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<p>In the rapidly expanding landscape of the Social Network of Things (SNoT), ensuring real-time cyber threat detection while maintaining device energy efficiency poses a significant challenge. Conventional intrusion detection approaches often fail to balance accuracy with energy constraints, limiting their scalability in resource-constrained IoT ecosystems. To address this gap, we present an Energy-Aware Threat Propagation Detection Framework (EAPDF) that combines temporal sequence analysis with energy consumption profiling to identify and characterize malicious propagation behaviors. The framework employs PrefixSpan-based sequential pattern mining and XGBoost classification, enhanced with two novel evaluation metrics: Threat Propagation Delay (TPD) and Threat Propagation Energy Footprint (TPEF), which quantify both the timing and energy cost of attacks. Performance was validated on the SNoT-IDS2025 dataset, containing 25,000 labeled IoT attack instances, where EAPDF achieved 99.21% accuracy, 0.994 AUC-PR, an average energy usage of 0.042 Joules, and an average detection delay of 1.83 ms. Compared with benchmark datasets such as CICIDS2017 and IoT-23, the proposed model demonstrated superior scalability, early-stage detection capability, and suitability for edge-deployable security systems. Beyond performance gains, this work provides a reproducible and data-driven framework with applicability to diverse domains including smart cities, healthcare IoT, and industrial control systems, thereby advancing energy-aware cyber threat intelligence in next-generation IoT networks.</p>

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Energy-aware detection of threat information propagation speed in social network of things using XGBoost and sequential pattern mining

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月20日

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  • Romil Rawat,
  • Hitesh Rawat,
  • Anjali Rawat

摘要

In the rapidly expanding landscape of the Social Network of Things (SNoT), ensuring real-time cyber threat detection while maintaining device energy efficiency poses a significant challenge. Conventional intrusion detection approaches often fail to balance accuracy with energy constraints, limiting their scalability in resource-constrained IoT ecosystems. To address this gap, we present an Energy-Aware Threat Propagation Detection Framework (EAPDF) that combines temporal sequence analysis with energy consumption profiling to identify and characterize malicious propagation behaviors. The framework employs PrefixSpan-based sequential pattern mining and XGBoost classification, enhanced with two novel evaluation metrics: Threat Propagation Delay (TPD) and Threat Propagation Energy Footprint (TPEF), which quantify both the timing and energy cost of attacks. Performance was validated on the SNoT-IDS2025 dataset, containing 25,000 labeled IoT attack instances, where EAPDF achieved 99.21% accuracy, 0.994 AUC-PR, an average energy usage of 0.042 Joules, and an average detection delay of 1.83 ms. Compared with benchmark datasets such as CICIDS2017 and IoT-23, the proposed model demonstrated superior scalability, early-stage detection capability, and suitability for edge-deployable security systems. Beyond performance gains, this work provides a reproducible and data-driven framework with applicability to diverse domains including smart cities, healthcare IoT, and industrial control systems, thereby advancing energy-aware cyber threat intelligence in next-generation IoT networks.