Wireless Network Intrusion Detection Algorithm Based on Multiple Perspectives Hierarchical Clustering

Abstract

Aiming at the problems of high false detection rate, difficult to find unknown attack behavior and high cost of obtaining marked data in existing wireless network intrusion detection algorithms based on supervised learning, this paper proposes an unsupervised wireless network intrusion detection algorithm based on multiple perspectives hierarchical clustering. The algorithm is based on unsupervised learning, and does not need to manually mark a large number of wireless network data participating in classifier learning. It has the advantages of easy access to training datasets and detection of unknown types of attack behavior. At the same time, the algorithm introduces multiple perspectives cosine distance as the similarity measure between wireless network data objects in hierarchical clustering, which makes the clustering results more reasonable and the judgment of network data behavior more accurate, and reduces the false detection rate of intrusion detection to a certain extent. In this paper, Aegean WIFI intrusion dataset (AWID) is selected as the experimental dataset, and principal component analysis is used to reduce the dimension of the experimental dataset, which greatly reduces the time complexity of intrusion detection algorithm. Experimental results show that the proposed wireless network intrusion detection algorithm based on multiple perspectives hierarchical clustering has a significant improvement in detection rate, false detection rate and detection of unknown attack types compared with traditional wireless network intrusion detection algorithms.

Keywords

• |multiple perspectives|hierarchical clustering|wireless network|intrusion detection|principal component analysis (pca)