Cybersecurity Risks Mitigation in the Internet of Things
Annamalai Alagappan, Leo John Baptist Andrews, Sampath Kumar Venkatachary, D Sarathkumar, Raymon Antony Raj
Abstract
Internet of Things is a dynamic platform and involves data which in turn attracts cybersecurity threats. Numerous organisations are increasingly placing its focus towards understanding cyber risks in a dynamic and complex environment while also trying to quantify their exposure. This article focuses on addressing the assessment of cybersecurity threats in IoT and its vectors through a risk-based approach using machine learning algorithms while highlighting and discussing the traditional cyber risk assessment techniques at length. Built on the statistics analysis, it can be decided that the Bayesian algorithm performs in estimating the potential risks at 82% and in comparison, the CAQ model using J48 classifies at 94%. The Bayes algorithm also helps in quantifying the complex risks originating from different sources and helps in understanding how the risk factors emerge and connect in an IoT environment and what controls are required for mitigating them.