Machine Learning and Blockchain-based Secure Communication for Internet of Military Vehicles (IoMV) Underlying 5G Networks.

Abstract

The rapid advancement of modern technology has led to increased utilization of the Internet of Things (IoT) across various sectors. This includes the integration of IoT in battlefield networks, enabling seamless connectivity and data exchange. By leveraging IoT devices and sensors, battlefield networks can enhance operational efficiency, real-time monitoring, and decision-making capabilities, thereby improving military operations' effectiveness. However, due to the critical nature of battlefield networks, they are vulnerable to network attacks such as cyber-attacks, jamming, and spoofing. We propose an AI and Blockchain-based secure data exchange framework for battlefield operations to address this issue. This paper uses the "5G-NIDD" dataset and applies Explainable Artificial Intelligence (XAI) for essential feature selection. Additionally, we employ five different Machine Learning (ML) algorithms to classify malicious and non-malicious battlefield data. Non-malicious data is securely passed through the blockchain layer, while malicious data is eliminated from the network. To mitigate computational complexity and ensure scalability, we leverage the low latency and high reliability of the 5G channel. Our results demonstrate that the XGBoost model outperforms other algorithms with 98.8% accuracy. Furthermore, we achieve high scalability, low latency, and reduced data storage costs by using the InterPlanetary File System (IPFS) with 5G technology.