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Title: An automated penetration semantic knowledge mining algorithm based on bayesian inference
Authors: Zang, Yichao
Hu, Tairan
Zhou, Tianyang
Deng, Wanjiang
Keywords: Automated penetration testing
Bayesian inference
Cyber security
Penetration semantic knowledge
Issue Date: 1-Jan-2021
Publisher: Tech Science Press
Citation: Zang, Yichao, Hu, Tairan, Zhou, Tianyang, Deng, Wanjiang (2021-01-01). An automated penetration semantic knowledge mining algorithm based on bayesian inference. Computers, Materials and Continua 66 (3) : 2573-2585. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
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 of Bayesian network, is constructed to formalize penetration testing data. Then, we adopt the maximum likelihood estimate method to optimize the model parameters and decompose a large Bayesian network into smaller networks based on conditional independence of variables for improved solution efficiency. Finally, irrelevant variable elimination is adopted to extract penetration semantic knowledge from the conditional probability distribution of the model. The experimental results show that the proposed method can discover penetration semantic knowledge from raw penetration testing data effectively and efficiently. © 2021 Tech Science Press. All rights reserved.
Source Title: Computers, Materials and Continua
ISSN: 1546-2218
DOI: 10.32604/cmc.2021.012220
Rights: Attribution 4.0 International
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