Antonio Di Pietro

Analysing interdependencies of critical infrastructures using agent discrete event simulation

The paper explores the possibility of using interacting agents for modelling and discrete event simulation as a tool to approach interdependencies analysis and evaluation for critical infrastructures. A discrete event simulation system was developed, using agent-oriented programming, considering the following limited sets of critical infrastructures: a great hospital infrastructure, a railway transportation infrastructure and other public transportation infrastructures. Faults inside the electricity distribution system are simulated, producing electrical power outages whose duration could be variable with respect to time and space, and generating consequences inside the transportation infrastructures. The hospital infrastructure users, such as different types of physicians, nurses, subsidiary personnel, students and patients are also modelled using agent oriented architectures. The objective of the simulations is to study and analyse the interdependencies of the considered infrastructures Many typologies of test scenarios are also executed and the severity of the generated consequences are analysed in the paper.

A Platform for Disaster Response Planning with Interdependency Simulation Functionality

Catastrophic events can result in great loss of lives and property. Planning an effective disaster response to minimize associated losses is a fundamental challenge for decision makers. The planning process can be improved by simulating interdependent critical infrastructures and evaluating system behavior during disaster scenarios. This paper describes a disaster response planning simulation platform that supports decision making based on the interdependencies existing between a power grid and a supervisory control and data acquisition (SCADA) system. By considering the physical constraints on the power grid and SCADA network, a set of feasible configurations is presented to disaster responders. The utility of the platform is demonstrated using an example scenario involving power distribution to a hospital during a disaster event.

Assessing the Impact of Cyber Attacks on Wireless Sensor Nodes That Monitor Interdependent Physical Systems

This paper describes a next-generation security information and event management (SIEM) platform that performs real-time impact assessment of cyber attacks that target monitoring and control systems in interdependent critical infrastructures. To assess the effects of cyber attacks on the services provided by critical infrastructures, the platform combines security analysis with simulations produced by the Infrastructure Interdependencies Simulator (i2Sim). The approach is based on the mixed holistic reductionist (MHR) methodology that models the relationships between functional components of critical infrastructures and the provided services. The effectiveness of the approach is demonstrated using a scenario involving a dam that feeds a hydroelectric power plant. The scenario considers an attack on a legacy SCADA system and wireless sensor network that reduces electricity production and degrades the services provided by the interdependent systems. The results demonstrate that the attack is detected in a timely manner, risk assessment is performed effectively and service level variations can be predicted. The paper also shows how the impact of attacks on services can be estimated when limits are imposed on information sharing.

Assessing the Impact of Cyber Attacks on Interdependent Physical Systems

Considerable research has focused on securing SCADA systems and the physical processes they control, but an effective framework for the real-time impact assessment of cyber attacks on SCADA systems is not yet available. This paper attempts to address the problem by proposing an innovative framework based on the mixed holistic reductionist methodology. The framework supports real-time impact assessments that take into account the interdependencies existing between critical infrastructures that are supervised and controlled by SCADA systems. Holistic and reductionist approaches are complementary approaches that support situation assessment and evaluations of the risk and consequences arising from infrastructure interdependencies. The application of the framework to a sample scenario on a realistic testbed demonstrates the effectiveness of the framework for risk and impact assessments.

A Middleware Improved Technology (MIT) to Mitigate Interdependencies between Critical Infrastructures

An angle iron forms two sides of a right isosceles triangular housing. These sides are each apertured and the apertures are provided with metal-framed lens plates which bolt onto the housing sides. Preferably, in one instance the lens material is mounted in the plate for ease of access to the interior of the housing and in the door instance the lens is mounted in the aperture and retained by the frame plate. A fluorescent lamp tube assembly is received inside the housing for providing illumination through both lenses. Access thereto is via removal of a metal-framed lens plate. The housing has mounting elements which permit a user to flush mount the housed light source, e.g. on a mining machine.

