Research On Intrusion Detection Method Based On Real-Coded Genetic Neural Network

With the advancement of science and technology and the development of computer network techniques, the Internet age is coming. It's arrival has completely changed people's way of life, and more and more people had been engaged in the network,enjoying the various conveniences that network brings. However, as the Internet is rapidly expanding, the security issue has become the core issue that can not be ignored in the internet development.Traditional network security model can not fit the development of network technology, PPDR model emerged as the age requires. Intrusion detection is an important composed part in PPDR model. It makes up for security protection measures about firewall and data encryption. It can identify malicious intention and act to the computer and network resources, and make an instant response. Intrusion detection analysis technology is the core of intrusion detection system which includes abnormity intrusion detection and abused intrusion detection.Considering the problem of high rate of false negatives and false positives of IDS, this thesis presents a kind of synchronous detection model of classification detector, which is based on the real-coded genetic neural network and is a kind of abnormity intrusion detection. Meanwhile, a method of sample streamlining in the data pretreatment process has also been presented, by which sample data can be effectively compressed. Finally, in order to take advantage of both the traits that the real-coded genetic algorithms are good at global searching, and the great performance of the back propagation (BP) in accurate local searching, we join the real-coded genetics algorithm and BP algorithm together to optimize the initial weights of BP with GA. And then the trained network can be used as a separate detector, which is more effective in intrusion detection. Their effective combination can overcome the shortcomings of slow constringency rate and immersion minim value of the traditional BP algorithm, and omit the individual's coding and decoding operations of binary-coded genetic algorithms during evolvement process. The research shows that this technology is well, and it has the advantages of rapid learning rate and high classify accuracy.