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Network Intrusion Detection System Based on Artificial Immune

Author: ChenYuanYuan
Tutor: LiBo
School: Chongqing University of Technology
Course: Applied Computer Technology
Keywords: Network Intrusion Detection Artificial Immune Negative selection Clonal selection Affinity variation Optimization of the gene pool
CLC: TP393.08
Type: Master's thesis
Year: 2011
Downloads: 37
Quote: 0
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By researchers in recent years because of its important position in the field of computer security, widespread concern. , Although advances in intrusion detection technology is huge, but the traditional intrusion detection system still has some shortcomings. Such as unsatisfactory in terms of flexibility, distribution and efficiency, the need to explore new technologies to improve the overall performance of the intrusion detection system. Biological immune system is to protect the organism from external pathogens hazards of a physiological system, it has a distributed self organizing, parallel processing, the immune memory and robustness advantages. The function of the immune system and intrusion detection systems are surprisingly similar, so we can learn from the self-learning self-evolution in biological immune system mechanisms to improve the performance of intrusion detection system. The main subject of the research include: 1. First expounded the basic concepts and principles of intrusion detection systems and biological immune. The biological immune to the feasibility and advantages and disadvantages of the intrusion detection system. According to the IDS CIDF system specification, design a new model of network intrusion detection system based on immune principle framework proposed intelligent intrusion detection system, the introduction of affinity mutation, the gene pool is optimized to improve the efficiency of the model for intrusion pattern recognition and correct rate. The model with the requirements of self-organizing, self-learning and adaptive characteristics, and based on the self-nonself recognition in biological immune. The model consists of four parts: generate detector affinity variation, gene bank restructuring, detect intrusions, the four form an organic whole, in order to generate a variety of detector use less detector detects a variety of The purpose of the invasion. 3, the new model simulation. The model uses data on the characteristics of the 65 encoding in real network extraction datagram by adjusting the parameters affecting the detection efficiency of the system was tested, experimental results show that the new model has a better detection rate and low false detection rate. 4, summarize the research content of this paper, the analysis of this model also needs to address the problem, put forward a vision for the future.

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