The Research On Network Intrusion Detection Algorithm Based On Artificial Immune

Artificial Immune System (AIS) is a kind of computing system to solve many kinds of complex problems based on the function, principle, and character of biologieal immune system theories. Artificial Immune System,which is an emergent cross-discipline research field generated by life science and computer science, is a novel intelligent computation study after Artificial Neural Network and Evolutionary Computation. The basic function of biological immune system is to recongnize self and non-self, and then to classify and eliminate non-self.Biological immune system has immune recognition , immune response , immune memory,immune tolerance and other characteristics. It is a complex distributed system which works in self-adaptive,self-learning,self-organization and parallel processing.With the in-depth study into various information processing mechanisms contained in biological immune system, many effective models and algorithms of intrusion detection can be established and designed,which plays an important role for the establishment of new theory and new method of intrusion detection based on biological immune system, also for the improvement of the current situation of network security. Inspired by the powerful recognition capability of immune system, we focus the study on the abnormal intrusion detection model based on immune system.Based on the ai-net,an algorithm for abnormal intrusion detection is proposed .In view of interactional flaws between users and intrusion detection model,which current algorithms frequently overlook, we bring forward the concept of Early Warning Factor.A framework of instrusion detection algorithm based on immune theory is also discussed at the final part.The main contribution of the dissertation are summarized as fellows:1.The theory of intrution detection , biological immune system, Artifieial Immune System and data clustering has been discussed,as well as their relationships,which provide the base for the algorithm.2. An algorithm based on abnormal intrusion detection is presented in the thesis. Compressed with an advanced ai-net algorithm,the network data in the algorithm is divided into "self"and "nonself" sets with hierarchical clustering analysis,which can be used for abnormal intrusion detection.The experiment results show that the algorithm has fewer parameters and little sensitivity than ai-net and performs well in detection.3. The current intrusion detection algorithm take more performance into consideration, but little for the practical requirements of users.In view of the Intrution Detection System's actual situation,the concept of Early Warning Factor is presented in the thesis , which make it easy to get better tradeoff between detection rate and false positive rate according to security policies chosen by user. On the base of the algorithm, a framework of intrution detection algorithm based on the immune theory is discussed,so does the every part which composes the framework.