Intrusion Detection Method Based on Fuzzy Reasoning Drived by Neural Network
AN Zhiping · Jisuanji gongcheng · 2003
This paper describes a novel intrusion detection method based on fuzzy reasoning drived by neural network (NN). In order to overcome the difficulty of specifying the membership functions of rules depending on experiences of experts in multi-dimension space, neural network is introduced to distinguish non-linearly input/output characteristics of complex system and to generate rule sets and membership functions automatically. The NNs in this experiment are trained using data generated artificially, eliminating both problems, which are the facts that A BP NN is initialized randomly and must undergo supervised learning before being used as a detector and that obtaining training data with knowledge of the desired output for each input vector. The technique demonstrated in this experiment appears to be sensitive and robust, moreover, which is able to detect unknown attack and plays down false alarms and missing alarms.