Clustering Detection Method of Network Intrusion Feature Based on Support Vector Machine and LCA Block Algorithm - Zhang, Jie; Sun, Jinguang; He, Hua (Wireless personal communications, 2022)
Traditional methods ignore the imbalance of network data, resulting in unsatisfactory clustering detection results, long detection time, and high rate of missed detection and false alarm. In this regard, this paper proposes a clustering detection method of network intrusion feature based on support vector machine and LCA block algorithm. Firstly, the useless features were deleted by reducing the dimension of the data set, thus improving the clustering detection accuracy. Secondly, the training sample set was divided, and the multi-level support vector model was established by two classification support vector machines. Finally, the LCA algorithm was adopted to identify the network intrusion features, achieving clustering detection of network intrusion feature. The results show that the proposed method achieves better clustering detection results and effectively reduces the average clustering detection time.