A Monte Carlo-based exploration framework for identifying components vulnerable to cyber threats in nuclear power plants. (July 2018)

• Record Type: Journal Article
• Authors: Wang, Wei; Cammi, Antonio; Di Maio, Francesco; Lorenzi, Stefano; Zio, Enrico
• Is Part Of: Reliability engineering & system safety. Volume 175(2018)
• Page Start: 24
• Page End: 37
• Publication Date: 2018-07
• Subjects: Nuclear Power Plant -- Cyber-Physical System -- Cyber threats -- Safety margins -- Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED); LFR Lead-cooled Fast Reactor -- ALFRED Advanced Lead-cooled Fast Reactor European Demonstrator -- CPS Cyber-Physical System -- CR Control Rod -- DoS Denial of Service -- FA Fuel Assemblies -- I&C Instrumentation and Control -- MC Monte Carlo -- NPP Nuclear Power Plant -- OS Order Statistics -- PI Proportional-Integral -- SG Steam Generator -- SISO Single Input Single Output; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2018.03.005
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: An exploration framework for identifying CPS vulnerabilities to cyber threats is proposed. Cyber attack scenarios are explored by Monte Carlo sampling. A safety margin estimation approach is proposed for cyber threat prioritization. The framework is illustrated with respect to the digital I&C system of an ALFRED simulator. Abstract: 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.