Network Intrusion Detection Technology Research Based On Intelligent Algorithm

Intrusion detection can recognize the attacks which attempt to happen, are happening or have happened, and it is a kind of active network security protection measure. The existing intrusion detection technologies have the deficiency of high false positive rate, higher false negative rate, and poor real-time performance. Especially high detection accuracy is usually based on abundant or self-contained training data.In order to overcome those difficulties that traditional intrusion detection is confronted with, a new detection model---Neural Network Trained by Swarm Intelligence is presented. Firstly, intrusion detection's conception, sorts, characteristics, research content and difficulties confronted by the traditional intrusion detection are analyzed systematically. Then we formulate the principle of some widely known neural network and discuss the concept of Quantum-behaved Particle Swarm Optimization and Modified Quantum Particle Swarm Optimization. We show that MQPSO-Trained BP can increase diversity of population, avoid particle prematurity and improve global search performance. Neural network's conception, characteristics, structure and the training algorithm of wavelet neural network are analyzed. In turn, we proposed an approach of using QPSO and MQPSO to train Wavelet Neural Network.Then, we use MQPSO-Trained WNN to intrusion with QPSO-WNN also tested for the purpose of performance comparison. The well-known KDD CUP99 Intrusion Detection Data Set was used as the experimental data. Experimental result on KDD CUP99 intrusion detection datasets shows that the accuracy of anomaly detection was enhanced and the false positive rate for normal state in the network anomaly detection was declined.The work in this paper indicate that modified QPSO algorithm are promising training algorithms for neural network and could generate better performance than QPSO. It works well on intrusion detection problem trained by neural network.