Improving ML/DL Solutions for Anomaly Detection in IoT Environments

As part of the evolution toward an era of Web 3.0, the Internet of Things (IoT) bridges physical smart devices to digital world to enhance services for consumer convenience. However, the rapid increase of IoT devices led also to the inheritance of security, privacy, and trust problems, already well-known in traditional networks, making IoT devices even more vulnerable. To be able to detect anomalies and protect such IoT devices from cyberattacks, different techniques have been proposed in the literature using diverse approaches going from the logic-based (knowledge bases and ontologies) ones to the statistical ones (Machine Learning-ML/Deep Learning-DL). In this paper, we focus on the later approaches (ML/DL) to identify, reproduce, evaluate, and compare different state-of-the-art machine learning algorithms for anomaly detection in IoT environments, along with the main datasets used in such research works. Once suitable ML models and datasets are identified, we investigated the potential for enhancing them by incorporating a feature selection algorithm. This aims to reduce the dataset’s dimensionality while concurrently improving performance metrics such as accuracy, precision, recall, and F1-score.