. The experimental results and analysis show that SVAAKSC has lower false positive rate and better analysis time.
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