A State of the Art Survey of Machine Learning Algorithms for IoT Security
Alan Fuad Jahwar *
Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
Subhi R. M. Zeebaree
Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
*Author to whom correspondence should be addressed.
Abstract
The Internet of Things (IoT) is a paradigm shift that enables billions of devices to connect to the Internet. The IoT's diverse application domains, including smart cities, smart homes, and e-health, have created new challenges, chief among them security threats. To accommodate the current networking model, traditional security measures such as firewalls and Intrusion Detection Systems (IDS) must be modified. Additionally, the Internet of Things and Cloud Computing complement one another, frequently used interchangeably when discussing technical services and collaborating to provide a more comprehensive IoT service. In this review, we focus on recent Machine Learning (ML) and Deep Learning (DL) algorithms proposed in IoT security, which can be used to address various security issues. This paper systematically reviews the architecture of IoT applications, the security aspect of IoT, service models of cloud computing, and cloud deployment models. Finally, we discuss the latest ML and DL strategies for solving various security issues in IoT networks.
Keywords: Deep learning, intrusion detection, network attacks, intrusion datasets