A Clustering Algorithm Oriented to Intrusion Detection
Wei Li, Zhongming Yang, Yaping Chang, Bin Zhang · 2017
In order to solve the problem of the lack of prior knowledge in intrusion detection, as an unsupervised learning algorithm, the clustering algorithm is applied to intrusion detection. Aiming at the shortcomings of intrusion detection algorithm based on traditional hierarchical clustering, such as high time complexity and high false positive rate, a new clustering algorithm for intrusion detection is proposed with four data sets to validate its effectiveness. Experimental results show that: Compared with the traditional hierarchical clustering algorithm, the clustering algorithm proposed in this paper achieves lower false positive rate and consumes less training time in the case of a comparable detection rate.