A Hybrid LSTM-Autoencoder Based Approach for Network Anomaly Detection System in IoT Environments
Lyna Touileb, Kaouthar Zekri, Abbas Bradai, Yannis Pousset, Jean Charles Point · 2024
In the evolving landscape of the Internet of Things (IoT), the susceptibility to cyber threats is widespread, emphasizing the critical need for robust security measures. This paper introduces an innovative anomaly detection system based on a hybrid LSTM-autoencoder approach. Focused on protocol headers analysis in Packet Capture (PCAP) datasets, for robust anomaly detection, our model demonstrates high F1-score in anomalies detection with 99% and 96% on CICIDS2017 and on real network traffic, respectively. Refining our strategy, we address the intricacies of IoT environments, presenting a significant leap forward in intrusion detection for IoT networks.