• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Enhanced Network Intrusion Detection using Deep Convolutional Neural Networks

Vol. 12, No.10, October 31, 2018
10.3837/tiis.2018.10.028, Download Paper (Free):

Abstract

Network Intrusion detection is a rapidly growing field of information security due to its importance for modern IT infrastructure. Many supervised and unsupervised learning techniques have been devised by researchers from discipline of machine learning and data mining to achieve reliable detection of anomalies. In this paper, a deep convolutional neural network (DCNN) based intrusion detection system (IDS) is proposed, implemented and analyzed. Deep CNN core of proposed IDS is fine-tuned using Randomized search over configuration space. Proposed system is trained and tested on NSLKDD training and testing datasets using GPU. Performance comparisons of proposed DCNN model are provided with other classifiers using well-known metrics including Receiver operating characteristics (RoC) curve, Area under RoC curve (AuC), accuracy, precision-recall curve and mean average precision (mAP). The experimental results of proposed DCNN based IDS shows promising results for real world application in anomaly detection systems.


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Cite this article

[IEEE Style]
Sheraz Naseer and Yasir Saleem, "Enhanced Network Intrusion Detection using Deep Convolutional Neural Networks," KSII Transactions on Internet and Information Systems, vol. 12, no. 10, pp. 5159-5178, 2018. DOI: 10.3837/tiis.2018.10.028

[ACM Style]
Naseer, S. and Saleem, Y. 2018. Enhanced Network Intrusion Detection using Deep Convolutional Neural Networks. KSII Transactions on Internet and Information Systems, 12, 10, (2018), 5159-5178. DOI: 10.3837/tiis.2018.10.028