Machine learning approaches to IoT security: A systematic literature review

With the continuous expansion and evolution of IoT applications, attacks on those IoT applications continue to grow rapidly. In this systematic literature review (SLR) paper, our goal is to provide a research asset to researchers on recent research trends in IoT security. As the main driver of our SLR paper, we proposed six research questions related to IoT security and machine learning. This extensive literature survey on the most recent publications in IoT security identified a few key research trends that will drive future research in this field. With the rapid growth of large scale IoT attacks, it is important to develop models that can integrate state of the art techniques and technologies from big data and machine learning. Accuracy and efficiency are key quality factors in finding the best algorithms and models to detect IoT attacks in real or near real-time (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

As IoT applications continue to expand, attacks on them are growing rapidly, with recent research trends emphasizing the development of models that integrate big data and machine learning technologies for better security.