Comprehensive Evaluation of XBNet for Multi-Class IoT Attack Detection

Iman Akour, Mohammad Alauthman, Ammar Almomani, Ramakrishnan Raman and Varsha Arya
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Iman Akour: University of Sharjah, UAE
Mohammad Alauthman: Department of Information Security, Faculty of Information Technology, University of Petra, Jordan
Ammar Almomani: Department of Computer Information Science, Higher Colleges of Technology, Sharjah, UAE
Ramakrishnan Raman: Symbiosis International University, Pune, India
Varsha Arya: Hong Kong Metropolitan University, China & UCRD, Chandigarh University, India & Center for Interdisciplinary Research, University of Petroleum and Energy Studies, India

International Journal of Cloud Applications and Computing (IJCAC), 2025, vol. 15, issue 1, 1-26

Abstract: IoT environments face growing security threats due to their heterogeneity, resource limits, and scale. This paper evaluates an IoT-optimized Xtremely Boosted Network (XBNet) for multi-class attack detection, incorporating protocol-aware normalization, advanced neural architectures, and ensemble strategies. Using the UNB CIC IoT 2023 dataset (33 attacks, 105 devices), the authors conducted hyperparameter, complexity, and error analyses. XBNet achieved 99.5% binary, 94.5% 8-class, and 96.7% 34-class accuracy—outperforming traditional methods with efficient computation. SHAP analysis highlighted protocol-specific features: flow duration (DoS) and packet variance (DDoS). Error analysis showed 68% of DoS/DDoS misclassifications were due to temporal pattern issues. Runtime tests showed feasible deployment from edge to servers, with 42% memory savings via quantization at 98.8% accuracy. The results offer practical insights for real-world IoT security and guide future intrusion detection advances.

Date: 2025
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