Research on risk assessment model of information security based on particle swarm algorithm -RBF neural network

Abstract

The risk assessment of information security is an important evaluation method and decision-making mechanism in the process of constructing information security mechanisms. The risk assessment of information security has character of complex, nonlinear, uncertain and strong real-time, the traditional mathematical model for the risk assessment of information security not only lays some limitations, but also lays large subjective randomness and fuzziness, it is difficult to operate and lack of self-learning ability. Combining with RBF neural network theory and particle swarm optimization fuzzy evaluation method, this paper establish a security risk assessment model based on RBF neural network optimized by particle swarm. The simulation results prove that: the advanced RBF neural network model can achieve quantitative assessment of information system on the level of risk factors and has higher fitting precision, stronger learning ability and faster velocity of convergence than traditional neural network.

Information Security Risk Assessment on Complex Information System

“…Artificial Intelligence (AI) techniques have found applications in the field of risk management [13] , such as neural networks and fuzzy modeling. Artificial Neural Network (ANN) has a strong nonlinear mapping ability, with strong learning ability and high classification and prediction accuracy.…”