Ming Wan1, Quanliang Li1, Jiangyuan Yao2,*, Yan Song3, Yang Liu4, Yuxin Wan5
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4033-4049, 2022, DOI:10.32604/cmc.2022.030895
Abstract Anomaly detection is becoming increasingly significant in industrial cyber security, and different machine-learning algorithms have been generally acknowledged as various effective intrusion detection engines to successfully identify cyber attacks. However, different machine-learning algorithms may exhibit their own detection effects even if they analyze the same feature samples. As a sequence, after developing one feature generation approach, the most effective and applicable detection engines should be desperately selected by comparing distinct properties of each machine-learning algorithm. Based on process control features generated by directed function transition diagrams, this paper introduces five different machine-learning algorithms as alternative detection engines to discuss their… More >
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