Study of building misuse detection models based on genetic algorithms
Da Liu · Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University · 2004
The attack model bases of traditional intrusion detection systems were manually built,hampering the popularization and application of IDSs.A study was conducted to realize the automation of intrusive feature extraction and attack rule generation.An adaptive method based on genetic algorithms was presented for learning the intrusion detection rules.This approach used heuristic search in the data space of network features.The genetic operations run through some operators.The individuals with high fitness were produced,and the same attributes of an intrusion were found.In the simulations and experiments the features of an attack were summarized inductively through the databases of the DAPRA Intrusion Detection Evaluation Program,and it accorded with the objectivity and attack rule summarized by research experts.This method dealt with the noise data,and had the robustness.The adaptive method for building misuse detection models can automatically create the model bases of attacks,and strengthen the transplantation of IDSs.