Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 1 of 1
Full-Text Articles in OS and Networks
Semi-Supervised Spatial-Temporal Feature Learning On Anomaly-Based Network Intrusion Detection, Huy Mai
Semi-Supervised Spatial-Temporal Feature Learning On Anomaly-Based Network Intrusion Detection, Huy Mai
Computer Science and Computer Engineering Undergraduate Honors Theses
Due to a rapid increase in network traffic, it is growing more imperative to have systems that detect attacks that are both known and unknown to networks. Anomaly-based detection methods utilize deep learning techniques, including semi-supervised learning, in order to effectively detect these attacks. Semi-supervision is advantageous as it doesn't fully depend on the labelling of network traffic data points, which may be a daunting task especially considering the amount of traffic data collected. Even though deep learning models such as the convolutional neural network have been integrated into a number of proposed network intrusion detection systems in recent years, …