Machine Learning Algorithms for IoT Security

Abstract

Worldwide Internet of Things has been rapidly evolved within smart devices that interact with each other via machine to machine communications. It is revolutionizing the IT market and will have deep, economic and social impact on our lives. The interconnecting devices in IoT networks usually become targets for cyber-attacks. In this regard, far-reaching efforts have been made to address the security and privacy issues in IoT networks primarily through symmetric and asymmetric cryptographic approaches. Due to resource constraints, heterogeneity, massive real time data generated by the IoT devices the traditional cryptographic algorithm become insufficient to encompass the entire security to the IoT devices. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems. In this paper, we systematically review the security algorithms, attack parameters, and the current security solutions for the IoT networks.