Visible to the public Using Convolutional Neural Networks to Network Intrusion Detection for Cyber Threats

TitleUsing Convolutional Neural Networks to Network Intrusion Detection for Cyber Threats
Publication TypeConference Paper
Year of Publication2018
AuthorsLin, W., Lin, H., Wang, P., Wu, B., Tsai, J.
Conference Name2018 IEEE International Conference on Applied System Invention (ICASI)
KeywordsBehavior features, Conferences, convolution, convolutional neural networks, cyber threats, Deep Learning, feature extraction, feedforward neural nets, Intrusion detection, learning (artificial intelligence), LeNet-5, Metrics, network behavioural features, network intrusion detection, network threat classification, pattern classification, privacy, pubcrawl, security of data, Support vector machines, threat detection classification, threat vectors, Training
AbstractIn practice, Defenders need a more efficient network detection approach which has the advantages of quick-responding learning capability of new network behavioural features for network intrusion detection purpose. In many applications the capability of Deep Learning techniques has been confirmed to outperform classic approaches. Accordingly, this study focused on network intrusion detection using convolutional neural networks (CNNs) based on LeNet-5 to classify the network threats. The experiment results show that the prediction accuracy of intrusion detection goes up to 99.65% with samples more than 10,000. The overall accuracy rate is 97.53%.
DOI10.1109/ICASI.2018.8394474
Citation Keylin_using_2018