Visible to the public Artificial Neural Networks for detecting Intrusions: A survey

TitleArtificial Neural Networks for detecting Intrusions: A survey
Publication TypeConference Paper
Year of Publication2020
AuthorsMahmoud, Loreen, Praveen, Raja
Conference Name2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)
KeywordsArtificial neural networks, attacks., Biological neural networks, convolution, cyber physical systems, feature extraction, intrusion detection system, Metrics, networks, Neurons, policy-based governance, pubcrawl, Recurrent neural networks, Resiliency, Training
AbstractNowadays, the networks attacks became very sophisticated and hard to be recognized, The traditional types of intrusion detection systems became inefficient in predicting new types of attacks. As the IDS is an important factor in securing the network in the real time, many new effective IDS approaches have been proposed. In this paper, we intend to discuss different Artificial Neural Networks based IDS approaches, also we are going to categorize them in four categories (normal ANN, DNN, CNN, RNN) and make a comparison between them depending on different performance parameters (accuracy, FNR, FPR, training time, epochs and the learning rate) and other factors like the network structure, the classification type, the used dataset. At the end of the survey, we will mention the merits and demerits of each approach and suggest some enhancements to avoid the noticed drawbacks.
Citation Keymahmoud_artificial_2020