{"title":"Interplay of ML and blockchain for secure Internet of Military Vehicles communication underlying 5G","authors":"Maulik Sojitra , Nilesh Kumar Jadav , Rajesh Gupta , Usha Patel , Janam Patel , Sudeep Tanwar , Giovanni Pau , Fayez Alqahtani , Amr Tolba","doi":"10.1016/j.adhoc.2025.103968","DOIUrl":null,"url":null,"abstract":"<div><div>Internet of Things (IoT) networks have rapidly transformed various sectors, including modern warfare, where Internet of Military Vehicles (IoMVs) enable remote connection, monitoring, and data sharing. However, IoMV sensors lack inherent security measures to combat threats such as DDoS, jamming, and spoofing. Traditional security solutions relying on AI face challenges such as inefficient feature selection, lack of transparency, and susceptibility to data tampering. In this paper, we propose an AI and Blockchain based secure data exchange architecture for battlefield IoMV networks. Our approach employs an Explainable Artificial Intelligence (XAI) technique for optimal feature selection and uses five different Machine Learning algorithms to classify malicious and non-malicious data. Notably, the XGBoost model achieves an accuracy of 98.8%. Non-malicious data is securely forwarded to a blockchain network, where a smart contract validates its legitimacy, and stored off-chain using the Inter-Planetary File System (IPFS) to enhance scalability and reduce storage costs. Additionally, leveraging low latency 5G communication ensures rapid and reliable data transmission. This integration of AI for real-time threat detection, blockchain for tamper-proof storage, and 5G for enhanced communication significantly improves battlefield operations by enabling secure and efficient decision-making.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103968"},"PeriodicalIF":4.8000,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ad Hoc Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1570870525002161","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Internet of Things (IoT) networks have rapidly transformed various sectors, including modern warfare, where Internet of Military Vehicles (IoMVs) enable remote connection, monitoring, and data sharing. However, IoMV sensors lack inherent security measures to combat threats such as DDoS, jamming, and spoofing. Traditional security solutions relying on AI face challenges such as inefficient feature selection, lack of transparency, and susceptibility to data tampering. In this paper, we propose an AI and Blockchain based secure data exchange architecture for battlefield IoMV networks. Our approach employs an Explainable Artificial Intelligence (XAI) technique for optimal feature selection and uses five different Machine Learning algorithms to classify malicious and non-malicious data. Notably, the XGBoost model achieves an accuracy of 98.8%. Non-malicious data is securely forwarded to a blockchain network, where a smart contract validates its legitimacy, and stored off-chain using the Inter-Planetary File System (IPFS) to enhance scalability and reduce storage costs. Additionally, leveraging low latency 5G communication ensures rapid and reliable data transmission. This integration of AI for real-time threat detection, blockchain for tamper-proof storage, and 5G for enhanced communication significantly improves battlefield operations by enabling secure and efficient decision-making.
期刊介绍:
The Ad Hoc Networks is an international and archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in ad hoc and sensor networking areas. The Ad Hoc Networks considers original, high quality and unpublished contributions addressing all aspects of ad hoc and sensor networks. Specific areas of interest include, but are not limited to:
Mobile and Wireless Ad Hoc Networks
Sensor Networks
Wireless Local and Personal Area Networks
Home Networks
Ad Hoc Networks of Autonomous Intelligent Systems
Novel Architectures for Ad Hoc and Sensor Networks
Self-organizing Network Architectures and Protocols
Transport Layer Protocols
Routing protocols (unicast, multicast, geocast, etc.)
Media Access Control Techniques
Error Control Schemes
Power-Aware, Low-Power and Energy-Efficient Designs
Synchronization and Scheduling Issues
Mobility Management
Mobility-Tolerant Communication Protocols
Location Tracking and Location-based Services
Resource and Information Management
Security and Fault-Tolerance Issues
Hardware and Software Platforms, Systems, and Testbeds
Experimental and Prototype Results
Quality-of-Service Issues
Cross-Layer Interactions
Scalability Issues
Performance Analysis and Simulation of Protocols.