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Publication Type : Conference Paper
Publisher : 2018 9th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2018
Source : 2018 9th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2018, Institute of Electrical and Electronics Engineers Inc. (2018)
ISBN : 9781538644300
Keywords : Artificial intelligence, Computer crime, Cyber security, Cyber-attacks, Cyber-safety, Deep learning, Deep neural networks, ICT systems, Intrusion detection, Intrusion Detection Systems, Learning algorithms, Learning systems, Network intrusion detection systems, Network security
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Computer Science, Electronics and Communication
Year : 2018
Abstract : Intrusion detection system (IDS) has become an essential layer in all the latest ICT system due to an urge towards cyber safety in the day-to-day world. Reasons including uncertainty in finding the types of attacks and increased the complexity of advanced cyber attacks, IDS calls for the need of integration of Deep Neural Networks (DNNs). In this paper, DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS). A DNN with 0.1 rate of learning is applied and is run for 1000 number of epochs and KDDCup-'99' dataset has been used for training and benchmarking the network. For comparison purposes, the training is done on the same dataset with several other classical machine learning algorithms and DNN of layers ranging from 1 to 5. The results were compared and concluded that a DNN of 3 layers has superior performance over all the other classical machine learning algorithms.
Cite this Research Publication : V. K. Rahul, Vinayakumar, R., Dr. Soman K. P., and Poornachandran, P., “Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security”, in 2018 9th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2018, 2018.