Abstract: Based on the uncertainty and complexity of risk assessment of information security and limitations of the application of the traditional mathematical models in risk assessment of information security, we proposed an evaluating method of risk assessment of information security based on particle swarm-wavelet neural network(PWNN) by means of integrating the artificial neural networks, wavelet analysis and particle swarm optimization algorithm Firstly, the risk factors were quantized by fuzzy evaluation method, and the input of ANN was fuzzily pretreated. Secondly, the wavcletneural network was trained by particle swarm optimization algorithm The simulation results show that risk level of the information system can be evaluated ctuantitatively by the PWNN model proposed in this paper, and the shortcomings of current assessment methods can be overcome, such as more subjectivity, randomness and fuzzy conclusion, and PWNN has better learning ability and more faster convergence rate than that of the current methods.

Key words: Information security, Risk assessment, Wavclet neural network(WNN) , Particle swam, Optimization