Anomaly Detection based on GA&FART Approach of Computer Network Security

Preecha Somwang, Woraphon Lilakiatsakun, and Surat Srinoy


Intrusion Detection System, Computer Network Security, Genetic Algorithm, time series analysis


The problems of intrusion detection in a computer network security are difficulty of having a protective line in the information security against attackers. Researchers have developed Intrusion Detection System (IDS) which is capable of detecting attacks in several available environments. This paper aims to provide the intrusion detection technique into the system by using integrates like the Genetic Algorithm (GA) with the Fuzzy Adaptive Resonance Theory (FART). The GA is applied to randomly select the best attribution and reduction to the featured space. The FART is used to classify different group of data: Normal and Anomalous. The results show that this proposed technique can improve the performance of anomalous detection, showing the high performance of the detection rate and minimizing the false alarm rate. The approach was evaluated on the benchmark data from KDDCup’99 data set.

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