Security audit system using Adaptive Genetic Algorithm and Support Vector Machine

With the development of internet, network is playing an increasingly important part in people´s lives, but it is bringing a variety of problems, especially computer security, which has become a serious practical trouble now days. Security audit system is an essential component of computer security mechanisms underlying the firewall and intrusion detection system for computer security. Due to the fact that detection is inefficient and lacks intelligence in traditional security audit system, this paper combines Adaptive Genetic Algorithm(AGA) and Support Vector Machines(SVM) as an intelligent AGA-SVM algorithm in audit analysis, with AGA applied to optimize the penalty factor and kernel function parameters of SVM. Based on online training of AGA-SVM, a security audit system model is established and the result of the experiment shows that the method is effective and reliable.