Hybrid CNN–LSTM Framework for DDoS Detection in 5G-Enabled IoT Environments

The increasing deployment of 5G-based Internet of Things (IoT) infrastructure brings about unparalleled opportunities and the exacerbated threat of malicious Distributed Denial-of-Service (DDoS) attacks. Conventional signature-based or static rule-based intrusion detection systems are still inadequate for the dynamic and high-volume traffic in such systems. In order to address these issues, in this paper, we propose a hybrid model with CNN and LSTM. The network features spatial correlations, which are captured by the CNN layers, and the LSTM units learn sequential dependencies for detecting attack behavior evolution. Experimental evaluations performed on the CICDDoS2019 and BoT-IoT datasets show how each module helps, leading to an overall detection accuracy of more than 99% and low false positive rates, and classification latency in real time, 19.5 ms for CICDDoS2019 and 13.6 ms for BoT-IoT), comparisons to relevant existing machine and deep learning baselines establish the trade-off made between accuracy, scalability and practical deployment efficiency. The results show that the proposed model is a suitable and practical choice for protecting 5G-enabled IoT systems against DDoS attacks.