Role of Artificial Intelligence and Machine Learning in IoT Device Security
Sandeep Kumar Jangam, Partha Sarathi Reddy Pedda Muntala · International Journal of Artificial Intelligence Data Science and Machine Learning · 2022
The current explosion of devices in the Internet of Things (IoT) has transformed industries, homes, healthcare, and smart cities, allowing unparalleled interconnectedness and access to real-time data. Nevertheless, hyperconnectivity is also a crucial security threat. The cybersecurity tools currently in use are not able to match the variation and quantity of IoT networks. In this article, a new trend in the development of Artificial Intelligence (AI) and Machine Learning (ML) and its application in the secure architecture of the Internet of Things is discussed. AI and ML have been seen to be self-sufficient in terms of detecting, counteracting, and forecasting attacks, a factor that makes them suitable to the dynamic nature of IoTs. The likelihood of all device authentication, detecting anomalies, preventing intrusion, and protecting information is examined in this paper on how to employ AI/ML. The article describes the existing approaches and limitations in detail and on the basis of a deep literature analysis. It goes further to present a powerful framework with supervised and unsupervised learning models to develop proven and tough security systems. A case study comparing the performance measures on accuracy, false positive rate and detection latency of different ML algorithms is provided. Findings indicate that IoT systems based on ML significantly increase the efficiency of detecting threats. The conclusion of the paper is an argument about implications for future research, standardization requirements and ethical issues. As the paradigms of IoT security are becoming revolutionized with the introduction of AI/ML, the article can be considered an elaborate guide to both academicians and practitioners in this emerging field