Intrusion Detection System Based on Genetic Attribute Reduction Algorithm Based on Rough Set and Neural Network

Abstract

The development of computer network technology not only brings convenience to people’s life but also has many information and data security problems and threats. The performance and problems of traditional intrusion detection system make it insufficient to resist intrusion attacks effectively and with high quality. Therefore, this paper proposes an intrusion detection system based on the combination of genetic attribute reduction algorithm based on rough set and neural network. Based on the traditional BP neural network, this paper combines the genetic attribute reduction algorithm based on rough set to optimize the structure and performance of the system. The experimental results show that the genetic attribute reduction algorithm based on rough set has faster convergence speed and can effectively shorten the running time of the system and improve the efficiency of the algorithm. At the same time, the intrusion detection system based on the combination of genetic attribute reduction algorithm based on rough set and neural network has significantly improved the detection rate of five intrusion attacks compared with the traditional algorithm and achieved the purpose of optimizing the real time and effectiveness of intrusion detection.