Diagnosis and Threat Detection Capabilities of the SERENITY Monitoring Framework

The SERENITY monitoring framework offers mechanisms for diagnosing the causes of violations of security and dependability (S&D) properties and detecting potential violations of such properties, called “threats”. Diagnostic information and threat detection are often necessary for deciding what an appropriate reaction to a violation is and taking pre-emptive actions against predicted violations, respectively. In this chapter, we describe the mechanisms of the SERENITY monitoring framework which generate diagnostic information for violations of S&D properties and detecting threats

Diagnosis of plan execution and the executing agent

Abstract. We adapt the Model-Based Diagnosis framework to perform (agentbased) plan diagnosis. In plan diagnosis, the system to be diagnosed is a plan, consisting of a partially ordered set of instances of actions, together with its executing agent. The execution of a plan can be monitored by making partial observations of the results of actions. Like in standard model-based diagnosis, observed deviations from the expected outcomes are explained qualifying some action instances that occur in the plan as behaving abnormally. Unlike in standard model-based diagnosis, however, in plan diagnosis we cannot assume that actions fail independently. We focus on two sources of dependencies between failures: dependencies that arise as a result of a malfunction of the executing agent, and dependencies that arise because of dependencies between action instances occurring in a plan. Therefore, we introduce causal rules that relate health states of the agent and health states of actions to abnormalities of other action instances. These rules enable us to introduce causal set and causal effect diagnoses that use the underlying causes of plan failing to explain deviations and to predict future anomalies in the execution of actions.

Using DESs for temporal diagnosis of multi-agent plan execution

Abstract. The most common reason for plan repair are the violation of a plan’s temporal constraints. Air Traffic Control is an example of an area in which violations of the plan’s temporal constraints is rather a rule than an exception. In such domains there is a need for identifying the underlying causes of the constraint violations in order to improve plan repairs and to anticipate future constraint violations. This paper presents a model for identifying the causes of the temporal constraint violations.

Diagnosability analysis of patterns on bounded labeled prioritized Petri nets

International audienceChecking the diagnosability of a discrete event system aims at determining whether a fault can always be identified with certainty after the observation of a bounded number of events. This paper investigates the problem of pattern diagnos-ability of systems modeled as bounded labeled prioritized Petri nets that extends the diagnosability problem on single fault events to more complex behaviors. An effective method to automatically analyze the diagnosability of a pattern is proposed. It relies on a specific Petri net product that turns the pattern diagnosability problem into a model-checking problem

Decentralized diagnosis in a spacecraft attitude determination and control system

International audienceIn model-based diagnosis (MBD), structural models can provide useful information for fault diagnosis and fault-tolerant control design. In particular, they are known for supporting the design of analytical redundancy relations (ARRs) which are widely used to generate residuals for diagnosis. On the other hand, systems are increasingly complex whereby it is necessary to develop decentralized architectures to perform the diagnosis task. Decentralized diagnosis is of interest for on-board systems as a way to reduce computational costs or for large geographically distributed systems that require to minimizing data transfer. Decentralized solutions allow proper separation of industrial knowledge, provided that inputs and outputs are clearly defined. This paper builds on the results of [1] and proposes an optimized approach for decentralized fault-focused residual generation. It also introduce the concept of Fault-Driven Minimal Structurally-Overdetermined set (FMSO) ensuring minimal redundancy. The method decreases communication cost involved in decentralization with respect to the algorithm proposed in [1] while still maintaining the same isolation properties as the centralized approach as well as the isolation on request capability. 1. Introduction With increasing complexity of industrial processes, the requirement for reliability, availability and security is growing significantly. Fault detection and isolation (FDI) are becoming a major issue in industry. The structural approach constitutes a general framework to provide information when the system becomes complex. The main aim of the structural approach application is to identify the subsets of equations which include redundancy. The system structure analysis, originally developed for the decomposition of large systems of equations for their hierarchical resolution, was adopted by the Fault Detection and Isolation (FDI) community [2, 3]. Structural concepts are used for analysis of system monitor ability using the concept of complete matching on a graph. Decentralized diagnosis has received considerable attention to deal with distributed systems or with systems that may be too large to be diagnosed by one centralized site. In the same way, the decentralized solution allows proper separation of industrial knowledge, provided that inputs and outputs are clearly defined

Diagnosis of higher-order discrete-event systems

Preventing major events, like the India blackout in 2012 or the Fukushima nuclear disaster in 2011, is vital for the safety of society. Automated diagnosis may play an important role in this prevention. However, a gap still exists between the complexity of systems such these and the effectiveness of state-of-the-art diagnosis techniques. The contribution of this paper is twofold: the definition of a novel class of discrete-event systems (DESs), called higher-order DESs (HDESs), and the formalization of a relevant diagnosis technique. HDESs are structured hierarchically in several cohabiting subsystems, accommodated at different abstraction levels, each one living its own life, as happens in living beings. The communication between subsystems at different levels relies on complex events, occurring when specific patterns of transitions are matched. Diagnosis of HDESs is scalable, context-sensitive, and in a way intelligent