Enhancing unsupervised anomaly-based cyberattacks detection in smart homes through hyperparameter optimization

As smart homes become increasingly interconnected, it is crucial to ensure their security against cyber threats. This research focuses on improving anomaly detection within smart home environments through the application of unsupervised learning methods. Unlike traditional methods that require prior knowledge of specific attack types, unsupervised methods learn from normal operational data, identifying anomalies as deviations from this norm. This study investigates the impact of hyperparameter tuning on the effectiveness of these methods compared to their default settings. This research aims to identify optimal strategies for the configuration of unsupervised learning algorithms in smart home environments. We applied four models (Elliptic Envelope, Isolation Forest, Local Outlier Factor, and One-class SVM) in four datasets (Bot-IoT, IoTID20, N-baiot, and Ton-IoT). Our findings indicate a significant improvement in performance with hyperparameter optimization.