Simulation on the Effective Detection Model for Network Differentiation Intrusion

Abstract

The problem of network differentiation intrusion is researched. Network intrusion has the characteristics of complex changes such as concealed, randomness, difference and abruptness. Traditional method cannot describe the change rules, leading to low correct rate of detection. For this, a detection method for network differentiation intrusion based on artificial immune algorithm is proposed. The dynamic change equation of network differentiation intrusion characteristics is established, to obtain the cross point distribution condition of network differentiation intrusion characteristics. The network differentiation intrusion signature database is updated, and the network differentiation intrusion feature in the database is selected. The results show that the artificial immune algorithm solves the problem existing in traditional algorithm, improves the correct rate of network differentiation intrusion detection. Introduction With the increasing of network intrusion events, once the network is vulnerable to malicious attacks, network security will suffer great damage, leading to private data leakage of user, malicious tampering to the rights of user, network paralysis, however the intrusion detection system as the last protection of safe defense, is able to detect intrusion behavior of various forms, therefore network intrusion detection is always a hotspot in research of network security[1,2]. During the process of network intrusion detection, network intrusion detection methods commonly used include the method based on BP neural network algorithm, fuzzy clustering algorithm, and neighborhood model k-means algorithm [3-5]. Among them, the most commonly used is the BP neural network algorithm. But this algorithm used for network intrusion detection, when the difference of network is strong, the change rules of which cannot be accurately described [6]. Therefore, further research should be carried out for the network differentiation intrusion detection problem. Thus, the network differentiation intrusion detection method, has very broad prospects for development, has received great attention. The principle of network differentiation intrusion detection method A establish network differentiation intrusion feature dynamic equation. During network differentiation intrusion detection process, c H is set to be data collection of network differentiation intrusion feature, H is the number of samples in feature collection in specified time period. The following formula is utilized to describe dynamic situation of the network differentiation intrusion characters: