Georgios Giannopoulos

ANALYSING CRITICAL INFRASTRUCTURE FAILURE WITH A RESILIENCE INOPERABILITY INPUT–OUTPUT MODEL

Over the past few years much effort has been made in modelling economic losses resulting from critical infrastructure failure. It has appeared that including resilience measures in the modelling approach, which may mute the losses considerably, is a challenging task. At the same time it is necessary because it prevents the modeller from generating overestimates. This study presents two directions to improve the modelling of (economic) resilience for which the state-of-the art with respect to dynamic inoperability input–output modelling is taken as a starting point. Firstly, the new model allows for a different recovery path than the traditionally assumed ‘concave up decreasing curve’ describes for a disrupted infrastructure or economic sector in the aftermath of a disaster. In this paper, we explain how the recovery path may depend on the type of disaster. Secondly, the model refines the aspect of ‘inventory’ as a resilience measure. Inventory is interpreted in a broad sense here: it can be any resilience measures which enable an infrastructure or economic sector to continue its supply despite being disrupted. The model is applied to both a simple two-sector illustrative example and a severe winter storm scenario in Europe using economic data from the World Input–Output Database to show its practical usefulness.

A fault diagnosis system for interdependent critical infrastructures based on HMMs

Abstract Modern society depends on the smooth functioning of critical infrastructures which provide services of fundamental importance, e.g. telecommunications and water supply. These infrastructures may suffer from faults/malfunctions coming e.g. from aging effects or they may even comprise targets of terrorist attacks. Prompt detection and accommodation of these situations is of paramount significance. This paper proposes a probabilistic modeling scheme for analyzing malicious events appearing in interdependent critical infrastructures. The proposed scheme is based on modeling the relationship between datastreams coming from two network nodes by means of a hidden Markov model (HMM) trained on the parameters of linear time-invariant dynamic systems which estimate the relationships existing among the specific nodes over consecutive time windows. Our study includes an energy network (IEEE 30 model bus) operated via a telecommunications infrastructure. The relationships among the elements of the network of infrastructures are represented by an HMM and the novel data is categorized according to its distance (computed in the probabilistic space) from the training ones. We considered two types of cyber-attacks (denial of service and integrity/replay) and report encouraging results in terms of false positive rate, false negative rate and detection delay.

Inoperability Input-Output Modeling: Inventory Optimization and Resilience Estimation during Critical Events

AbstractAssessing the ability of critical infrastructures to overcome shocks and optimizing their preparedness for critical events is a key factor in reducing damage, ensuring resilience, and mitigating monetary losses. When considering the problem in a broad sense, the assessment of technical dependencies among engineered systems can be supported by the analysis of economical relationships. A key tool to accomplish this objective is to exploit the input-output approach proposed by Wassily Leontief for the quantitative analysis of the relationships among different branches of an economy. Recently, the input-output approach was effectively exploited to assess the resilience of economies against critical events that may affect some sectors and ripple through neighboring economic segments as a function of their vulnerability, inertia, and centrality to the overall economy. Building on an existing approach, this paper considers a dynamic inoperability input-output model with inventories, examined according to...

Intelligent Energy Systems: Introducing Power–ICT Interdependency in Modeling and Control Design

This paper introduces a novel control scheme for mitigating the cascading effects of a failure in the power transmission grid. Modern power systems include controller entities, which exchange information (measurement/control signals) with the grid through information communication technology (ICT). Nevertheless, networking problems can generate observability/reachability issues, which prevent the controller from correctly estimating the grids state and applying in a timely manner the necessary actions. Thus, we present a control approach that explicitly takes into account the potential degradation of the ICT performance during power failure events. To this end, the power-induced ICT deficiencies are modeled as additive communication latency, to quantify their impact on the controllers effectiveness. Moreover, in order to overcome excessive/varying delays, a model predictive control architecture is proposed, which produces directives for both the safeguard cut of power lines and the load/generation regulation, in a manner that minimizes the impact of the initial power failure on the overall grid.

