5G-SIID: an intelligent hybrid DDoS intrusion detector for 5G IoT networks

Abstract

The constrained resources of Internet of Things (IoT) devices make them susceptible to Distributed Denial-of-Service (DDoS) attacks that disrupt service availability by overwhelming systems. Thus, effective intrusion detection is critical to ensuring uninterrupted IoT activities. This research presents a scalable system that combines machine and deep learning models with optimized data processing to secure IoT devices against DDoS attacks. A real-world 5G-IoT network simulation dataset was used to evaluate performance. Robust feature selection identified the 10 most informative features from the high-dimensional data. These features were used to train eight classifiers, namely: k-Nearest Neighbors (KNN), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long-Short-Term Memory (LSTM) and hybrid CNN-LSTM models for DDoS attack detection. Experiments demonstrated 99.99% and 99.98% accuracy for multiclass and binary classification using the proposed hybrid CNN-LSTM model. Crucially, time- and space-complexity analysis validates real-world feasibility. Unlike prior works, this system optimally balances accuracy, efficiency, and adaptability through a precisely engineered model architecture, outperforming existing models. In general, this accurate, efficient, and adaptable system addresses critical IoT security challenges, improving cyber resilience in smart cities and autonomous vehicles.