Cybersecurity for tactical 6G networks: Threats, architecture, and intelligence

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-08-26 DOI:10.1016/j.future.2024.107500
Jani Suomalainen , Ijaz Ahmad , Annette Shajan , Tapio Savunen
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Abstract

Edge intelligence, network autonomy, broadband satellite connectivity, and other concepts for private 6G networks are enabling new applications for public safety authorities, e.g., for police and rescue personnel. Enriched situational awareness, group communications with high-quality video, large scale IoT, and remote control of vehicles and robots will become available in any location and situation. We analyze cybersecurity in intelligent tactical bubbles, i.e., in autonomous rapidly deployable mobile networks for public safety operations. Machine learning plays major roles in enabling these networks to be rapidly orchestrated for different operations and in securing these networks from emerging threats, but also in enlarging the threat landscape. We explore applicability of different threat and risk analysis methods for mission-critical networked applications. We present the results of a joint risk prioritization study. We survey security solutions and propose a security architecture, which is founded on the current standardization activities for terrestrial and non-terrestrial 6G and leverages the concepts of machine learning-based security to protect mission-critical assets at the edge of the network.

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战术 6G 网络的网络安全:威胁、架构和情报
边缘智能、网络自治、宽带卫星连接和其他专用 6G 网络概念正在为公共安全机构(如警察和救援人员)带来新的应用。丰富的态势感知、带有高质量视频的群组通信、大规模物联网以及车辆和机器人的远程控制将在任何地点和情况下可用。我们分析了智能战术气泡中的网络安全,即公共安全行动中的自主快速部署移动网络。机器学习在使这些网络能够快速协调不同行动、保护这些网络免受新兴威胁以及扩大威胁范围方面发挥着重要作用。我们探索了不同威胁和风险分析方法在关键任务网络应用中的适用性。我们介绍了联合风险优先级研究的结果。我们对安全解决方案进行了调查,并提出了一个安全架构,该架构建立在当前地面和非地面 6G 标准化活动的基础上,并利用基于机器学习的安全概念来保护网络边缘的关键任务资产。
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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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