AI-Blockchain Approach for MQTT Security: A Supply Chain Case Study

The use of the MQTT protocol in critical sectors such as healthcare and industry has prompted research to propose solutions for strengthening its security and preventing it from attacks that are growing exponentially and becoming increasingly sophisticated and difficult to detect. This paper aims to improve the security of the MQTT architecture, ensuring it is resilient to current attacks and adaptable to potential future attacks while considering the constraints of the IoT environment. To achieve this, the proposed architecture is based on the interaction between the AI model, which continuously analyzes device behavior, and smart contracts, which automatically apply appropriate security measures once fraud is detected. A device reputation mechanism is used to prevent malicious devices from rejoining the network. The AI model proposed in this article was initially trained on a set of malicious behaviors using supervised learning. The results show that the detection accuracy achieved 95.97%. This accuracy is expected to improve over time through the integration of un-supervised learning into the architecture, enabling the discovery of new attack patterns and additional parameters for malicious behavior identification. For simulation testing, the architecture was applied to supply chain management as a case study of critical applications, and smart contracts were deployed in the Remix environment. The architecture demonstrated resilience and robustness across various attack scenarios.

@article{AKNIN2024,
  title     = {AI-Blockchain Approach for MQTT Security: A Supply Chain Case Study},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Raouya AKNIN and Hind El Makhtoum and Youssef Bentaleb},
  doi       = {10.14569/IJACSA.2024.01511128},
  url       = {https://doi.org/10.14569/IJACSA.2024.01511128}
}