ANNIDS: intrusion detection system based on artificial neural network

Yan-Heng Liu Yan-Heng Liu,Ai-Min Wang Ai-Min Wang,Da-Xin Tian Da-Xin Tian

doi:10.1109/icmlc.2003.1259699

ANNIDS: intrusion detection system based on artificial neural network

Yan-Heng Liu Yan-Heng Liu, Ai-Min Wang Ai-Min Wang + Show 1 more

https://doi.org/10.1109/icmlc.2003.1259699

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Publication Date: Nov 2, 2003

Citations: 15

Affiliation: Jilin Province Science and Technology Department, Jilin University

#Adaptive Resonance Theory Neural Network #Artificial Neural Network + Show 8 more

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Abstract

This paper describes a network intrusion detection system based on artificial neural network (ANNIDS). The advantage of neural network ensures that ANNIDS does not need expert knowledge and it can find unknown or novel intrusions. The key part of ANNIDS is an adaptive resonance theory neural network (ART). ANNIDS can be trained in real-time and in an unsupervised way. A weight hamming distance method is used in detection, which is simple and correct in finding anomalous behavior. A well-trained ANNIDS can monitor the network in real time. The experimental results show that ANNIDS performs best when vigilance parameter is 0.4 to 0.5 and intrusion threshold is 0.4. The false positive error is about 8%, the negative error is about 2%, and the total error is lower 10%.

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