Call For Paper - Upcoming Conferences
Research Article | Open Access | Download PDF
Volume 13 | Issue 6 | Year 2026 | Article Id. IJECE-V13I6P111 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I6P111

A Lightweight Security Decision Framework for IoT Intrusion Detection Using Entropy-Guided Feature Integrity and Adaptive Ensemble Learning

Saif Wali Ali Alsudani, Mohammad-Reza Feizi-Derakhshi

Received Revised Accepted Published
12 Mar 2026 11 Apr 2026 10 May 2026 27 Jun 2026

Citation :

Saif Wali Ali Alsudani, Mohammad-Reza Feizi-Derakhshi, "A Lightweight Security Decision Framework for IoT Intrusion Detection Using Entropy-Guided Feature Integrity and Adaptive Ensemble Learning," International Journal of Electronics and Communication Engineering, vol. 13, no. 6, pp. 134-144, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I6P111

Abstract

The rapid growth of the Internet of Things (IoT) has intensified cybersecurity risks while exposing the limitations of traditional security solutions in resource-constrained environments. Intrusion detection in IoT systems, therefore, requires reliable, real-time decision-making with minimal computational overhead. This paper presents a lightweight IoT security decision framework that combines entropy-guided feature selection with an adaptive ensemble-based intrusion detection strategy. The proposed approach employs an entropy–correlation (EnCor) feature selection pipeline to construct a compact and informative feature subset, reducing complexity while preserving discriminative security characteristics. Detection decisions are generated using a soft voting ensemble of complementary machine learning classifiers, supported by an adaptive fallback mechanism to improve reliability under diverse attack scenarios. The framework is specifically designed for edge- and gateway-level IoT deployment, avoiding the high latency and computational demands associated with deep learning and blockchain-based solutions. Experimental evaluation on the TON_IoT and CICIoT2023 datasets demonstrates high detection accuracy with low inference latency and reduced memory consumption. The results confirm that effective intrusion detection can be achieved without compromising practical deployment feasibility. Overall, the proposed framework establishes intrusion detection as an efficient and deployable security decision layer for real-world IoT environments.

Keywords

Internet of Things (IoT) Security, Intrusion Detection System (IDS), Entropy-Based Feature Selection, Ensemble Learning, Edge Computing Security.

