Claudio Balducelli

Safeguarding information intensive critical infrastructures against novel types of emerging failures

Abstract The complexity of information intensive critical infrastructures, like electricity networks, telecommunication networks and public transportation networks is today augmented much more than in the past: such complexity augments the number of possible failures and anomalous working conditions and consequently decreases the survivability of the infrastructures. In this paper, the possibility is investigated to detect early anomalies and failures inside information intensive critical infrastructures by the introduction of anomaly detectors being “self-aware” about the normal working conditions of the infrastructure itself. This approach has the objective to improve the performance of the most popular signature-based algorithms for intrusion detection, and makes use of different classes of time-oriented algorithms based on artificial intelligence paradigm. It has the advantage to work also in presence of unknown and unexpected types of attacks or failures. The tests, to evaluate the performance of the utilised detectors, are executed inside an emulated supervisory control and data acquisition (SCADA) system of an electrical power transmission grid, and a proposal for the future integration inside real SCADA systems is also reported.

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.

Electric Power System Anomaly Detection Using Neural Networks

The aim of this work is to propose an approach to monitor and protect Electric Power System by learning normal system behaviour at substations level, and raising an alarm signal when an abnormal status is detected; the problem is addressed by the use of autoassociative neural networks, reading substation measures. Experimental results show that, through the proposed approach, neural networks can be used to learn parameters underlaying system behaviour, and their output processed to detecting anomalies due to hijacking of measures, changes in the power network topology (i.e. transmission lines breaking) and unexpected power demand trend.

Dependability and Survivability of Large Complex Critical Infrastructures

This paper refers to research activities related to SAFEGUARD project (IST Project Number: IST-2001-32685). The aims of the project is to examine LCCI’s in terms of nature of different facets in each infrastructure: organizational, computational (cyber) and physical layers. Critical inter-dependencies among layers can thus be analyzed. Possible impact of bad events, early classified in attack scenarios with and without SAFEGUARD, will be coped with countermeasures to maintain at acceptable level system’s operability. SAFEGUARD, an agent-based middleware, is conceived to operate embedded inside of the cyber-layers, the more sensitive part to malicious attacks and anomalies, and is designed to enhance dependability and survivability of a LCCI. Self-healing mechanism of SAFEGUARD agents will start with the trouble diagnosis and classification using Hybrid Intrusion Detection techniques (software instrumentation, novelty detection, etc.). Once the problem has been diagnosed, a number of techniques will be used to solve and repair the fault (i.e.: adaptive middleware technology, backup, hot standby and so on). More self-healing mechanisms will have to be combined and coordinated to with an attempt to deal with the source of the problem.

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.

Developing an Expert System for Fault Diagnosis of a Turbo Generator Group Using an OPS5 Production Rules Programming Environment

The Turbogenerator is one of the most complex and critical subsystems of a thermal production plant; it is composed of 3–5 coaxial stages (low, medium, and high pressure turbines, and generator), each one supported by a couple of oil film bearings. Almost all known turbogenerator malfunctions result in an alteration of the vibrational state of the machine during operation. Thus turbogenerators are factory equipped by vibration sensors, triggering alarms when vibration amplitude goes beyond a prefixed threshold.