Design of DSS for Supporting Preparedness to and Management of Anomalous Situations in Complex Scenarios

Decision Support Systems (DSS) are complex technological tools, which enable an accurate and complete scenario awareness, by integrating data from both “external” (physical) situation and current behaviour and state of functioning of the technological systems. The aim is to produce a scenario analysis and to guess identify educated the most efficient strategies to cope with possible crises. In the domain of Critical Infrastructures (CI) Protection, DSS can be used to support strategy elaboration from CI operators, to improve emergency managers capabilities, to improve quality and efficiency of preparedness actions. For these reasons, the EU project CIPRNet, among others, has realised a new DSS designed to help operators to deal with the complex task of managing multi-sectorial CI crises, due to natural events, where many different CI might be involved, either directly or via cascading effects produced by (inter-)dependency mechanisms. This DSS, called CIPCast, is able to produce a real-time operational risk forecast of CI in a given area; other than usable in a real-time mode, CIPCast could also be used as scenario builder, by using event simulators enabling the simulation of synthetic events whose impacts on CI could be emulated. A major improvement of CIPCast is its capability of measuring societal consequences related to the unavailability of primary services such as those delivered by CI.

Resources allocation in disaster response using Ordinal Optimization based approach

Recent events, such as Hurricane Katrina, have revealed the need for coordinated and effective disaster responses. An optimal distribution of available resources is essential for disaster response effectiveness. Emergency responders are faced with the challenges of increased size and complexity of critical infrastructures that provide vital resources for disaster response operations. In this paper, we propose a simulation-based tool to assist emergency responders in finding the optimal distribution of available resources during a disaster event. The proposed tool utilizes the Disaster Response Network Enabled Platform (DR-NEP) which is an infrastructure interdependencies simulation platform for disaster response support. DR-NEP is a simulation network platform that integrates different simulators for different infrastructures to form a universal simulation platform. We employ a new concept in Discrete Event Systems optimization called Ordinal Optimization to address the problem of resources allocation during a disaster event. The objective of the optimization problem is maximizing the operational capacity of a critical infrastructure, a hospital in this case. Due to the huge combinatorial feasible search space, an Ordinal Optimization based approach is used to solve the problem using two main concepts: goal softening and order comparison. This approach aims at finding a Good Enough solution set (G) with an acceptable probability and efficient computational effort. This paper describes early results of our work that shows the use of our approach in optimizing resources allocation in a simulated disaster event.

A Decision Support System for Emergency Management of Critical Infrastructures Subjected to Natural Hazards

Natural hazards might damage elements of Critical Infrastructures and produce perturbations on the delivered services. In addition, (inter)dependency phenomena interconnecting infrastructures, may amplify impacts through cascading effects. In this paper, we present a Decision Support System (DSS) aiming at predicting the possible effects of natural hazards on the services provided by critical infrastructures. The system employs modeling and simulation techniques to forecast the effects of natural hazards on critical infrastructures services.

Inter-System Software Adapter for Decision Support by Interfacing Disaster Response Platforms a Simulation Platforms

An inter-system adapter design is described which links the web service based DR NEP and WebSimP simulation platforms. The adapter enables the PSS Sincal electrical simulator to share results with the DR NEP which coordinates the data exchange among interdependent simulators. In this paper, we describe an adapter which supports decision makers involved in natural crisis management by calculating constraints on electrical loads based on the state of the electrical grid. By eliminating non-feasible electrical distribution options, the adapter reduces the deliberation time and improves the decision outcome. This inter-system adapter can be generalized to other infrastructure systems.

Towards a decision support tool for assessing, managing and mitigating seismic risk of electric power networks

Recent seismic event worldwide proved how fragile the electric power system can be to seismic events. Decision Support Systems (DSSs) could have a critical role in assessing the seismic risk of electric power networks and in enabling asset managers to test the effectiveness of alternative mitigation strategies and investments on resilience. This paper exemplifies the potentialities of CIPCast, a DSS recently created in the framework of the EU-funded project CIPRNet, to perform such tasks. CIPCast enables to perform risk assessment for Critical Infrastructures (CI) when subjected to different natural hazards, including earthquakes. An ad-hoc customization of CIPCast for the seismic risk analysis and management of electric power networks is featured in this paper. The international literature describes effective and sound efforts towards the creation of software platforms and frameworks for the assessment of seismic risk of electric power networks. None of them, unfortunately, achieved the goal of creating a user-friendly and ready available DDS to be used by asset managers, local authorities and civil protection departments. Towards that and building on the international literature, the paper describes metrics and methods to be integrated within CIPCast for assessing the earthquake-induced physical and functional impacts of the electric power network at component and system level. The paper describes also how CIPCast can inform the service restoration process.