Visible to the public Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security

TitleEvaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security
Publication TypeConference Paper
Year of Publication2018
AuthorsVigneswaran, Rahul K., Vinayakumar, R., Soman, K.P., Poornachandran, Prabaharan
Conference Name2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT)
Date Publishedjul
Keywordsadvanced cyber attacks, Biological neural networks, classical machine learning algorithms, Collaboration, computer security, cyber safety, cyber security, Deep Learning, deep neural networks, DNN, ICT system, Intrusion detection, learning (artificial intelligence), machine learning, machine learning algorithms, Metrics, N-IDS, network intrusion detection system, neural nets, Neural Network Security, policy-based governance, pubcrawl, resilience, Resiliency, security of data, Training
AbstractIntrusion 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.
Citation Keyvigneswaran_evaluating_2018