Intrusion Detection Model Based on Improved Genetic Algorithm Neural Network in Computer Integrated Process System

Abstract:

In order to solve the problems of high false alarm rate and fail rate in intrusion detection system of Computer Integrated Process System (CIPS) network, this paper takes advantage that Genetic Algorithm (GA) possesses overall optimization seeking ability and neural network has formidable approaching ability to the non-linear mapping to propose an intrusion detection model based on Genetic Algorithm Neural Network (GANN) with self-learning and adaptive capacity, which includes data collection module, data preprocessing module, neural network analysis module and intrusion alarm module. To overcome the shortcomings that GA is easy to fall into the extreme value and searches slowly, it improves the adjusting method of GANN fitness value and optimizes the parameter settings of GA. The improved GA is used to optimize BP neural network. Simulation results show that the model makes the detection rate of the system enhance to 97.11%.

• Periodical: Applied Mechanics and Materials (Volumes 380-384)
• Pages: 2708-2711
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.380-384.2708
• Online since: August 2013
• Authors: Li Kun Zou, Shao Kun Liu, Guo Fu Ma
• Keywords: BP Neural Network (BPNN), Computer Integrated Process System (CIPS), Genetic Algorithm Neural Network (GANN), Intrusion Detection, Principal Component Analysis (PCA)

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