activity
19972022
most citedWhy Is Diagnosis Using Belief Networks Insensitive to Imprecision In Probabilities?

62 citations · 161 across the 16 of their papers we have counts for

collaborators

16 papers

eess.SY2022

Quantifying impact on safety from cyber-attacks on cyber-physical systems

Eleftherios Vlahakis, Gregory Provan, Gordon Werner +2

We propose a novel framework for modelling attack scenarios in cyber-physical control systems: we represent a cyber-physical system as a constrained switching system, where a singl…

cs.CR2021★ 1 cited

Grounds for Suspicion: Physics-based Early Warnings for Stealthy Attacks on Industrial Control Systems

Mazen Azzam, Liliana Pasquale, Gregory Provan +1

Stealthy attacks on Industrial Control Systems can cause significant damage while evading detection. In this paper, instead of focusing on the detection of stealthy attacks, we aim…

cs.CR2021

Efficient Predictive Monitoring of Linear Time-Invariant Systems Under Stealthy Attacks

Mazen Azzam, Liliana Pasquale, Gregory Provan +1

Attacks on Industrial Control Systems (ICS) can lead to significant physical damage. While offline safety and security assessments can provide insight into vulnerable system compon…

cs.AI2014

Query DAGs: A Practical Paradigm for Implementing Belief Network Inference

Adnan Darwiche, Gregory M. Provan

We describe a new paradigm for implementing inference in belief networks, which relies on compiling a belief network into an arithmetic expression called a Query DAG (Q-DAG). Each…

cs.AI2014★ 30 cited

A Model-Based Active Testing Approach to Sequential Diagnosis

Alexander Feldman, Gregory Provan, Arjan van Gemund

Model-based diagnostic reasoning often leads to a large number of diagnostic hypotheses. The set of diagnoses can be reduced by taking into account extra observations (passive moni…

cs.AI2014★ 27 cited

Approximate Model-Based Diagnosis Using Greedy Stochastic Search

Alexander Feldman, Gregory Provan, Arjan van Gemund

We propose a StochAstic Fault diagnosis AlgoRIthm, called SAFARI, which trades off guarantees of computing minimal diagnoses for computational efficiency. We empirically demonstrat…