The Research Of Artificial Immune Algorithm Optimization And Applications

The immune system is regarded as a second brain of human and other mammals, and is a subject of great research interest because of its powerful information process capabilities. The basic function of biological immune system is to recognize self and non-self, then to clarify and eliminate non-self. Biological immune system has immune recongition, immune memory, immune regulation, immune tolerance, immune surveillance and other characteristics. It is a self-adaptive, self-learning, self-organization, parallel and distributed complex system.Artificial Immune System (AIS) is the computing system to solving many kinds of complex problems based on the functionalities, disciplines, characteristics and other related immune theories of biological immune system. AIS is a novel intelligent computing research field after Artifical Neural Network and Evolutionary Computation, and it is an interdisciplinary hot research field derived from life science and computer science. AIS research is to extract the special information processing mechanisms contained in biological immune system, to build the corresponding models and algorithms, and to implement novel intelligent information processing systems, which could be used to solve many kinds of technical problems faced in the development of the national economy and society. The objective of this paper is to explore artificial immune theory the and optimize the artificial immune algorithms, put forward a new method of detecting the black holes, then design the artificial immune system based on these improvements. Meanwhile introduce the artificial immune detection into the detection of web attack and Business Intelligence (BI) system. The primary research works and contributions of this dissertation are summarized as follows.1) Two types of black holes named HS and HD are introduced based on the reserch of traditional nagative selection algorithm. By studying the fuctions and characteristics of Major Histocompatibility Complex (MHC), the conception of eigenvalue is put forward, and the traditional rule of r-contiguous bits match is replaced with the eigenvalue match. Then the negative selection algorithm is improved and the probability of detector generation is increased. And the MHC detecting window is built using the self eigenvalue matrix and all balck holes that missed by detector set are successfully recognized. Meanwhile the conception of sub-metrix is introduced, to improve the detecting efficience of MHC detecting window. At last the program of artificial immune system is wrote based on the improved algorithm, and a successful simulation is made.2) The actual detecting area will be smaller than the area in theory when the detector generation is insufficiency. Based on this reason and the research of clonal algorithm, the clonal algorithm is introduced into MHC detecting window. The convergence of clonal algorithm is improved using the genetic eigenvalue from MHC detecting window. And by complementing the special detectors, the detector set appetency to missed non-self modes is increased, the burden of MHC detecting window is reduced and the detecting efficience of it is improved. At last the clone module is added in the artificial immune system program and a successful simulation is made.3) Denial of service attack ways and elements are studied, as well as the existing defense strategies. Aiming at the most dangerous SYN flooding attact and UDP flooding attact, the validity of IP address strategy and four sections detection strategy with weight are proposed. Then the artificial immune system is introduce d into the detecting of denial of service attact. The data packet analysis module and IP address extraction module are programmed, and then integrated in the artificial immune system. Thus the flooding attact detecting system is built based on artificial immune alorighm, and a successful simulation is made in the server.4) An integrated business intelligence system is built including ETL module, data warehouse and OLAP based on the research of business intelligence technology, then is put into the actual application. The artificial immune system is introduced into BI system to detect the abnormal data in the cube. By developing the encode module and data analysis module, the artificial immune system is integrated with the BI system, and a successful simulation is made.