Dynamic Adaptive Of Neihborhood Shape-space Immunity Intrusion Detection

Immune intrusion detection is an effective technology in the field of intrusiondetection.As all know, which could detect the abnormal in environment of the systemand network attack timely, then issue warning to the system. The basic idea is thatcombining the biological immunity principles with immune intursion detection. Itcan solve some problem of intrusion detection. In this paper, under in-depthunderstanding and research the background of the current mainstream immunityintrusion detection algorithm: binary immunity intrusion detection and real-valuedimmunity intusion detection, I compare exhaustively advantages and disadvantagesof the two algorithms. According to the research status at home and abroad, finally,the paper use the neighborhood shape-space as the shape-space of the immunityintrusion detection. And the paper research the mechanism of dynamic adaptive ofneighborhood immunity intrusion detection on the basis of neighborhood shape-space. When the paper research dynamic adaptive of neighborhood negativeselection algorithm, The assignment of neighborhood is very important. When thepaper research that updating detector collection, if algorithm is wrong, thephenomenon that system appear will be system performance cuting down.In connection with above problem, firstly I have to analyze the neighborhoodshape-space, according to the law of environmental change, to find the existingevenly distributed principle can not expression effectively the network eigenvectors.By using entropy discretization, this thesis improve the neighborhood negativeselection algorithm and make sure it have performance of adaptive. Secondly, Iimprove tradition genetic algorithm. Immune genetic algorithm can updateeffectively the detector collection.In this thesis, the algorithm improve adaptive performance of neighborhoodimmunity intrusion detection. Under the premise of making sure the system stableand save data information, it improve system adaptive performance. Even if the network environmental is changeable, system also can work effectively, and improvesystem detection rate.