The Research And Application Of Naive Bayes Classification In Intrusion Detection

With the increasingly wide applications of computer and network technology, the network security problems are more and more remarkable. Because new attacks ceaselessly emerge with the intrusion technologies, firewall and other passive security methods cannot provide complete protection. As an important and active security mechanism, Intrusion Detection will reinforce the traditional system security mechanism. Intrusion detection techniques can help us to detect attacks against computer system by monitoring the behavior of users, networks, and computer systems. By monitoring and analyzing, the anomalous and illegal activities been taken can be discovered, which include attack using security vulnerabilities by legitimate users or unauthorized access. In addition, Intrusion Detection System (IDS) can diagnose which type of attack or malicious activity is taking and then take active response to stop the intrusion.Intelligent Methods for Intrusion Detection System is hot spot in the field of network security, aim to the problem of high rate of false negatives and false positives of IDS, proposed the genetic neural network. This method Naive Bayes based on the Kernel Density Estimation are effective on accurate local searching, By using the Feature Selection of the Symmetrical uncertainty, an improved the time and the accuracy is proposed, corresponding experiment results show that when applying in intrusion detection, K_NBC performs better on the detection efficiency and false alarm rate.The followings are the main contents.(1) Analysing and researching the feature selection method of Symmetrical uncertainty,compare with other feature selection methods on the same search scenario.(2) Introducing the benefits and shortcoming Bayse, by Kernel Density Estimation replacing the Gauss Function,The experimental result shows that the method is effective in IDS.