Intrusion detection using hierarchical neural networks

作     者：Zhang, CL Jiang, J Kamel, M 

作者机构：Univ Waterloo Dept Elect & Comp Engn Pattern Anal & Machine Intelligence Res Grp Waterloo ON N2L 3G1 Canada 

出 版 物：《PATTERN RECOGNITION LETTERS》 (模式识别快报)

年 卷 期：2005年第26卷第6期

页      面：779-791页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：intrusion detection neural networks back propagation algorithm radius basis functions hierarchical neural network neural network ensembles 

摘      要：Most intrusion detection system (IDS) with a single-level structure can only detect either misuse or anomaly attacks. Some IDSs with multi-level structure or multi-classifier are proposed to detect both attacks, but they are limited in adaptively learning. In this paper, two hierarchical IDS frameworks using Radial Basis Functions (RBF) are proposed. A serial hierarchical IDS (SHIDS) is proposed to identify misuse attack accurately and anomaly attacks adaptively. A parallel hierarchical IDS (PHIDS) is proposed to enhance the SHIDS s functionalities and performance. The experiments show that the two proposed IDSs can detect network intrusions in real-time, train new classifiers for novel intrusions automatically, and modify their structures adaptively after new classifiers are trained. (c) 2004 Elsevier B.V. All rights reserved.