通过机器学习注入的NEXT-G和超越生态系统的主动防御分析,确保数字主权和信任的现代威胁情报

Sudhakar Kumar , Sunil K. Singh , Rakesh Kumar , Chandra Kumari Subba , Kwok Tai Chui , Brij B. Gupta
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引用次数: 0

摘要

在网络物理系统(CPS)和物联网(IoT)领域,本研究提出了一种新方法,通过为NEXT-G和超越生态系统注入ml的主动防御分析,确保近代威胁情报的数字主权和信任。随着第六代及以后(6G和B)无线网络的快速发展,我们的框架旨在通过利用先进的机器学习算法来主动预测和应对安全事件。这种方法有效地解决了传统模型的缺点,确保数字资产和通信保持安全、可信和合法控制。该研究深入研究了该范式在NextG网络架构中的理论整合,通过动态和适应性防御机制加强数字主权。深入的技术检查包括先进的机器学习算法、自适应防御和可扩展性考虑。通过批判性地分析和比较现有的安全方法,本研究显着推进了无线网络安全的技术知识和实际应用,支持防御6G及以后时代不断发展和复杂的威胁特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neoteric Threat Intelligence Ensuring Digital Sovereignty and Trust through ML-Infused Proactive Defense Analytics for NEXT-G and Beyond Ecosystems
In the domain of Cyber-Physical Systems (CPS) and the Internet of Things (IoT), this research presents a novel approach to Neoteric Threat Intelligence ensuring Digital Sovereignty and Trust through ML-Infused Proactive Defense Analytics for NEXT-G and Beyond Ecosystems. As Sixth Generation and Beyond (6G and B) wireless networks undergo rapid evolution, our framework is designed to proactively anticipate and counter security incidents by utilizing advanced machine learning algorithms. This approach effectively addresses the shortcomings of conventional models, ensuring that digital assets and communications remain secure, trustworthy, and under rightful control. The study delves into the theoretical integration of this paradigm within the NextG network architecture, reinforcing digital sovereignty through a dynamic and adaptable defense mechanism. In-depth technical examinations include advanced machine learning algorithms, adaptive defenses, and scalability considerations. By critically analyzing and comparing existing security approaches, this study significantly advances technical knowledge and practical applications for wireless network security, supporting defenses against the evolving and complex threats characteristic of the 6G and Beyond era.
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CiteScore
4.50
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