Improving IoT Security Through Machine Learning

The swift proliferation of the Internet of Things (IoT) has escalated security apprehensions, prompting extensive research into viable solutions using established techniques and algorithms. Nevertheless, the intricacies arising from IoT limitations, such as constrained power backup and storage capacity, impede the implementation of intricate security algorithms. Addressing these challenges, Computational Intelligence emerges as a panacea. By meticulously analyzing data patterns, these techniques assess gathered information to make well-informed decisions, thereby reinforcing the security of IoT devices. Computational Intelligence encompasses several key pillars, including Artificial Neural Networks (ANN), Fuzzy Set theory, Evolutionary Computing, and Swarm Intelligence. Within this realm, Machine Learning assumes a pivotal role. This investigation primarily focuses on the utilization of. Naive Bayes, Decision Trees, Random Forest and k-Nearest Neighbors algorithms to detect potential attacks within the MQTT dataset-a well-established benchmark for IoT traffic experimentation. Notably, the simulation results underscore remarkable achievements, as all algorithms attain an impressive accuracy rate of 99%, concurrently yielding a corresponding fl-score of 99%. These findings underscore the potential of Computational Intelligence in revolutionizing IoT security, paving the way for robust protection in the ever-expanding IoT landscape.