Enhanced Adaptive Hybrid Convolutional Transformer Network for Malware Detection in IoT

Many university networks use IoT devices, which increases vulnerability and malware threats. The complex, multidimensional structure of IoT network traffic and the imbalance between benign and dangerous data make traditional malware detection techniques ineffective. The Adaptive Hybrid Convolutional Transformer Network (AHCTN) is a novel model that uses CNNs for spatial feature extraction and Transformer networks for global temporal dependencies in IoT data. Unique preprocessing methods like Category Importance Scaling and Logarithmic Skew Compensation handle unbalanced data and severely skewed numerical characteristics. The Unified Feature Selector combines statistical and model-based feature selection methods and guarantees that only the most relevant characteristics are utilized for classification. DWS and LRW handle data imbalance. Our feature engineering approaches, such as Flow Efficiency and Packet Interarrival Consistency, improve prediction accuracy by capturing essential data correlations. The integration of advanced machine learning techniques ensures precise malware classification and enhances cybersecurity by addressing vulnerabilities in IoT-driven academic networks. The AHCTN model was carefully tested using the IoEd-Net dataset, which contains a variety of IoT devices and network activity. The AHCTN outperforms previous models with 98.9% accuracy. It also performs well in Log Loss (0.064), AUC (99.1%), Weighted Temporal Sensitivity (97.1%), and Anomaly Detection Score (96.8%), recognizing uncommon but essential abnormalities in academic network data. These findings demonstrate AHCTN's robustness and scalability for academic IoT malware detection.