CIPRNet Bibliography of CIP literature

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2017

Giovinazzi, Sonia, Di Pietro, Antonio, Mei, Matteo, Pollino, Maurizio and Rosato, Vittorio
ANIDIS 2017 - XVII Convegno , page 9.
Pistoia, Italy
September 2017

Keywords: Seismic effect of uncertainty on objectives
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risk
and intrinsic properties of something resulting in susceptibility to a risk source that can lead to an event with a consequence
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vulnerability
, Critical infrastructures, Determistic scenarios, Decision Support Systems Decision Support System
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DSS
, Physical damage and Functional impacts

Abstract: This paper presents a novel decision support system for the assessment and mitigation of seismic effect of uncertainty on objectives
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risk
for Critical Infrastructures (CI). The tool, named CIPCast-ES, was developed by ENEA in the framework of the EU-funded CIPRNet project. CIPCast-ES provides a user-friendly geographical interface for querying and analysing data, producing and evaluating scenarios, through the deterministic assessment of the expected physical damage to See critical infrastructure
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CI
components and the induced impacts for both real and simulated seismic events. A novel contribution introduced in CIPCast-ES, compared to similar See computer simulation.
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simulation
platforms, is the integration, in a unique framework, of: (i) real-time acquisition of earthquake events (source: INGV); (ii) seismic fragility assessment of the different components for the different See critical infrastructure
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CI
systems considered; (iii) estimation of damage and impact scenarios; (iv) estimation of restoration times for See critical infrastructure
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CI
services and informed support for an optimal allocation of resources. The paper presents the data See conceptual model
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model
that was specifically designed to account for topological and system performance properties and the adopted metrics used to assess See critical infrastructure
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CI
seismic intrinsic properties of something resulting in susceptibility to a risk source that can lead to an event with a consequence
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vulnerability
, damage and service impacts. An implementation of CIPCast-ES related to a seismic An event describes what happens to a component in the CI model if a condition is fulfilled, e.g. the tripping of a transmission line at a certain time.
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event
in Central Italy affecting the inhabited area of Rome and its Distribution Electric Power Network is presented and discussed in the paper

2016

Stergiopoulos, George, Kotzanikolaou, Panayiotis, Theocharidou, Marianthi, Lykou, Georgia and Gritzalis, Dimitris
International Journal of The European Council Directive 2008/114/EC defines: ‘Critical infrastructure’ (CI) means an asset, system or part thereof located in Member States which is essential for the maintenance of vital societal functions, health, safety, security, economic..
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Critical Infrastructure
Protection, 12:46 - 60
2016
ISSN: 1874-5482

Keywords: Critical infrastructures; Cascading failures; Dependency is the relationship between two (critical infrastructure) products or services in which one product or service is required for the generation of the other product or service.
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Dependency
effect of uncertainty on objectives
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risk
graphs; Time analysis; Resilience; Fuzzy logic

Abstract: Abstract Dependency is the relationship between two (critical infrastructure) products or services in which one product or service is required for the generation of the other product or service.
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Dependency
analysis of critical infrastructures is a computationally intensive problem when dealing with large-scale, cross-sectoral, cascading and common-cause failures. The problem intensifies when attempting a dynamic, time-based Dependency is the relationship between two (critical infrastructure) products or services in which one product or service is required for the generation of the other product or service.
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dependency
analysis. This paper extends a previous graph-based effect of uncertainty on objectives
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risk
analysis methodology to dynamically assess the evolution of cascading failures over time. Various growth models are employed to capture slow, linear and rapidly evolving effects, but instead of using static projections, the evolution of each Dependency is the relationship between two (critical infrastructure) products or services in which one product or service is required for the generation of the other product or service.
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dependency
is "objectified" by a fuzzy system that also considers the effects of nearby dependencies. To achieve this, the impact (and, eventually, risk) of each Dependency is the relationship between two (critical infrastructure) products or services in which one product or service is required for the generation of the other product or service.
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dependency
is quantified on the time axis into a form of many-valued logic. In addition, the methodology is extended to analyze major failures triggered by concurrent common-cause cascading events. A The European Council Directive 2008/114/EC defines: ‘Critical infrastructure’ (CI) means an asset, system or part thereof located in Member States which is essential for the maintenance of vital societal functions, health, safety, security, economic..
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critical infrastructure
Dependency is the relationship between two (critical infrastructure) products or services in which one product or service is required for the generation of the other product or service.
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dependency
analysis tool, CIDA, that implements the extended effect of uncertainty on objectives
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risk
-based methodology is described. CIDA is designed to assist decision makers in proactively analyzing dynamic and complex Dependency is the relationship between two (critical infrastructure) products or services in which one product or service is required for the generation of the other product or service.
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dependency
effect of uncertainty on objectives
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risk
paths in two ways: (i) identifying potentially underestimated low effect of uncertainty on objectives
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risk
dependencies and reclassifying them to a higher effect of uncertainty on objectives
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risk
category before they are realized; and (ii) simulating the effectiveness of alternative mitigation controls with different reaction times. Thus, the CIDA tool can be used to evaluate alternative defense strategies for complex, large-scale and multi-sectoral Dependency is the relationship between two (critical infrastructure) products or services in which one product or service is required for the generation of the other product or service.
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dependency
scenarios and to assess their resilience in a cost-effective manner.

