A class of count-and threshold mechanisms, collectively dubbed α-count, able to discriminate between transient faults and intermittent faults in computing systems is presented. Transient faults discrimination has long been pursued in commercial systems: threshold-based techniques have been practiced for several years for this purpose. The present work aims to contribute to the usefulness of count-andthreshold schemes, through analysis of the behaviour and exploration of the effects on the system. A mathematically defined structure simple enough to be analysed by means of standard tools is adopted. α-count is equipped with internal parameters, designed to be tuned to suit environmental variables (such as transient fault rate, intermittent fault occurrence patterns). Extensive behaviour analysis for two embodiments of the scheme, both under the usual assumption of exponentially distributed fault rates and with more realistic fault patterns is carried out.

In this paper the consolidate identification of faults, distinguished as transient or permanerdintermittent, is approached. Transient faults discrimination has long been performed in commercial systems: threshold-based techniques have been practiced for several years for this purpose. The present work aims to contribute to the usefulness of the count-and-threshold scheme, through the analysis of its behaviour and the exploration of its effects on the system. To this goal, the scheme is mechanized as a device named acount, endowed with a few controllable parameters. a-count tries to balance between two conflicting requirements: to keep in the system those components that have experienced just transient faults; and to remove quickly those affected by permanent or intermittent faults. Analytical models are derived, allowing detailed study of a-count's behaviour; the actual evaluation, in a range of configurations, is performed by standard tools, in terms of the delay in spotting faulty components and the probability of improperly blaming correct ones.

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Electrical power system ICT threatsCritical infrastructure dependencies A B S T R A C TIn this paper we present an approach to model and quantify (inter)dependencies between the Electrical Infrastructure (EI) and the Information Infrastructure (II) that implements the EI control and monitoring system. The quantification is achieved through the integration of two models: one that concentrates more on the structure of the power grid and its physical quantities and one that concentrates on the behavior of the control system supported by the II. The modeling approach is exemplified on a scenario whose goal is to study the effects of an II partial failure (a denial of service attack that compromises the communication network) on the remote control of the EI. The approach has been initially developed as part of the European project CRUTIAL.

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In modern information infrastructures, diagnosis must be able to assess the status or the extent of the damage of individual components. Traditional one-shot diagnosis is not adequate, but streams of data on component behavior need to be collected and filtered over time as done by some existing heuristics. This paper proposes instead a general framework and a formalism to model such over-time diagnosis scenarios, and to find appropriate solutions. As such, it is very beneficial to system designers to support design choices. Taking advantage of the characteristics of the hidden Markov models formalism, widely used in pattern recognition, the paper proposes a formalization of the diagnosis process, addressing the complete chain constituted by monitored component, deviation detection and state diagnosis. Hidden Markov models are well suited to represent problems where the internal state of a certain entity is not known and can only be inferred from external observations of what this entity emits. Such over-time diagnosis is a first class representative of this category of problems. The accuracy of diagnosis carried out through the proposed formalization is then discussed, as well as how to concretely use it to perform state diagnosis and allow direct comparison of alternative solutions.

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