The Research Of Intrusion Detection Based On Genetic Neural Network

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 based on the traits that the genetic algorithm are good in global searching and the back propagation(BP)are effective on accurate local searching, joining the genetics algorithm and BP algorithm together and optimizing the initial weights of BP with GA. Meanwhile, an improved genetic algorithm (IGA) is proposed, corresponding experiment results show that when applying in intrusion detection, IGA-BP performs better on the detection efficiency and false alarm rate.The followings are the main contents.(1) Virtues and drawbacks of BP algorithm and several improved measures are introduced,by virtue of qualitative analysis of the flow characters, distinguishing from the discrepancy of flow characters in different measures.(2) The GA is analysed and researched, aimed at the drawbacks of genetic algorithm, such as prematurity, bad local search ability and etc, crossover operators and mutation operators of genetic algorithms are improved,compare the performance discrepancies between them. (3) By optimizing the initial weights and values of ANN with IGA, The experimental result shows that the method is effective in improving the efficiency and accuracy of intrusion detection.