Network intrusion detection based on relative mutual K-nearest neighbor density peak clustering - Ren, Chunhua; Wang, Changyuan; Yu, Yang; Yang, Wanan; Guo, Ruiqi (Frontiers in Physics, 2025)
Network security is the core guarantee for the stable operation of Cyber-Physical-Social Systems (CPSS), and intrusion detection technology, as a key link in network security, is crucial to ensuring the security and reliability of CPSS. The application of traditional clustering algorithms in intrusion detection usually relies on a preset number of clusters. However, network intrusion data is highly random and dynamic, and the number and distribution structure of clusters are often difficult to determine in advance, resulting in limited detection accuracy and adaptability. To tackle this issue, this paper introduces a density peak clustering algorithm, RMKNN-FDPC, which integrates relative mutual K-nearest neighbor local density with a fuzzy allocation strategy for network intrusion detection, aiming to enhance the capability of identifying unknown attack patterns. Firstly, in the stage of local density calculation, the relative mutual K-nearest neighbor method is used instead of the traditional truncation distance method to more accurately characterize the local density distribution by considering the mutual neighborhood relationship between data points. Secondly, in the remaining point allocation stage, the fuzzy allocation strategy of the mutual K-nearest neighbor effectively avoids the error propagation problem caused by chain allocation in traditional density peaks clustering algorithm (DPC). Finally, a large number of experiments were conducted, including KDD-CUP-1999 experiments, synthetic dataset experiments, real dataset experiments, face dataset experiments, parameter analysis experiments, and run time analysis experiments. The experimental results show that the proposed method performs exceptionally well in the clustering task and can effectively mine network intrusion information.