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Comprehensive Evaluation of XBNet for Multi-Class IoT Attack Detection

Author

Listed:
  • 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)

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.

Suggested Citation

  • Iman Akour & Mohammad Alauthman & Ammar Almomani & Ramakrishnan Raman & Varsha Arya, 2025. "Comprehensive Evaluation of XBNet for Multi-Class IoT Attack Detection," International Journal of Cloud Applications and Computing (IJCAC), IGI Global Scientific Publishing, vol. 15(1), pages 1-26, January.
    • Handle: RePEc:igg:jcac00:v:15:y:2025:i:1:p:1-26
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