Attack and Anomaly Detection in IoT Sensors Using Machine Learning Approaches

The widespread adoption of Internet of Things (IoT) devices has revolutionized various sectors, bringing advancements and challenges. This article introduces a machine learning-based intrusion detection system for securing IoT environments [1]. Utilizing anomaly-based detection, the model employs Support Vector Machines, k-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, Decision Trees, boosting, voting, and Random Forest. These techniques empower Anomalybased Intrusion Detection Systems to scrutinize IoT traffic effectively. The proposed model demonstrates proficiency in detecting intrusions and abnormal traffic using the IoTID20 dataset, enhancing overall IoT security.