Intrusion detection system combining misuse detection and anomaly detection using Genetic Network Programming
Yunlu Gong, Shingo Mabu, Ci Chen, Yifei Wang, Kotaro Hirasawa
Abstract
Yunlu Gong, Shingo Mabu, Ci Chen, Yifei Wang, Kotaro Hirasawa
Abstract
In this paper, a class association rule mining approach based on Genetic Network Programming(GNP) for detecting network intrusion combining misuse detection and anomaly detection is proposed. The proposed approach is an extension of the intrusion detection approach using GNP, so it can detect and distinguish normal, known intrusion and unknown intrusion. The simulation result shows that the detection rate is improved compared with traditional intrusion detection approach, and normal, known intrusion and unknown intrusion are distinguished with high accuracy.
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In this paper, a class association rule mining approach based on Genetic Network Programming(GNP) for detecting network intrusion combining misuse detection and anomaly detection is proposed. The proposed approach is an extension of the intrusion detection approach using GNP, so it can detect and distinguish normal, known intrusion and unknown intrusion. The simulation result shows that the detection rate is improved compared with traditional intrusion detection approach, and normal, known intrusion and unknown intrusion are distinguished with high accuracy.
Key concepts: Intrusion detection system, Anomaly-based intrusion detection system, Misuse detection, Computer science, Anomaly detection, Data mining, Intrusion, Genetic programming