An Efficient Hierarchical LSTM-based Framework for Intrusion Detection in Internet of Things (IoT) Systems
Abdelhammid Bouazza, Hichem Debbi, Hicham Lakhlef, Abdelkarim Smaili · 2023
The rapid expansion of the Internet of Things (IoT) technology has enabled the interconnection of countless devices and systems worldwide. However, due to its lack of adequate cyber security measures, it has become a prime target for malicious hackers. To address this critical issue, a fully secure IoT network must be established, allowing for a safe and efficient deployment of this technology. This paper presents a sophisticated two-level deep learning-based intrusion detection model that uses long-short-term memory (LSTM), incorporating binary and multi-level classification techniques, time-based filtering and aggregation processes, all designed to optimize performance. The proposed model was evaluated using the UNSW-NB15 dataset, achieving highly satisfactory results, including reduced false alarm rates (false positives), and high accuracy and precision.