Deep Learning for Intrusion Detection and Security of Internet of Things (IoT): Current Analysis, Challenges, and Possible Solutions

Abstract

In the last decade, huge growth is recorded globally in computer networks and Internet of Things (IoT) networks due to the exponential data generation, approximately zettabyte to a petabyte. Consequently, security issues have also been arisen with the network growth. However, intrusion detection in such big data is challenging. Smart homes, cities, grids, devices, objects, e-commerce, e-banking, e-government, etc., are different advanced applications of the evolving networks. Many Intrusion Detection Systems (IDS) have been developed recently due to most computer networks' exposure to security and privacy threats. Data confidentiality, integrity, and availability damage will occur in case of IDS prevention failure. Conventional techniques are not effective enough to cope the advanced attacks. Advanced deep learning techniques have been proposed for automatic intrusion detection and abnormal behavior identification of networks. This research aims to provide an inclusive analysis of intrusion detection based on deep learning techniques followed by different intrusion detection systems. In this review, public network-based datasets of IDS are fully explored and analyzed. Deep learning techniques for IDS have been critically evaluated based on different performance metrics (accuracy, precision, recall, f-1 score, false alarm rate, and detection rate). Furthermore, existing challenges and possible solutions for networks security and privacy have been discussed.