HyperIDS: a hypergraph learning and quantum-inspired transformer ensemble framework for IoT intrusion detection

The unprecedented increase in the number of Internet of Things (IoT) devices has widened the attack surface of modern-day networks, making them vulnerable to various cyberattacks. Conventional intrusion detection mechanisms face challenges in identifying the subtle correlations between network traffic characteristics and providing consistent detection rates in varying heterogeneous environments. With this context, this research aims at introducing HyperIDS, an innovative Intrusion Detection System (IDS) which combines Hypergraph Learning, Quantum-Inspired Feature Selection and Optimization, and Transformer-based Ensemble Classifier for intelligent IoT cyberattack detection. First, Dual-Fitness Enhanced Gaussian Quantum Particle Swarm Optimization (DFE-GQPSO) approach is utilized to select the most relevant traffic characteristics while reducing feature space and computational cost. These selected traffic features are then converted to a hypergraph form, allowing higher order relations between different entities of the network to be captured. Following this step, Hypergraph Neural Networks (HGNN) is deployed to generate structural and relational representations from the hypergraph structure of the dataset. Long-term dependencies and attack patterns are subsequently extracted using a transformer encoder. The final classification process involves combining CatBoost and XGBoost using stacking ensemble method. In addition, a SHAP-based explainability module is incorporated to ensure transparency and trustworthiness of the developed system. In order to evaluate the proposed framework, HyperIDS is experimentally tested against two commonly used cyber security datasets, namely, TON_IoT and Bot-IoT. Experimental findings have shown superior effectiveness of HyperIDS in detecting cyber-attacks with 98.92, 98.81, 98.76, and 98.78% of accuracy, precision, recall, and F1 score, respectively on the TON_IoT dataset. Similarly, accuracy, precision, recall, and F1 scores of HyperIDS reach 99.14, 99.05, 99.01, and 99.03% on the Bot-IoT dataset. Comparison with conventional machine learning (ML), deep learning (DL), and hybrid intrusion detection techniques have proven HyperIDS's superiority in detecting cyberattacks on IoT infrastructure.