Clustering Detection Method of Network Intrusion Feature Based on Support Vector Machine and LCA Block Algorithm

• Published in: Wireless personal communications Vol. 127; no. 1; pp. 599 - 613
• Main Authors: Zhang, Jie, Sun, Jinguang, He, Hua
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2022; Springer Nature B.V
• Subjects: Algorithms; Clustering; Communications Engineering; Computer Communication Networks; Engineering; False alarms; Intrusion; Networks; Signal,Image and Speech Processing; Support vector machines; Feature divide; Support vector machine; Clustering detection; LCA block algorithm; Network intrusion feature
• ISSN: 0929-6212; 1572-834X
• DOI: 10.1007/s11277-021-08353-y

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.