Optimal design of hadoop intrusion detection system based on neural network boosting algorithms

摘要

The security of massive data has always been the focus of computer security research. With the increase of data storage, the computing platform of single node can not deal with the increasing security of massive data. It is urgent to use distributed computing platform to improve computing efficiency and detection accuracy. The physical deployment of intrusion detection system on cloud computing platform consists of monitoring server, Hadoop master server, IDS server, node and IDS terminal management. The experimental results show that the proposed intrusion detection system based on Hadoop cloud node has better detection effect. This paper searches for the optimal weights, and then begins the training of the neural network. The whole process uses the Hadoop framework of distributed computing platform to implement the genetic algorithm and the neural network algorithm in the cloud computing platform. At the same time, the algorithm is improved to improve the efficiency and accuracy of intrusion detection. The results show that the intrusion detection technology is very effective to protect the application system and help it against various types of intrusion attacks.