Survivable network system analysis: a case study

Ellison, Robert J.; Linger, Richard C.; Longstaff, Thomas A.; Mead, Nancy R.

doi:10.1109/52.776952

Cited by 98 publications

(64 citation statements)

References 4 publications

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“…Survivability strategies [3] -that include resistance, recognition and recovery phases-aim at providing protection for critical infrastructures in the presence of attacks. Since the attacks of September 11, 2001, to the Twin Towers there has been an even growing interest by the worldwide communities on the physical and cyber-security issues in critical infrastructures [1], so security and survivability should be considered as first-class properties in the system development life-cycle.…”

Section: Introductionmentioning

confidence: 99%

Modelling Security of Critical Infrastructures: A Survivability Assessment

Rodríguez

Merseguer

Bernardi

2014

The Computer Journal

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Critical infrastructures, usually designed to handle disruptions caused by human errors or random acts of nature, define assets whose normal operation must be guaranteed to maintain its essential services for human daily living. Malicious intended attacks to these targets need to be considered during system design. To face with these situations, defense plans must be developed in advance. In this paper, we present a UML profile, named SecAM, that enables the modelling and security specification for critical infrastructures during the early phases (requirements, design) of systems development life-cycle. SecAM endows security assessment, through survivability analysis, of different security solutions before system deployment. As a case study, we evaluate the survivability of the Saudi Arabia crude-oil pipeline network under two different attack scenarios. The stochastic analysis, carried out with Generalized Stochastic Petri nets, quantitatively estimates the minimisation of attack damages into the crude-oil network.

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Section: Introductionmentioning

confidence: 99%

Modelling Security of Critical Infrastructures: A Survivability Assessment

Rodríguez

Merseguer

Bernardi

2014

The Computer Journal

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“…Network survivability refers to the ability of the network system to complete the critical mission still timely when it suffers the attack, failures and accidents [8] . Survivability can be grouped into static survivability and dynamic survivability.…”

Section: Survivability Measurement Of Military Logistics Supply Networkmentioning

confidence: 99%

Research on Simulation and Survivability Measurement of Military Logistics Supply Network

Wang¹,

Yu²,

Liang³

et al. 2015

Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference

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Abstract. The military logistics supply network is generated through computer simulation firstly. Then on the basis of the military logistic network, the survivability index and measurement model of the military logistic network is established in topology layer and business layer, providing foundation and basis for the simulation experiment and survivability measurement of the actual military logistic network.

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“…Survivability [4][5][6][7][8] is commonly defined as the ability of a system to fulfill its mission, in a timely manner, in the presence of attacks, failures. In recent years for improving the ability of recovering from failure and attack a variety of survivable approaches are proposed.…”

Section: Introductionmentioning

confidence: 99%

A Framework to Evaluate the Ability to Provide Essential Services to End Users

Li¹,

Liu²,

Zhou³

et al. 2017

Advanced Science and Technology Letters

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Abstract. For a network system survivability refers to the ability to provide essential services to end users in the presence of failures and/or attacks. Survivability evaluation focuses on the measurement of this a-bility. As a dominating way of survivability evaluation, the model-based analysis technology for wireless sensor networks(WSNs) faces three prob-lems. First, the technology assumes that node distribution accords with some statistical regularity, and network has a fixed topology. However, in WSNs the node distribution and topology may change at any time which makes the evaluation result be untrustworthy. Second, survivabil-ity indicators are calculated by hand. But the personnel calculation is error prone. Third, there is no a systematic way to compute survivabil-ity indicators, i.e., we are not able to perform calculations of indicators by calling fundamental calculation processes. To solve the first problem, we propose a Continuous Time Markov Chain(CTMC) to characterize evolution of behaviors of single nodes under failures and attacks, and a parallel composition of CTMCs to characterize evolution of behaviors of WSN. The parallel composition can characterize links between nodes such that the calculation of survivability indicators does not depend on the node distribution regularity and topology. To solve the second prob-lem, we develop an algorithm to map the initial deployment of WSN to the parallel composition of CTMCs described by the modeling language of the stochastic model checker PRISM. To solve the third problem, we present how to formalize survivability indicators including k-connectivity etc. with Continuous Stochastic Logic(CSL). Finally, we show the effects of node misbehaviors on survivability by numerical analysis.

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Survivable network system analysis: a case study

Cited by 98 publications

References 4 publications

Modelling Security of Critical Infrastructures: A Survivability Assessment

Modelling Security of Critical Infrastructures: A Survivability Assessment

Research on Simulation and Survivability Measurement of Military Logistics Supply Network

A Framework to Evaluate the Ability to Provide Essential Services to End Users

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