Comparison between machine learning and deep learning for intrusion detection.

In the domain of network security, there is a never-ending quest for cyber-attacks that might disrupt a network. Furthermore, hostile actions in the network are rapidly increasing because of the unanticipated inception and rising use of the Internet. It is important to build a reliable intrusion detection system (IDS) to resist unwanted access to network resources, Information protection, and network intrusion detection. Deep learning has recently acquired popularity because of the possibilities it has for machine learning. As a result, Deep Learning algorithms are being used in a variety of domains, including pattern recognition and categorization. Intrusion detection analysis used data from security event monitoring to assess the network's status. Several traditional machine-learning algorithms for intrusion detection have been proposed, however detection performance and accuracy must be improved. This paper examines the many methods that have been used to classify network traffic in real-time. We selected to experiment with various methods on UNSW-NB15 datasets to find the optimum way for real-time intrusion detection. [ABSTRACT FROM AUTHOR]