Previous research on behaviour-based attack detection on networks of IoT devices has resulted in machine learning models whose ability to adapt to unseen data is limited, and often not demonstrated. In this paper we present an approach for modelling IoT network attacks that focuses on generalizability, yet also leads to better detection and performance. First, we present an improved rolling window approach for feature extraction, and introduce a multi-step feature selection process that reduces overfitting. Second, we build and test models using isolated train and test datasets, thereby avoiding common data leaks that have limited the generalizability of previous models. Third, we rigorously evaluate our methodology using a diverse portfolio of machine learning models, evaluation metrics and datasets. Finally, we build confidence in the models by using explainable AI techniques, allowing us to identify the features that underlie accurate detection of attacks.
View on arXiv