Using Machine Learning for Detection and Classification of Cyber Attacks in Edge IoT

Internet of Things (IoT) devices are omnipresent due to their ease of use and level of connectivity. Because of wide deployment, IoT network traffic security is a large issue, especially as the devices become more common at the edge of the connected ecosystem. In general, low-powered IoT devices themselves are not inherently secure, so tailored security mechanisms are needed to make the ecosystem secure. The incorporation of the cloud also adds new security issues with the cloud service provider (CSP). In addition, several smart applications necessitate deploying edge-based infrastructure due to their real-time computation and communication requirements, while also having the ability to detect and mitigate different cyber attacks and remain light-weight. In this paper, we propose a machine learning-based approach to detect and classify different edge IoT network traffic driven cyber attacks, and evaluate their strengths and weaknesses. Particularly, we will compare eleven machine learning models to determine the best security agent trained for attack detection and classification on an edge IoT cyber security dataset with fourteen different attacks. We also provide experimental evaluation and analysis of our work, followed by our conclusion.