Intrusion Detection Approach Using Connectionist Expert System

摘要

In order to improve the detection efficiency of rule-based expert systems, an intrusion detection approach using connectionist expert system is proposed. The approach converts the AND/OR nodes into the corresponding neurons, adopts the three-layered feed forward network with full interconnection between layers, translates the feature values into the continuous values belong to the interval [0,1], shows the confidence degree about intrusion detection rules using the weight values of the neural networks and makes uncertain inference with sigmoid function. Compared with the rule-based expert system, the neural network expert system improves the inference efficiency.