Artificial Immune Detection for Network Intrusion Data Based on Quantitative Matching Method

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 elements, increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions. The proposed model's objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection, overcoming the disadvantages of traditional methods. The experiment results show that the proposed model can detect intrusions effectively. It has a detection rate of more than 99.6% on average and a false alarm rate of 0.0264%. It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.