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arXiv 2302.13865cs.CR
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面向5G及未来网络的AI驱动容器安全方法:综述

AI-Driven Container Security Approaches for 5G and Beyond: A Survey

Ilter Taha Aktolga, Elif Sena Kuru, Yigit Sever, Pelin Angin

AI总结:

本文为面向5G及未来网络的AI驱动容器安全综述,梳理了容器安全领域的研究方向与分类,重点调研了利用机器学习应对安全挑战的相关方法。

摘要:

基于微服务的软件部署在云端的应用日益广泛,这一趋势大量利用了容器化软件。容器内部运行的应用以及容器环境本身的安全,是云环境与5G中的关键基础设施。为解决安全隐患,研究工作聚焦于容器安全领域,涵盖入侵检测、恶意软件检测、容器放置策略等子方向。这些安全研究大致可分为两类:基于规则的方法,以及能够应对新型威胁的机器学习方法。本研究针对容器安全相关文献展开调研,重点关注利用机器学习应对安全挑战的方法。

英文摘要:

The rising use of microservices based software deployment on the cloud leverages containerized software extensively. The security of applications running inside containers as well as the container environment itself are critical infrastructure in the cloud setting and 5G. To address the security concerns, research efforts have been focused on container security with subfields such as intrusion detection, malware detection and container placement strategies. These security efforts are roughly divided into two categories: rule based approaches and machine learning that can respond to novel threats. In this study, we have surveyed the container security literature focusing on approaches that leverage machine learning to address security challenges.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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