The THREAT-ARREST Cyber Range Platform

George Hatzivasilis, S. Ioannidis, Michail Smyrlis, G. Spanoudakis, Fulvio Frati, C. Braghin, E. Damiani, Hristo Koshutanski, George Tsakirakis, T. Hildebrandt, Ludger Goeke, Sebastian Pape, Oleg Blinder, M. Vinov, G. Leftheriotis, M. Kunc, Fotis Oikonomou, Giovanni Magilo, Vito Petrarolo, A. Chieti, Robert Bordianu
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引用次数: 4

Abstract

Emerging technologies are facilitating our daily activities and drive the digital transformation. The Internet of Things (IoT) and 5G communications will provide a wide range of new applications and business opportunities, but with a wide and quite complex attack surface. Several users are not aware of the underlying threats and most of them do not possess the knowledge to set and operate the various digital assets securely. Therefore, cyber security training is becoming mandatory both for simple users and security experts. Cyber ranges constitute an advance training technique where trainees gain hands-on experiences on a safe virtual environment, which can be a realistic digital twin of an actual system. This paper presents the cyber ranges platform THREAT-ARREST. Its design is fully model-driven and offers all modern training features (i.e. emulation, simulation, serious games, and fabricated data). The platform has been evaluated under the smart energy, intelligent transportation, and healthcare domains.
威胁-逮捕网络靶场平台
新兴技术正在为我们的日常活动提供便利,并推动数字化转型。物联网(IoT)和5G通信将提供广泛的新应用和商业机会,但具有广泛且相当复杂的攻击面。一些用户没有意识到潜在的威胁,他们中的大多数人不具备安全设置和操作各种数字资产的知识。因此,网络安全培训对简单用户和安全专家来说都是强制性的。网络训练场是一种先进的培训技术,学员可以在安全的虚拟环境中获得实践经验,虚拟环境可以是实际系统的现实数字孪生体。本文介绍了网络靶场平台THREAT-ARREST。它的设计完全是模型驱动的,并提供所有现代训练功能(即仿真,模拟,严肃游戏和捏造的数据)。该平台已在智能能源、智能交通和医疗保健领域进行了评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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