2015

Luiijf, E.
Cyber (In-)security of Industrial measure that is modifying risk
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Control
Systems: A Societal Challenge
In F. Koornneef and C. van Gulijk, editor, Computer Safety, Reliability, and Security: 34th International Conference, SAFECOMP2015, Delft, The Netherlands, September 23-25, 2015 Proceedings of LNCS , page 7-15.
EWICS TC7
Publisher: Springer,
September 2015
Luiijf, E.
Building Public Private Cooperation in Cyber Security
The See critical infrastructure protection
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CIP
Report, 14(8):11-12
May 2015
Luiijf, E., Theocharidou, M. and Rome, E.
The See critical infrastructure protection
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CIP
Report, 14(8):3-5
May 2015
Luiijf, Eric and Kernkamp, Allard
Sharing Cyber Security Information
Publisher: TNO, The Hague, The Netherlands
March 2015

Keywords: Information Sharing,cyber security

Luiijf, Eric and Jan te Paske, Bert
The Hague, The Netherlands
March 2015

Keywords: SCADA, ICS, security

Kozik, Rafal, Choras, Michal, Flizikowski, Adam, Theocharidou, Marianthi, Rosato, Vittorio and Rome, Erich
Advanced services for critical infrastructures protection
Journal of Ambient Intelligence and Humanized Computing, :1-13
2015
ISSN: 1868-5137

Keywords: The European Council Directive 2008/114/EC defines: ‘Critical infrastructure’ (CI) means an asset, system or part thereof located in Member States which is essential for the maintenance of vital societal functions, health, safety, security, economic..
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Critical infrastructure
protection; CIPRNet project; Decision support; Services; Modelling denotes the action of designing and creating a conceptual model or a computational model or a CI model or a computer simulation. Other synonyms may apply as well.
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Modelling
and See computer simulation.
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simulation

Luiijf, Eric and Klaver, Marieke
Symposium on Critical Infrastructures: effect of uncertainty on objectives
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Risk
, Responsibility and Liability - Governing Critical Information and Communication Technologies
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ICT
: Elements that Require Attention
European Journal of effect of uncertainty on objectives
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Risk
Regulation, 6(2):263 - 270
2015
ISBN: 1867-299X

Abstract: With respect to critical information and communication technologies (ICT), nations most often declare their national The European Council Directive 2008/114/EC defines: ‘Critical infrastructure’ (CI) means an asset, system or part thereof located in Member States which is essential for the maintenance of vital societal functions, health, safety, security, economic..
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critical infrastructure
to include telecommunication services and in some cases critical services offered by key Internet Service Providers (ISP). This paper debates whether nations, their policy-makers, legislation and regulation largely overlook and fail to properly govern the full set of Information and Communication Technologies
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ICT
elements and services critical to the functioning of their nation. The related societal and economical effect of uncertainty on objectives
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risk
, however, needs to be closely mitigated, managed and governed. Legal and regulatory obligations to increase the Information and Communication Technologies
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ICT
resilience may sometimes encourage this process.

Krishna, V.B., Iyer, R.K. and Sanders, W.H.
ARIMA-Based Modeling and Validation of Consumption Readings in Power Grids
10th Int. Conf. on Critical Information Infrastructure Security (CRITIS 2015),
2015

Keywords: smart, meter, anomaly, attack, detection, auto, regressive, moving, average, integrated, electricity, theft, cyber-physical, ARIMA, ARMA, forecasting, critical, infrastructure, security, measurements

Abstract: Smart meters are increasingly being deployed to measure electricity consumption of residential as well as non-residential consumers. The readings reported by these meters form a time series, which is stored at electric utility servers for billing purposes. Invalid readings may be reported because of malicious compromise of the smart meters themselves, or of the network infrastructure that supports their communications. Although many of these meters come equipped with encrypted communications, they may potentially be vulnerable to cyber intrusions. Therefore, there is a need for an additional layer of validation to detect these intrusion attempts. In this paper, we make three contributions. First, we show that the ARMA See conceptual model
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model
proposed in the anomaly detection literature is unsuitable for electricity consumption as most consumers exhibit non-stationary consumption behavior. We use automated See conceptual model
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model
fitting methods from the literature to show that first-order differencing of these non-stationary readings makes them weakly stationary. Thus, we propose the use of ARIMA forecasting methods for validating consumption readings. Second, we evaluate the effectiveness of ARIMA forecasting in the context of a specific attack See conceptual model
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model
, where smart meter readings are modifed to steal electricity. Third, we propose additional checks on mean and variance that can mitigate the total amount of electricity that can be stolen by an attacker by 77:46%. Our evaluation is based on a real, open dataset of readings obtained from 450 consumer meters.

Note: Winning artcle od the 2015 CIPRNet Young CRITIS Award (CYCA)

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