Research Of Network Instrusion Detection System Method Based On Artificial Immunology

The rapid development of computer network technology, making people’s lives turnedupside down, people’s lives become increasingly dependent on the help of the network. Inthis process, the network security issues, especially in intrusion detection, has always beenthe focus of attention of experts and scholars. Intrusion detection in the beginning of thenetwork development, has entered people’s vision. With the development of intrusiondetection tools and techniques are constantly progress, including neural networks, geneticalgorithms, support vector, has a good performance in this area. In the1990s, emergingartificial Immunology, due to its features and characteristics and the similarity of theintrusion detection, is rapidly becoming the hot new field of intrusion detection.Artificial immunology through the imitation of biological immunity, the first of the"self" set of learning to generate a mature detector, then detector corresponding calculationwith the data to be detected, and thus be detected data found that "non-self". Algorithm togenerate a detector, the most important is the negative selection algorithm.In this article, we will be the first to introduce the basic principles and algorithms ofartificial immunology, combined with intrusion detection, analysis of current intrusiondetection based on artificial immune scientific research, including the new immunealgorithm framework design ideas in new areas, artificial immune theory and intrusiondetection analysis and comparison. On this basis, the immune algorithm is applied todifferent data sets, different number system, verify the correctness and efficiency of theimmune algorithm, and we get the detection parameters through many experiments.Finally, combined with previous results of the work and the conclusions of the experiment,we designed a lightweight and efficient intrusion detection system based on artificialimmune algorithm.