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<p>The rapid expansion of Internet of Things (IoT) systems has introduced significant security challenges, particularly in resource-constrained environments where traditional security mechanisms are often impractical. This paper presents a secure and lightweight hybrid framework that integrates cryptographic techniques with machine learning–based anomaly detection for IoT-based cyber defense. The proposed framework employs Elliptic Curve Cryptography (ECC) for key exchange, SPECK for lightweight encryption, and SHA-3 for data integrity, combined with a Random Forest classifier for anomaly detection. The framework is implemented and evaluated on a Raspberry Pi–based edge environment using the CIC-BCCC-NRC-IoT-2023 dataset. Experimental results demonstrate an accuracy of 89.5% and an F1-score of 90%, with an average end-to-end latency of 1.08 ms and energy consumption of approximately 4.5 mJ per inference. These results indicate that the proposed approach achieves a practical balance between security, computational efficiency, and detection performance under constrained conditions. While the framework shows promising results, its evaluation is limited to a controlled setup and a single primary dataset. Future work will focus on cross-dataset validation, adversarial robustness, and large-scale deployment analysis.</p>

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A secure and lightweight cryptographic-machine learning framework for IoT-based cyber defense in resource-constrained environments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月26日

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  • Gaurav Thakur,
  • Pradeep Chouksey,
  • Mayank Chopra,
  • Parveen Sadotra,
  • Diksha Sharma,
  • Arpit Koundal,
  • Sunil Kumar

摘要

The rapid expansion of Internet of Things (IoT) systems has introduced significant security challenges, particularly in resource-constrained environments where traditional security mechanisms are often impractical. This paper presents a secure and lightweight hybrid framework that integrates cryptographic techniques with machine learning–based anomaly detection for IoT-based cyber defense. The proposed framework employs Elliptic Curve Cryptography (ECC) for key exchange, SPECK for lightweight encryption, and SHA-3 for data integrity, combined with a Random Forest classifier for anomaly detection. The framework is implemented and evaluated on a Raspberry Pi–based edge environment using the CIC-BCCC-NRC-IoT-2023 dataset. Experimental results demonstrate an accuracy of 89.5% and an F1-score of 90%, with an average end-to-end latency of 1.08 ms and energy consumption of approximately 4.5 mJ per inference. These results indicate that the proposed approach achieves a practical balance between security, computational efficiency, and detection performance under constrained conditions. While the framework shows promising results, its evaluation is limited to a controlled setup and a single primary dataset. Future work will focus on cross-dataset validation, adversarial robustness, and large-scale deployment analysis.