A BP Neural Network Method for Intrusion Detection
Intrusion detection is often used as the second wall to protect network security, and it exerts vital effect. In this paper, the multi-layer neural network using the backpropagation (BP) algorithm is studies for the classification of intrusion detection. The network architecture is made up of three layers: the input layer, one hidden layer and the output layer. The computer system employs BP Neural Networks as classifier to recognize intrusion.The recognition process includes three stages: (1) feature extraction; (2) learning the training data selected from the feature data set; (3) identifying the intrusion and generating the result report of machine condition classification. Experimental results show that the proposed method is promising in terms of detection accuracy,computationalexpense andimplementation for intrusion detection.
Intrusion detection back-propagation (BP)neural network feature extraction
YANG Degang HU Chunyan WEI Jun
Department of Computer Science and Engineering, Chongqing University,Chongqing, China ;College of Ma College of Mathematics and Computer Science;Chongqing Normal University,Chongqin China Department of Computer Science and Engineering,Chongqing University,Chongqing, China;Zhuyi Medical C
国际会议
厦门
英文
785-788
2006-07-27（万方平台首次上网日期，不代表论文的发表时间）