Abstract: Given the proliferation of connected devices and the evolving threat landscape, intrusion detection plays a pivotal role in safeguarding IoT networks. However, traditional methodologies struggle to adapt to the dynamic and diverse settings of IoT environments. To address these challenges, this study proposes an innovative framework that leverages machine learning, specifically Red Fox Optimization (RFO) for feature selection, and Attention-based Bidirectional Long Short-Term Memory (Bi-LSTM). Additionally, the integration of blockchain technology is explored to provide immutable and tamper-proof logs of detected intrusions, bolstering the overall security of the system. Previous research has highlighted the limitations of conventional intrusion detection techniques in IoT networks, particularly in accommodating diverse data sources and rapidly evolving attack strategies. The attention mechanism enables the model to concentrate on pertinent features, enhancing the accuracy and efficiency of anomaly and malicious activity detection in IoT traffic. Furthermore, the utilization of RFO for feature selection aims to reduce data dimensionality and enhance the scalability of the intrusion detection system. Moreover, the inclusion of blockchain technology enhances security by ensuring the integrity and immutability of intrusion detection logs. The proposed framework is implemented using Python for machine learning tasks and Solidity for blockchain development. Experimental findings demonstrate the efficacy of the approach, achieving a detection accuracy of approximately 98.9% on real-world IoT datasets. These results underscore the significance of the research in advancing IoT security practices. By amalgamating machine learning, optimization techniques, and blockchain technology, this framework provides a robust and scalable solution for intrusion detection in IoT networks, fostering improved efficiency and security in interconnected environments.

N. Sunanda, K. Shailaja, Prabhakar Kandukuri, Krishnamoorthy, Vuda Sreenivasa Rao and Sanjiv Rao Godla, “Enhancing IoT Network Security: ML and Blockchain for Intrusion Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 15(4), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150497

@article{Sunanda2024,
title = {Enhancing IoT Network Security: ML and Blockchain for Intrusion Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150497},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150497},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {4},
author = {N. Sunanda and K. Shailaja and Prabhakar Kandukuri and Krishnamoorthy and Vuda Sreenivasa Rao and Sanjiv Rao Godla}
}