A stacking ensemble of deep learning models for IoT intrusion detection

Lazzarini, Riccardo; Tianfield, Huaglory; Charissis, Vassilis

Abstract

The number of Internet of Things (IoT) devices has increased considerably in the past few years, which resulted in an exponential growth of cyber attacks on IoT infrastructure. As a consequence, the prompt detection of attacks in IoT environments through the use of Intrusion Detection Systems (IDS) has become essential. This article proposes a novel approach to intrusion detection in IoT based on a stacking ensemble of deep learning (DL) models. This approach is named Deep Integrated Stacking for the IoT (DIS-IoT) and it combines four different DL models into a fully connected DL layer, creating a standalone ensemble model. DIS-IoT is evaluated on three open-source datasets, namely ToN_IoT, CICIDS2017 and SWaT, in binary and multi-class classification and compared results with other standard DL methods. Experiments demonstrate that DIS-IoT is capable of a high-level accuracy with a very low False Positive rate (FPR) in all datasets. Results were also compared against other state-of-the-art works available in the literature, which used similar methods on the same ToN_IoT dataset. DIS-IoT achieves comparable performance with others in binary classification and outperforms them in multi-class classification.

Citation

Lazzarini, R., Tianfield, H., & Charissis, V. (2023). A stacking ensemble of deep learning models for IoT intrusion detection. Knowledge-Based Systems, 279, Article 110941. https://doi.org/10.1016/j.knosys.2023.110941

• Journal Article Type: Article
• Acceptance Date: Aug 26, 2023
• Online Publication Date: Sep 13, 2023
• Publication Date: 2023-11
• Deposit Date: Sep 18, 2023
• Publicly Available Date: Sep 18, 2023
• Journal: Knowledge-Based Systems
• Print ISSN: 0950-7051
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 279
• Article Number: 110941
• DOI: https://doi.org/10.1016/j.knosys.2023.110941
• Keywords: Internet of things, Intrusion detection systems, Deep learning, Ensemble learning, Stacking