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A Monte Carlo-based exploration framework for identifying components vulnerable to cyber threats in nuclear power plants

机译:基于蒙特卡洛的探索框架,用于识别易受核电厂网络威胁的组件

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摘要

With the extensive use of digital Instrumentation and Control (I&C) systems, Nuclear Power Plants (NPPs) are becoming Cyber-Physical Systems (CPSs). Their integrity can, then, be compromised also by security breaches (such as cyber attacks). Multiple failure modes (such as bias, drift and freezing) can occur, both due to random failures or induced by malicious external attacks. In this paper, we illustrate an exploration approach that, based on safety margins estimation, allows identifying the most vulnerable components to malicious external attacks. For demonstration, we apply the approach to the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED). Its object-oriented model is embedded within a Monte Carlo (MC)-driven engine that injects different types of cyber attacks at random times and magnitudes. Safety margins are, then, calculated and used for identifying the most vulnerable CPS components. This allows selecting protections to make ALFRED resilient towards maliciously induced failures. (C) 2018 Elsevier Ltd. All rights reserved.
机译:随着数字仪表和控制(I&C)系统的广泛使用,核电站(NPP)逐渐成为网络物理系统(CPS)。然后,安全漏洞(例如网络攻击)也可能损害其完整性。可能由于随机故障或恶意外部攻击而导致多种故障模式(例如偏差,漂移和冻结)。在本文中,我们说明了一种探索方法,该方法基于安全边际估计,可以确定最容易受到恶意外部攻击的组件。为了演示,我们将该方法应用于先进的铅冷却快堆欧洲演示器(ALFRED)。它的面向对象模型嵌入在蒙特卡洛(MC)驱动的引擎中,该引擎在随机的时间和大小上注入不同类型的网络攻击。然后,计算安全裕度并将其用于识别最易受攻击的CPS组件。这样可以选择保护措施,以使ALFRED能够抵抗恶意诱发的故障。 (C)2018 Elsevier Ltd.保留所有权利。

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