Cybersecurity Knowledge Extraction Using XAI (CROSBI ID 313290)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Šarčević, Ana ; Pintar, Damir ; Vranić, Mihaela ; Krajna, Agneza
engleski
Cybersecurity Knowledge Extraction Using XAI
Global networking, growing computer infrastructure complexity and the ongoing migration of many private and business aspects to the electronic domain commonly mandate using cutting-edge technologies based on data analysis, machine learning, and artificial intelligence to ensure high levels of network and information system security. Transparency is a major barrier to the deployment of black box intelligent systems in high-risk domains, such as the cybersecurity domain, with the problem getting worse as machine learning models increase in complexity. In this research, explainable machine learning is used to extract information from the CIC-IDS2017 dataset and to critically contrast the knowledge attained by analyzing if–then decision tree rules with the knowledge attained by the SHAP approach. The paper compares the challenges of the knowledge extraction using the SHAP method and the if–then decision tree rules, providing guidelines regarding different approaches suited to specific situations.
cybersecurity ; knowledge extraction ; explainable artificial intelligence (XAI) ; Shapley additive explanations (SHAP) ; decision tree ; if–then rules
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