A Survey on Learning-Based Intrusion Detection Systems for IoT Networks

Abstract

Internet of Things (IoT) networks have developed tremendously over the past years. The main concept behind this technology is to facilitate information exchange between devices without human intervention. However, the eccentric and heterogeneous nature of this type of network demands certain security requirements and algorithms that differ from those implemented in traditional networks. Recently, several studies have explored the use of Machine Learning and Deep Learning methodologies to overcome the security problems in IoT networks and preserve data privacy. This paper explores the diverse security threats and challenges existing in IoT networks. It reviews some learning-based Intrusion Detection Systems that are proposed as countermeasures to many consequent security breaches in IoT environments such as Denial of Service, spoofing, or eavesdropping attacks.