Interplay of ML and blockchain for secure Internet of Military Vehicles communication underlying 5G

IF 4.8 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Maulik Sojitra , Nilesh Kumar Jadav , Rajesh Gupta , Usha Patel , Janam Patel , Sudeep Tanwar , Giovanni Pau , Fayez Alqahtani , Amr Tolba
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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.
ML和区块链的相互作用,用于5G基础上的安全军车联网通信
物联网(IoT)网络迅速改变了包括现代战争在内的各个领域,其中军用车辆互联网(iomv)实现了远程连接,监控和数据共享。然而,IoMV传感器缺乏固有的安全措施来对抗DDoS、干扰和欺骗等威胁。依赖人工智能的传统安全解决方案面临着特征选择效率低下、缺乏透明度、易受数据篡改等挑战。在本文中,我们提出了一种基于AI和区块链的战场IoMV网络安全数据交换架构。我们的方法采用可解释人工智能(XAI)技术进行最佳特征选择,并使用五种不同的机器学习算法对恶意和非恶意数据进行分类。值得注意的是,XGBoost模型达到了98.8%的准确率。非恶意数据被安全地转发到区块链网络,在那里智能合约验证其合法性,并使用星际文件系统(IPFS)进行链下存储,以增强可扩展性并降低存储成本。此外,利用低延迟5G通信确保快速可靠的数据传输。这种集成了用于实时威胁检测的人工智能、用于防篡改存储的区块链和用于增强通信的5G,通过实现安全和有效的决策,显著改善了战场作战。
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来源期刊
Ad Hoc Networks
Ad Hoc Networks 工程技术-电信学
CiteScore
10.20
自引率
4.20%
发文量
131
审稿时长
4.8 months
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.
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