Abstract
The accurate and real-time prediction of network security situation is the premise and basis of preventing intrusions and attacks in a large-scale network. In order to predict the security situation more accurately, a quantitative prediction method of network security situation based on Wavelet Neural Network with Genetic Algorithm (GAWNN) is proposed. After analyzing the past and the current network security situation in detail, we build a network security situation prediction model based on wavelet neural network that is optimized by the improved genetic algorithm and then adopt GAWNN to predict the non-linear time series of network security situation. Simulation experiments prove that the proposed method has advantages over Wavelet Neural Network (WNN) method and Back Propagation Neural Network (BPNN) method with the same architecture in convergence speed, functional approximation and prediction accuracy. What is more, system security tendency and laws by which security analyzers and administrators can adjust security policies in near real-time are revealed from the prediction results as early as possible.
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Supported by the National High Technology Development 863 Program of China under Grant No. 2007AA01Z401, the National Research Foundation for the Doctoral Program of Higher Education of China under Grant No. 20050217007, and the National Defense Advanced Foundation under Grant No. 513150602.
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Lai, JB., Wang, HQ., Liu, XW. et al. WNN-Based Network Security Situation Quantitative Prediction Method and Its Optimization. J. Comput. Sci. Technol. 23, 222–230 (2008). https://doi.org/10.1007/s11390-008-9124-0
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DOI: https://doi.org/10.1007/s11390-008-9124-0