Faults and cyber attacks detection in critical infrastructures

In this paper we study the case of Critical Infrastructures (CIs), and especially power grid systems, which nowadays rely on computers and the Internet for their operation. We propose a combinatorial method for automatic detection and classification of faults and cyber-attacks, when there is limited data from the power grid nodes due to cyber implications. We design an experimental platform consisting of a power grid simulator and a cyber network emulator in order to demonstrate the efficiency of the proposed method.

Model predictive control of energy transmission grids accounting for power-ICT interdependencies

The operation of modern energy transmission grids results from the integration of power and ICT systems for monitoring and control purposes. The present paper performs an analysis of this interdependency, laying emphasis on the rise of anomalies in the data delivery process due to power perturbations, and studying how this consequent ICT malfunction can contribute back to the cascading of the initial power failure. Additionally, a delay-aware control scheme is introduced, so as to cope with the deficiencies of the communication network and facilitate the prompt application of suitable mitigation actions despite any distortion or unavailability of the required information.

EXPLOITING WEB ONTOLOGIES FOR AUTOMATED CRITICAL INFRASTRUCTURE DATA RETRIEVAL

Semantic web technologies play a significant role in many open data initiatives, including geo-mapping projects and platforms. At the same time, semantic principles are also promoted as a key enabling factor for multi-domain analyses of critical infrastructures as well as for improved emergency response. This chapter reviews the recent literature on ontology-based analysis and management of critical infrastructures, and proposes the use of ontology processing techniques to bridge the gap between infrastructure knowledge representation and available (often general-purpose) open data sources. In particular, it discusses an approach for matching a given critical infrastructure ontology to an ontology built on OpenStreetMap (OSM) tags that enables structured access to the associated geographical dataset.

A methodology for resilience optimisation of interdependent critical infrastructures

Many methodologies proposed in the literature for modelling and simulating the behaviour of critical infrastructures (CIs) in response to disruptive events are based on exhaustive analytic models, require extensive amounts of data and suffer from high computational burden. These factors restrain their applicability in practical policy making processes, which often require higher flexibility and efficiency in the delivery of analysis and prediction results. Taking these constraints into consideration, in this paper we propose a resilience-oriented framework for the high-level analysis of networked CIs, based on the concept of functional representation of interdependencies. We also show the usefulness of the proposed methodology to improve the operability of the network by appropriately tuning relevant parameters of selected assets according to an optimisation algorithm aiming at the improvement of the overall resilience of the CI network. This feature ultimately provides a guidance for selecting proper strategies to bound the propagation of damages across interdependent assets/infrastructures.

Controllability Assessment for Cascade Effects in ICT-enabled Power Grids

Despite the available monitor and control functionalities, the timely reconfiguration of the modern power grids is not always feasible, due to the inherent inertia of the power elements. Therefore, especially in the case of abrupt phenomena (e.g. loss of power lines), the power grids tend to suffer from severe cascade effects, since the triggering failure is usually followed by successive overloads. Additionally, the grid’s efficient reaction is further restrained by the performance of the underlying ICT system, i.e. delays in the reception of the monitor/control information. The present paper introduces a novel framework for assessing the controllability of ICT-enabled power grids. Assuming structural failures, the proposed methodology quantifies the system’s ability to be readjusted in a way that ceases the progressing damage and re-establishes operation within the grid’s physical constraints. The controllability is identified as a function of the overall system’s power and ICT characteristics, without considering any specific control strategy.

Automatic Fault Identification in Sensor Networks Based on Probabilistic Modeling

This work proposes a mechanism able to automatically categorize different types of faults occurring in critical infrastructures and especially water distribution networks. The mechanism models the relationship exhibited among the sensor datastreams based on the assumption that its pattern alters depending on the fault type. The first phase includes linear time invariant modeling which outputs a parameters vector. At the second phase the evolution of the parameter vectors is captured via hidden Markov modeling. The methodology is applied on data coming from the water distribution network of the city of Barcelona. The corpus contains a vast amount of data representative of nine network states. The nominal is included for enabling fault detection. The achieved classification rates are quite encouraging and the system is practical.