A New Deep Learning Approach for Anomaly Base IDS using Memetic Classifier

Shahriar Mohammadi, Amin Namadchian


A model of an intrusion-detection system capable of detecting attack in computer networks is described. The model is based on deep learning approach to learn best features of network connections and Memetic algorithm as final classifier for detection of abnormal traffic.
One of the problems in intrusion detection systems is large scale of features. Which makes typical methods data mining method were ineffective in this area. Deep learning algorithms succeed in image and video mining which has high dimensionality of features. It seems to use them to solve the large scale of features problem of intrusion detection systems is possible. The model is offered in this paper which tries to use deep learning for detecting best features.An evaluation algorithm is used for produce final classifier that work well in multi density environments.
We use NSL-KDD and Kdd99 dataset to evaluate our model, our findings showed 98.11 detection rate. NSL-KDD estimation shows the proposed model has succeeded to classify 92.72% R2L attack group.


Deep learning, KDD99, memetic algorithm, NSL-Kdd, classification function, anomaly base intrusion detection, intrusion-detection system (IDS)

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DOI: http://dx.doi.org/10.15837/ijccc.2017.5.2972


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