ORIGINAL RESEARCH article
Front. Phys.
Sec. Social Physics
Volume 13 - 2025 | doi: 10.3389/fphy.2025.1623161
This article is part of the Research TopicSecurity, Governance, and Challenges of the New Generation of Cyber-Physical-Social Systems, Volume IIView all 3 articles
Network Intrusion Detection Based on Relative Mutual K-Nearest Neighbor Density Peak Clustering
Provisionally accepted
- 1Yibin University, Yibin, China
- 2Southwest Petroleum University, Chengdu, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
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.
Keywords: CPSS, Network intrusion detection, Relative mutual k-nearest neighbor, Fuzzy allocation strategy, density peak clustering
Received: 05 May 2025; Accepted: 09 Jun 2025.
Copyright: © 2025 Ren, Wang, Yu, Yang and Guo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Changyuan Wang, Yibin University, Yibin, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.