Research On Intrusion Detection Techniques Based On Particle Swarm Optimization

With the development of computer technology and the popularity of network applications, the traditional security based on passive defense measures have been unable to adapt to today's changing network environment. How to protect computer users important information and how to create a secure network environment have become a key issues in network security research.Intrusion detection technology for its active defence and intelligent processing technology and the protection of their distributed characteristics of the area of network security has become a hot research topic. By intrusion detection technology, the system can suffer from external attack, maintain their safety and maneuverability, and can continue to provide critical services.Particle Swarm Optimization(PSO) is a very popular intelligent algorithms developed in recent years. Because of its arguments are simple, fast convergence, etc.,it has been widely studied and applied in various fields. But it also easily falls into local optimum and it do not always have the high convergence precision. The social phenomenon of good and bad says, if one person is good then his/her opponent is bad. The social phenomenon of good and bad says, if one person is good then his/her opponent is bad. In this paper we exploit this natural trait of human beings and propose a similar method for population initialization of PSO- Opposition Based Initialization in Particle Swarm Optimization. The proposed approach to population initialization used the opposition based method in which the population and its opposite population is taken as input initialization used the opposition based method in which the population and its opposite population is taken as input. The fitness of both populations are evaluated and only the fitter ones from both are selected as particles.Although PSO has been widely used in various fields, but the application in Intrusion Detection System is rare. This is mainly because there are no suitable encoding scheme when applied to PSO in Intrusion Detection System. Thus, we proposed a coding scheme. For the misuse detection systems based on feature matching. We convert non-numeric attribute to numeric attributes by defining the mapping function, and then standard the discrete attributes and continuous attributes, finally the learned rule was applied in the database. Combined with the Opposition Based Initialization in Particle Swarm Optimization, we extract the rules with a lower false alarm rate and the experiment proved that the extracted rules can improve the detection rate.For the rule learning of the Intrusion Detection System, we proposed a new encoding scheme, and the improved the Particle Swarm Optimization- Opposition Based Initialization in Particle Swarm Optimization, which is applied in intrusion detection. The coding scheme and the improved algorithm are analysis and experiments.