A Novel Intrusions Detection Method Based on HMM Embedded Neural Network

• Published in: Advances in Natural Computation pp. 139 - 148
• Main Authors: Jiang, Weijin, Xu, Yusheng, Xu, Yuhui
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Memory and file management (including protection and security); Memory organisation. Data processing; Software; Text; Intruder detector; Markov model; Pattern recognition; Neural network; Dynamical system; Character recognition; Modeling; Radial basis function; Database; Hidden Markov model; Robustness; Artificial intelligence; Computer security; Voice; Intrusion detection systems
• ISBN: 3540283234; 9783540283232
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11539087_16

Due to the excellent performance of the HMM (Hidden Markov Model) in pattern recognition, it has been widely used in voice recognition, text recognition. In recent years, the HMM has also been applied to the intrusion detection. The intrusion detection method based on the HMM is more efficient than other methods. The HMM based intrusion detection method is composed by two processes: one is the HMM process; the other is the hard decision process, which is based on the profile database. Because of the dynamical behavior of system calls, the hard decision process based on the profile database cannot be efficient to detect novel intrusions. On the other hand, the profile database will consume many computer resources. For these reasons, the combined detection method was provided in this paper. The neural network is a kind of artificial intelligence tools and is combined with the HMM to make soft decision. In the implementation, radial basis function model is used, because of its simplicity and its flexibility to adapt pattern changes. With the soft decision based on the neural network, the robustness and accurate rate of detection model network, the robustness and accurate rate of detection model are greatly improved. The efficiency of this method has been evaluated by the data set originated from Hunan Technology University.