Intrusion Detection System Model Based on CGA
In the face of the growing network security, intrusion detection system, and the traditional shortcomings of more and more prominent. It is the false alarm rate, missed alarm rate, and the problem of poor real-time has not been resolved. In this paper, th
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Intrusion Detection System Model Based on CGA Zongjiang Wang and Xiaobo Li
Abstract In the face of the growing network security, intrusion detection system, and the traditional shortcomings of more and more prominent. It is the false alarm rate, missed alarm rate, and the problem of poor real-time has not been resolved. In this paper, the lack of it, will make a concerted genetic algorithm combined with intrusion detection systems, designed a co-evolutionary genetic algorithm-based intrusion detection system model, it is great to make up for the lack of intrusion detection system now. I use a specific simulation experiments show the usefulness of this model in the final. Keywords Genetic algorithm Model
Collaboration
Intrusion detection system
41.1 Paper Background With the development of high-speed networks, data packet transmission rate more quickly, which requires detection of intrusion detection system must be a corresponding increase in response speed, and accuracy should likewise increase. However, whether misuse or anomaly detection technology, all data are necessary features of the library with a large number of models to compare, so the efficiency
Z. Wang (&) X. Li School of Computer Engineering, Wei Fang University, Wei Fang 261000, China e-mail: [email protected] X. Li e-mail: [email protected]
Z. Zhong (ed.), Proceedings of the International Conference on Information Engineering and Applications (IEA) 2012, Lecture Notes in Electrical Engineering 219, DOI: 10.1007/978-1-4471-4853-1_41, Springer-Verlag London 2013
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of the two is not high. So how to improve the accuracy of intrusion detection system and rapid response has become a reality.
41.2 Collaborative Genetic Algorithms 41.2.1 Introduction Genetic Algorithm Genetic algorithm is a fitness function based on genetic manipulation imposed by the individual groups of individuals within populations restructuring to achieve the iterative process. Genetic algorithm involved five major factors: parameter coding, initial population configuration, the design of fitness function, genetic operators and algorithm control of the design parameters set.
41.2.2 Collaborative Genetic Algorithm In this paper, it is the co-evolutionary genetic algorithm (CGA); it is the difference between common genetic algorithms: co-evolutionary algorithm the population is divided into several sub-species, by sub-populations are independent and mutually constraining the evolution to achieve with the evolution of all sub-populations, so the need for species identification, coding, fitness function, genetic operators to reconsider. Collaborative genetic algorithm and the general genetic algorithm is the same as the computing process, but also code to calculate the fitness value, genetic manipulation. The basic idea of CGA is to [1]: First of all complex systems will be optimized variables grouped into several less variable optimization problems; then many fewer variables were coded system, the formation of multiple independent sub-populations,
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