Anomaly intrusion detection based on soft computing technique
Zheng Ge, Qinghua Cao, Chao Liu · 2011
Soft computing techniques exploit the given tolerance of imprecision, partial truth, and uncertainty for a particular problem. In the process of intrusion detection, imprecision and uncertainty problems also exist. In order to solve these problems, the paper introduces a novel scheme to process sequences of system calls for anomaly intrusion detection based on interval type-2 fuzzy logic. Hidden markov models and normal database of short sequences are utilized to model normal behaviors. Interval type-2 Fuzzy logic system is incorporated to solve the sharp boundary problem and decide whether a sequence is normal or not. Experimental results show that the proposed scheme can effectively detect intrusions and reduce false positive alarms.