Model-based Security Metrics Using ADversary VIew Security Evaluation (ADVISE)

LeMay, Elizabeth; Ford, Michael; Keefe, Ken; Sanders, William H.; Muehrcke, Carol

doi:10.1109/qest.2011.34

Cited by 134 publications

(92 citation statements)

References 9 publications

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“…[28,23] propose models to assess system security against classes of adversaries characterized by user-specified 'profiles'. While these models share some similarities with ours -in particular, they too employ mdp's to keep track of possible adversary strategies -their intent is quite different from ours: they are used to build and assess analysis tools, rather than to obtain analytical results.…”

Section: Boreale and F Pampalonimentioning

confidence: 99%

Quantitative Information Flow under Generic Leakage Functions and Adaptive Adversaries

Boreale

Pampaloni

2014

Formal Techniques for Distributed Objects, Components, and Systems

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Abstract. We put forward a model of action-based randomization mechanisms to analyse quantitative information flow (qif) under generic leakage functions, and under possibly adaptive adversaries. This model subsumes many of the qif models proposed so far. Our main contributions include the following: (1) we identify mild general conditions on the leakage function under which it is possible to derive general and significant results on adaptive qif; (2) we contrast the efficiency of adaptive and non-adaptive strategies, showing that the latter are as efficient as the former in terms of length up to an expansion factor bounded by the number of available actions; (3) we show that the maximum information leakage over strategies, given a finite time horizon, can be expressed in terms of a Bellman equation. This can be used to compute an optimal finite strategy recursively, by resorting to standard methods like backward induction.

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Section: Boreale and F Pampalonimentioning

confidence: 99%

Quantitative Information Flow under Generic Leakage Functions and Adaptive Adversaries

Boreale

Pampaloni

2014

Formal Techniques for Distributed Objects, Components, and Systems

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“…Similarly, Sarraute et al [7] generate attack scenarios by taking into account lack of knowledge an adversary has about the network topology. LeMay et al [8] represent explicitly how an adversary is likely to attack the system depending on her preferences, actions cost, payoff and probability of detection. However, automated attack planning research has not addressed the problem of effective evidence collection in the cloud.…”

Section: Related Workmentioning

confidence: 99%

“…Subsequently, exploits a vulnerability of to mask her username and escalate her privileges (steps 4-5). Once has escalated her privileges, she can gain command line access and copy the credentials of another user, e.g., (steps [7][8]. This is due to the fact that anytime a user accesses the database, her credentials are written in the MongoDB log file (steps 6).…”

Section: Overhead Of Evidence Collection Activitiesmentioning

confidence: 99%

“…Attack scenarios represent a sequence of actions an adversary can perform to achieve her criminal goals (e.g., compromising a physical or virtual machine, or copying a VM image). Automated generation of attack scenarios [5,4,6,7,8] has been utilised extensively to identify the security breaches that are likely and to focus the security strategy of an organisation on their prevention. However, this might not always be possible; for example, as a security patch might not yet be available or a cloud provider may have to rely on the customer to apply such patches.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive evidence collection in the cloud using attack scenarios

Pasquale

Hanvey

Mcgloin

et al. 2016

Computers & Security

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“…MulVAL (Ou et al 2006;Huang et al 2011), NetSPA (Ingols et al 2009), or the TVA-tool (Jajodia et al 2005)). Some tools also require extensive expertise for manual configuration, such as the cyber-physical cyber security analysis tools ADVISE (LeMay et al 2011) and CyberSAGE (Vu et al 2014). The authors of this paper use an advanced threat modelling and attack simulation tool that has been designed to overcome the mentioned problems and is based on numerous research studies Holm et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Load balancing of renewable energy: a cyber security analysis

et al. 2018

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Background: In the coming years, the increase of automation in electricity distribution grids, controlled by ICT, will bring major consequences to the cyber security posture of the grids. Automation plays an especially important role in load balancing of renewable energy where distributed generation is balanced to load in a way that the grid stability is ensured. Threats to the load balancing and the smart grid in general arise from the activities of misbehaving or rouge actors in combination with poor design, implementation, or configuration of the system that makes it vulnerable. It is urgent to conduct an in-depth analysis about the feasibility and imminency of these potential threats ahead of a cyber catastrophy. This paper presents a cyber security evaluation of the ICT part of the smart grid with a focus on load balancing of renewable energy. Method: The work builds on a load balancing centered smart grid reference architecture model that is designed as part of the evaluation with the help of SCADA system and smart grid experts. The smart grid load balancing architecture represented by the model is then analyzed using a threat modelling approach that is encapsulated in a tool called securiCAD. Countermeasures are introduced in the model to measure how much each improve the cyber security of the smart grid.

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Model-based Security Metrics Using ADversary VIew Security Evaluation (ADVISE)

Cited by 134 publications

References 9 publications

Quantitative Information Flow under Generic Leakage Functions and Adaptive Adversaries

Quantitative Information Flow under Generic Leakage Functions and Adaptive Adversaries

Adaptive evidence collection in the cloud using attack scenarios

Load balancing of renewable energy: a cyber security analysis

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