Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Step Forward to Revolutionise IntrusionDetection System Using Deep Convolution Neural Network

Version 1 : Received: 7 July 2020 / Approved: 9 July 2020 / Online: 9 July 2020 (12:14:10 CEST)

How to cite: Bandyopadhyay, S.; Chowdhury, R.; Roy, A.; Saha, B. A Step Forward to Revolutionise IntrusionDetection System Using Deep Convolution Neural Network. Preprints 2020, 2020070187 (doi: 10.20944/preprints202007.0187.v1). Bandyopadhyay, S.; Chowdhury, R.; Roy, A.; Saha, B. A Step Forward to Revolutionise IntrusionDetection System Using Deep Convolution Neural Network. Preprints 2020, 2020070187 (doi: 10.20944/preprints202007.0187.v1).

Abstract

Cyber security plays an important role to protect our computer, network, program and data from unauthorized access. Intrusion detection system (IDS) and intrusion prevention system (IPS) are two main categories of cyber security, designed to identify any suspicious activities present in inbound and outbound network packets and restrict the suspicious incident. Deep neural network plays a significant role in the construction of IDS and IPS. This paper highlights a novel IDS using optimized convolution neural network (CNN-IDS). An optimized CNNIDS model is an improvement over CNN which selects the best weighted model by considering the loss in every epoch. All the experiments have been conducted on the well known NSL-KDD dataset. Information gain has been used for dimensionality reduction. The accuracy of the proposed model is evaluated through optimized CNN for both binary and multiclass categories. Finally, a critical comparison has been performed with other general classifiers like J48, Naive Bayes, NB tree, Random forest, Multilayer Perceptron (MLP), Support Vector Machine (SVM), Recurrent Neural Network (RNN) and Convolution Neural Network(CNN). All the experimental results demonstrate that the optimized CNN-IDS model records the best recognition rate with minimum model construction time.

Subject Areas

Intrusion Detection System; NSL-KDD Dataset; One Hot Encoding; Information Gain; Convolution Neural Network

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