References

  1. Menachem Domb, and Yehuda Shnaps, Cybersecurity Threats and Mitigations Related to Smart Cities Operation, Smart Cities – Foundations and Perspectives, IntechOpen, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  2. Oyeniyi Akeem Alimi, “Data-Driven Learning Models for Internet of Things Security: Emerging Trends, Applications, Challenges and Future Directions,” Technologies, vol. 13, no. 5, pp. 1-29, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  3. Amir Pakmehr et al., “DDoS Attack Detection Techniques in IoT Networks: A Survey,” Cluster Computing, vol. 27, pp. 14637-14668, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  4. Indu Radhakrishnan, Shruti Jadon, and Prasad Honnavalli, “Efficiency and Security Evaluation of Lightweight Cryptographic Algorithms for Resource-Constrained IoT Devices,” Sensors, vol. 24, no. 12, pp. 1-19, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  5. Faeiz Alserhani, “Intrusion Detection and Real-Time Adaptive Security in Medical IoT Using a Cyber-Physical System Design,” Sensors, vol. 25, no. 15, pp. 1-26, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  6. Huma Gupta, Akshay Jadhav, and Abhay Singh Bisht, “Comparative Analysis of Machine and Deep Learning Models for Intrusion Detection in Fog-Enabled IoT Networks,” International Journal of Networked and Distributed Computing, vol. 14, pp. 1-18, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  7. Cagatay Korkuc et al., “BBNAC: Blockchain Based Network Access Control for IT/OT Infrastructures,” Journal of Network and Systems Management, vol. 34, pp. 1-34, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  8. Zainab Alwaisi et al., “Securing Constrained IoT Systems: A Lightweight Machine Learning Approach for Anomaly Detection and Prevention,” Internet of Things, vol. 28, pp. 1-20, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  9. Huiyao Dong, and Igor Kotenko, “A Lightweight and Robust Approach to IoT Intrusion Detection based on Ensemble Deep Learning,” International Journal of Parallel, Emergent and Distributed Systems, pp. 1-21, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  10. Aya G. Ayad, Nehal A. Sakr, and Noha A. Hikal, “A Hybrid Approach for Efficient Feature Selection in Anomaly Intrusion Detection for IoT Networks,” Journal of Supercomputing, vol. 80, pp. 26942-26984, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  11. Salam Fraihat et al., “Intrusion Detection in Industrial Internet of Things Network using Feature Optimization and Hybrid Deep Learning,” Discover Internet of Things, vol. 6, pp. 1-48, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  12. A. Termanini et al., “Using Machine Learning to Detect Network Intrusions in Industrial Control Systems: A Survey,” International Journal of Information Security, vol. 24, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  13. P. L. S. Jayalaxmi et al., “Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey,” IEEE Access, vol. 10, pp. 121173-121192, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  14. C. Rajathi, and P. Rukmani, “Hybrid Learning Model for Intrusion Detection System: A Combination of Parametric and Non-Parametric Classifiers,” Alexandria Engineering Journal, vol. 112, pp. 384-396, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  15. A. Puviarasu, and V.K. Sudha, “Enhanced IoT Security: Privacy-Preserving Federated Learning Model for Accurate, Real-time Intrusion Detection across Devices,” Ain Shams Engineering Journal, vol. 17, no. 1, pp. 1-18, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  16. Dainan Zhang et al., “A Lightweight IoT Intrusion Detection Method based on Two-Stage Feature Selection and Bayesian Optimization,” AIMS Electronics and Electrical Engineering, vol. 9, no. 3, pp. 359-389, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  17. Naveen Saran, and Nishtha Kesswani, “An Analytics-driven Framework for Securing Industrial IoT-Enabled Supply Chain Management Systems,” Supply Chain Analytics, vol. 11, pp. 1-11, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  18. Abdulla Amin Aburomman, Marwan Alakhras, and Mamun Bin Ibne Reaz, “Leakage-Safe IoT Intrusion Detection with White Shark Optimizer-based Feature Selection,” Array, vol. 30, pp. 1-10, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  19. Samar M. Zayed, Samah Alshathri, and Walid El-Shafai, “Firefly Algorithm Optimized Hybrid Deep Learning Framework for Intrusion Detection in IoT Environments,” International Journal of Computational Intelligence Systems, vol. 19, pp. 1-33, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  20. Abdulaziz Aldaej et al., “Ensemble Technique of Intrusion Detection for IoT-Edge Platform,” Scientific Reports, vol. 14, pp. 1-16, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  21. G. Logeswari et al., “An Improved Synergistic Dual-Layer Feature Selection Algorithm with Two Type Classifier for Efficient Intrusion Detection in IoT Environment,” Scientific Reports, vol. 15, pp. 1-21, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  22. N. Satheesh Kumar et al., “Swarm-based Intelligent Models for Developing Cybersecurity Frameworks with IDS,” Scientific Reports, vol. 16, pp. 1-32, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  23. Silin Peng et al., “ROSE-BOX: A Lightweight and Efficient Intrusion Detection Framework for Resource-Constrained IIoT Environments,” Applied Sciences, vol. 15, no. 12, pp. 1-23, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  24. Khawla Shalabi, Qasem Abu Al-Haija, and Mustafa Al-Fayoumi, “A Blockchain-based Intrusion Detection/Prevention Systems in IoT Network: A Systematic Review,” Procedia Computer Science, vol. 236, pp. 410-419, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  25. Arbia Riahi Sfar et al., “A Roadmap for Security Challenges in the Internet of Things,” Digital Communications and Networks, vol. 4, no. 2, pp. 118-137, 2018.
    [CrossRef] [Google Scholar] [Publisher Link]
  26. Yuqiang Wu, Bailin Zou, and Yifei Cao, “Current Status and Challenges and Future Trends of Deep Learning-Based Intrusion Detection Models,” Journal of Imaging, vol. 10, no. 10, pp. 1-25, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  27. Hessah A. Alsalamah, and Walaa N. Ismail, “A Swarm-Based Multi-Objective Framework for Lightweight and Real-Time IoT Intrusion Detections,” Mathematics, vol. 13, no. 15, pp. 1-32, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  28. Alyazia Aldhaheri et al., “Deep Learning for Cyber Threat Detection in IoT Networks: A Review,” Internet of Things and Cyber-Physical Systems, vol. 4, pp. 110-128, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  29. Shereen Ismail, Salah Dandan, and Ala'a Qushou, “Intrusion Detection in IoT and IIoT: Comparing Lightweight Machine Learning Techniques Using TON_IoT, WUSTL-IIOT-2021, and EdgeIIoTset Datasets,” IEEE Access, vol. 13, pp. 73468-73485, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  30. M. Arivukarasi et al., “Federated Autoencoder IDS for IoT: A Fed-ANIDS Approach using CICIoT2023,” Systems and Soft Computing, vol. 8, pp. 1-14, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  31. Ashish K. Sharma et al., Securing IoT Environments: Deep Learning-Based Intrusion Detection, Deep Learning for Intrusion Detection: Techniques and Applications, Wiley, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  32. Gede Teguh Satya Dharma et al., “Weighted ANOVA and Mutual Information for Enhanced Intrusion Detection System,” KINETIK: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, vol. 11, no. 1, pp. 113-122, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  33. Babatunde Olanrewaju-George, and Bernardi Pranggono, “Federated Learning-based Intrusion Detection System for the Internet of Things using Unsupervised and Supervised Deep Learning Models,” Cyber Security and Applications, vol. 3, pp. 1-10, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  34. Elijah Mwandata Maseno, Zenghui Wang, and Yanxia Sun, “Performance Evaluation of Intrusion Detection Systems on the TON_IoT Datasets Using a Feature Selection Method,” Proceedings of the 2024 8th International Conference on Computer Science and Artificial Intelligence, Beijing China, pp. 607-613, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  35. Srinivas Cheekati et al., “Intelligent Cybersecurity for IoT: A Hybrid QRIME-SDPN Approach for Network Attack Detection on CIC-IoT-2023,” 2025 13th International Conference on Smart Grid (icSmartGrid), Glasgow, United Kingdom, pp. 774-781, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  36. N. Dharini, V. S. Janani, and Jeevaa Katiravan, “Efficient Detection of Intrusions in TON-IoT Dataset using Hybrid Feature Selection Approach,” Scientific Reports, vol. 16, pp. 1-29, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  37. Gaurav Meena, and Ajay Indian, “IDS-IoT: Intrusion Detection System for the Internet of Things Using Enhanced Long-Short Term Memory,” Artificial Intelligence and Applications, Online First, pp. 1-18, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  38. Jamal Alotaibi, “AI-driven Intrusion Detection and Mitigation Framework for Software-defined IoT Networks,” Peer-to-Peer Networking and Applications, vol. 18, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]