计算机应用与软件2012，Vol.29Issue(2)：294-297,4.
基于隐马尔科夫模型和神经网络的入侵检测研究
RESEARCH ON HIDDEN MARKOV MODEL-BASED AND NEURAL NETWORK-BASED INTRUSION DETECTIONS
闫新娟 1谭敏生 1严亚周 2吕明娥2
作者信息
- 1. 南华大学 湖南衡阳421002
- 2. 湖南工学院 湖南衡阳421002
- 折叠
摘要
Abstract
Based on the disadvantages of intrusion detections based on Hidden Markov model and on neural network respectively, this paper proposed a hybrid of intrusion detection with the combination of the above two ways. It commences from the point of view of network protocol and takes TCP data packet as the analytic object, gives a kind of method of observation determination, the output of hidden Markov model is used as the input of neural network, and the neural network output is our final result. At last the experiments revealed that this hybrid intrusion detection method reaches a lower false alarm rate and missing rate than the method using either hidden Markov model or neural network alone.关键词
隐马尔科夫模型/神经网络/入侵检测Key words
Hidden markov model/Neural network/Intrusion detection分类
信息技术与安全科学引用本文复制引用
闫新娟,谭敏生,严亚周,吕明娥..基于隐马尔科夫模型和神经网络的入侵检测研究[J].计算机应用与软件,2012,29(2):294-297,4.
