Computer Virus Detection Method Research Based On Chaos Immune Algorithm

With the rapid development of the computer hardware and software technology and network technology, computer security problem becomes more and more prominent, and becomes the focus of attention. As part of Computer security, virus detection software t is to become the top priority of many scholars. Feature detection method, validation sum method, and some behavioral surveillance method are relatively mature research results which have been applied to virus detection software. However, these traditional virus detection methods can only detect known viruses, for unknown viruses and unknown variants of known viruses cannot do anything. Their common defects, such as their adaptability to environment, flexibility and detection efficiency, cannot meet the needs of users, which has been unable to meet its current needs of society. Thus a better virus detection method is urgently needed.Computer virus detection system is mainly used to prevent the virus from damaging to of computer files and stealing information from computers, to ensure that computer systems work, the computer from damage. The main function of biological immune system is to eliminate the bacteria, viruses and other foreign antigens which have invaded the organism, and maintain a healthy organism basic protection. Summing up, computer virus detection system and the immune system to the role of their existing systems are similar. It is based on the similarity of this function, and we use the basic principles of the immune system to detect computer virus, which has become a new research direction to ensure computer security.Through depth analysis of the working mechanism of the biological immune principle and the existing negative selection algorithm, based on the immune principle, though computer virus detection technology has been rapid development, the detection efficiency of the existing algorithms is relatively low; there may be a "black hole" phenomenon. As chaotic characteristics of artificial immune system, we introduced chaos theory to the existing negative selection algorithm, the improved method for the generation of immature detectors, no longer use the original randomly generated approach solves the generation of the detector Inefficiencies, and design computer virus detection model based on a Arnold cat map. Article specifically describes the workflow of the components of the model,the matching rules and use of the fitness function. Because phase space structure of the one-dimensional chaotic map is relatively simple, there is a big security risk. In order to overcome the lack of one-dimensional chaotic map, we use two-dimensional chaotic map to generate chaotic sequence. Two-dimensional chaotic map has better features to cover the test space, effectively improving the detection efficiency.The negative selection algorithm based on chaos theory, not only effectively improve the efficiency of the detector set generation, but also maintain the population diversity well.