Virus Detection Technology Based On Artificial Immune

With the development of networks, computer viruses spread increasingly, and the harm of the virus has become one of the most serious threats. In P2P networks and cloud computing platform, as long as one node is infected, the virus can spread across the network quickly and threat the safety of the whole network. The traditional virus detection technologies are limited in many aspects, and they can't meet the needs of the new network environment. The artificial immune theory is the analog of the biological immune system and it can indentify self and nonself accurately. Therefore, it is necessary to develop new virus detection technology based on artificial immune. In this thesis, we do reseach on virus detection algorithm based on artificial immune theory in the P2P network and cloud platform, the main tasks are described as follows:(1) The existing virus detection technologies and their challenges are summarized and analyzed. The related concepts of artificial immune theory and the research status at home and abroad are summarized. Besides, the application of the artificial immune theory in virus detection field is analyzed.(2) P2P networks provide the advantages of convenient resource sharing and direct communication. Meanwhile, the viruses obtain a more convenient spread and infection channels. Combining the characteristic of P2P network, the virus detection model based on immune collaboration is proposed. The nature of collaboration between peers is used to share the memory detector. In detector generation phase, an improved NSA based on double mature is proposed to reduce the redundancy of detector set. In virus detection phase, immune fusion P2P virus detection algorithm is proposed. Simulation results show that the improved algorithm can reduce the number of detectors and improve the detection performance. Meanwhile, the memory detectors can be quickly shared by peers, so the security of P2P network is improved.(3) By use of powerful data processing capability of cloud computing platform and the artificial immune network evolution idea, a cloud platform virus detection model based on improved immune network is proposed, and also the improved immune network virus detection algorithm based on MapReduce is given. The algorithm introduces the test set. The detectors are co-evolved in the immune network construction and co-evolution phases through the fitness between detectors and test set and stimulating level between detectors and antigens. Therefore, the detectors are optimized and the virus detection ability in the cloud platform is improved. Simulation results show that the improved immune network algorithm has better detection performance and can be used for virus detection effectively.