IoT attacks and mitigation plan: A preliminary study with Machine Learning Algorithms

Abstract

The threat landscape is growing exponentially. Security issues worsen as more emerging devices, such as the Internet of Things (IoT), embedded systems, and cyber-physical devices are connected to the Internet. Monitoring the IoT ecosystem traffic is necessary to limit the damage caused by cyber threats. Therefore, placing an intrusion detection system (IDS), installing an agent, or running any auditing process onto the IoT devices does not solve the sophisticated attacks on the IoT infrastructure and ecosystem. This paper discusses the efficient way to protect the IoT infrastructure and ecosystem based on previous research. State-of-the-art datasets are investigated. Three machine learning (ML) algorithms are used on a selected IoT dataset to test their efficiency, and the performances of the algorithms are discussed.