Research on Anomaly Detection in Massive Multimedia Data Transmission Network Based on Improved PSO Algorithm

With the development of computer network technology and the expansion of network system, sensitive data is facing the threat of hacker attack. Intrusion detection is an active network security defense measure, which is an attempt to invade, an ongoing intrusion or an intrusion that has occurred to identify the process. At present, the detection rate of intrusion detection method is low, the false alarm rate and false alarm rate is high, and the real-time performance is poor. It needs a large number of or complete data to achieve better detection performance. In this paper, the concept, characteristics, classification, research contents and difficulties of traditional intrusion detection for mass multimedia data transmission network are described. Then, the basic principle of neural network and particle swarm optimization (PSO) algorithm and the basic idea of particle swarm optimization algorithm with quantum (QPSO) behaviour are introduced. It is emphasized that QPSO has better convergence performance than PSO algorithm in global optimization problems. In this paper, the concept, characteristics and structure of neural network are described, and the algorithm and classification of wavelet neural network are introduced. Then taking wavelet neural network (WNN) as the object, using the QPSO algorithm as the training algorithm, the concrete operation process is given. The research work in this paper shows that the performance of neural network trained by QPSO algorithm and improved QPSO algorithm is better than that of other intelligent algorithms such as PSO algorithm and genetic algorithm, and the convergence speed is faster than that of PSO algorithm or GA algorithm. QPSO is a high performance neural network training algorithm, which can play a good role in neural network anomaly detection.