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Intrusion Detection Based on Neural Computation and Evolutionary Network

Author: BaiLin
Tutor: LiuFang
School: Xi'an University of Electronic Science and Technology
Course: Applied Computer Technology
Keywords: Adaptive Resonance Theory Artificial immune evolutionary network Clonal selection Unsupervised cluster analysis Intrusion Detection
CLC: TP393.08
Type: Master's thesis
Year: 2005
Downloads: 208
Quote: 2
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Abstract


In the issue of network security , intrusion detection as a proactive security technology to become a research focus in recent years . Intrusion detection requires analysis of the collected data to discover the complex , hidden attacks . Cluster analysis as a key technology in the data mining using unsupervised learning methods , data analysis technology . Clustering algorithm for the detection of network intrusion data , not only to overcome the traditional method of unknown attack detection capability is very limited defects , but also effectively improve system performance . This paper analyzes the pros and cons of existing intrusion detection systems , for its shortcomings , based on the intelligent integration of complementary perspective , clustering method based on neural computing and evolutionary network for intrusion detection research . The main contents : 1 . Conduct a more in-depth analysis and study of the existing clustering method for intrusion detection , summed up the pros and cons of these methods . 2. Focus on the the immune evolution of network artificial immune system . Immune network clustering solve the traditional clustering algorithm clustering requires the number of categories , dependent prototype , prior knowledge of the problem . Immune network - based intrusion detection and data distribution unrelated , can effectively detect abnormal behavior and unknown attacks . 3. Proposed intrusion detection based on ART and immune network clustering algorithm . The algorithm uses the the ART network of the massive intrusion detection data pre-processing , access to vaccines as artificial immune network 's initial antibody immune learning network , then the minimum spanning tree in graph theory clustering of the network structure , ultimately description the data characteristics of normal and abnormal behavior . Simulation results show that on the data sets of KDD CUP99 obtained by ART guiding the the initial antibody immune network , and accuracy are greatly improved convergence speed reconciliation . The method can be heavily compressed original data , and has nothing to do with the data distribution does not depend on a priori knowledge to solve the problem of the abnormal behavior is detected in the original data of the massive non-identity and unknown attacks .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Computer network > General issues > Computer Network Security
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