Cai Ming Liu1,2,3, Yan Zhang1,2,*, Zhihui Hu1,2, Chunming Xie1
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2361-2389, 2024, DOI:10.32604/cmc.2023.045282
Abstract Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods. This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method. The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements. Then, to improve the accuracy of similarity calculation, a quantitative matching method is proposed. The model uses mathematical methods to train and evolve immune More >
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