Yichao Zang1,*, Tairan Hu2, Tianyang Zhou2, Wanjiang Deng3
CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2573-2585, 2021, DOI:10.32604/cmc.2021.012220
Abstract Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration testing. Associative rule mining, a data mining technique, has been studied and explored for a long time. However, few studies have focused on knowledge discovery in the penetration testing area. The experimental result reveals that the long-tail distribution of penetration testing data nullifies the effectiveness of associative rule mining algorithms that are based on frequent pattern. To address this problem, a Bayesian inference based penetration semantic knowledge mining algorithm is proposed. First, a directed bipartite graph model, a kind… More >
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