Stuxnet worm impact on industrial cyber-physical system security

Karnouskos, Stamatis

doi:10.1109/iecon.2011.6120048

Cited by 381 publications

(181 citation statements)

References 15 publications

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“…For example, Stuxnet which attacks programmable logic controllers (PLCs). Stuxnet in June 2010 struck an Iranian nuclear facility at Nantaz attacking centrifuges for separating nuclear materials [43,44]. Linux.Darlloz is a worm that is capable of infecting wide range of devices such as home routers and set up boxes [45].…”

Section: Malware Definition and Characterizationmentioning

confidence: 99%

Internet of Things Malware : A Survey

Karanja¹,

Masupe²,

Jeffrey³

2017

IJCSES

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Section: Malware Definition and Characterizationmentioning

confidence: 99%

Internet of Things Malware : A Survey

Karanja¹,

Masupe²,

Jeffrey³

2017

IJCSES

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“…SCADAsystemsecurityvulnerabilitieswerefirsthighlightedbytheStuxnetattack (Karnouskos, 2011). Subsequently,therehasbeenanincreaseinthefrequencyandsophistication,oftheattacks asevidencedbyConstantin(2014).…”

Section: Introductionmentioning

confidence: 99%

Autonomic computing meets SCADA security

Nazir¹,

Patel

2017

2017 IEEE 16th International Conference on Cognitive Informatics &Amp; Cognitive Computing (ICCI*CC)

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“…One prominent such case was reported for the Iranean nuclear power plants, which got infected by the Stuxnet worm [22] via BYOD. Although BYOD and protection against it has received considerable attention in the literature [23]- [26], an epidemics-like treatment of malware infections (e.g., caused by BYOD) for risk management is not available so far.…”

Section: B Malware Infections and Percolation Theorymentioning

confidence: 99%

Risk Propagation Analysis and Visualization using Percolation Theory

König¹,

Raß²,

Schauer³

et al. 2016

ijacsa

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Abstract-This article presents a percolation-based approach for the analysis of risk propagation, using malware spreading as a showcase example. Conventional risk management is often driven by human (subjective) assessment of how one risk influences the other, respectively, how security incidents can affect subsequent problems in interconnected (sub)systems of an infrastructure. Using percolation theory, a well-established methodology in the fields of epidemiology and disease spreading, a simple simulationbased method is described to assess risk propagation systematically. This simulation is formally analyzed using percolation theory, to obtain closed form criteria that help predicting a pandemic incident propagation (or a propagation with averagecase bounded implications). The method is designed as a security decision support tool, e.g., to be used in security operation centers. For that matter, a flexible visualization technique is devised, which is naturally induced by the percolation model and the simulation algorithm that derives from it. The main output of the model is a graphical visualization of the infrastructure (physical or logical topology). This representation uses color codes to indicate the likelihood of problems to arise from a security incident that initially occurs at a given point in the system. Large likelihoods for problems thus indicate "hotspots", where additional action should be taken.

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Stuxnet worm impact on industrial cyber-physical system security

Cited by 381 publications

References 15 publications

Internet of Things Malware : A Survey

Internet of Things Malware : A Survey

Autonomic computing meets SCADA security

Risk Propagation Analysis and Visualization using Percolation Theory

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