What question did this study set out to answer?

The aim is to develop a secure framework combining cryptography and machine learning for IoT security in resource-limited environments.

June 28, 2026Scientific Reports0 citationsOpen Access

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

GTGaurav ThakurCentral University of Himachal Pradesh PCPradeep ChoukseyCentral University of Himachal Pradesh MCMayank ChopraCentral University of Himachal Pradesh

Key Points

The aim is to develop a secure framework combining cryptography and machine learning for IoT security in resource-limited environments.
Integrated cryptographic techniques with machine learning-based anomaly detection.
Used Elliptic Curve Cryptography (ECC) for key exchange and SPECK for lightweight encryption.
Evaluated the framework on a Raspberry Pi using the CIC-BCCC-NRC-IoT-2023 dataset.
Achieved an accuracy of 89.5% and an F1-score of 90%.
Average end-to-end latency measured at 1.08 ms.
Energy consumption was approximately 4.5 mJ per inference.

Abstract

Abstract 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.

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