Edge network intrusion detection based on multi-center incremental clustering algorithm

Yongming Feng; Xiaoming Ju

doi:10.1117/12.2659271

28 October 2022 Edge network intrusion detection based on multi-center incremental clustering algorithm

Yongming Feng, Xiaoming Ju

Proceedings Volume 12453, Third International Conference on Computer Communication and Network Security (CCNS 2022); 124530C (2022) https://doi.org/10.1117/12.2659271
Event: Third International Conference on Computer Communication and Network Security (CCNS 2022), 2022, Hohhot, China

Abstract

With the increase of network demand and the complexity of network environment, malicious attacks on network devices emerge in an endless stream. Compared with ordinary networks, edge networks have more limited resources and face more complex network environment. Edge networks need more efficient and accurate intrusion detection systems to ensure the security of edge networks. According to the characteristics of edge networks, an intrusion detection method based on multicenter incremental clustering is proposed in this paper. The method first uses the DBSCAN algorithm to cluster the initial data set. At the same time, the concept of multi-cluster centers is proposed. For a cluster with large number of samples and irregular distribution, the characteristics of the cluster can be described by multiple cluster centers. For the new incremental data, we determine the class of the new data by the location of the cluster center and the number of core sample points around it. The multi-center incremental clustering algorithm not only reduces the time of clustering new data, but also can effectively detect unknown network intrusion, which improves the efficiency and accuracy of network intrusion detection.

Citation Download Citation

Yongming Feng and Xiaoming Ju "Edge network intrusion detection based on multi-center incremental clustering algorithm", Proc. SPIE 12453, Third International Conference on Computer Communication and Network Security (CCNS 2022), 124530C (28 October 2022); https://doi.org/10.1117/12.2659271

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