A
Aad van Moorsel
Researcher at Newcastle University
Publications - 142
Citations - 3344
Aad van Moorsel is an academic researcher from Newcastle University. The author has contributed to research in topics: Web service & Smart contract. The author has an hindex of 27, co-authored 138 publications receiving 2605 citations. Previous affiliations of Aad van Moorsel include Naresuan University & Imperial College London.
Papers
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Journal Article
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes: Preface
Ozalp Babaoglu,Márk Jelasity,Alberto Montresor,Christof Fetzer,Stefano Leonardi,Aad van Moorsel,Maarten van Steen +6 more
Book ChapterDOI
Stochastic Simulation Techniques for Inference and Sensitivity Analysis of Bayesian Attack Graphs
TL;DR: In this paper, an efficient sensitivity analysis approach that exploits a quantitative relation with stochastic inference is presented and demonstrate an approach to identify the most critical nodes for protection of the network and solve the uncertainty problem for the assignment of priors to nodes.
Proceedings ArticleDOI
Introduction to the Proceedings of the EDOC 2006 Workshop Middleware for Web Services (MWS) 2006
TL;DR: This workshop is a follow-up to the successful MWS 2005 workshop held at the EDOC 2005 conference and the subsequent special issue of the International Journal of Business Process Integration and Management (IJBPIM).
Proceedings ArticleDOI
Towards the implementation of an internet-based neighbourhood watch scheme-Impacts of inclusive technologies on societies
TL;DR: In this article, the authors discuss the current state of their work regarding the development and planned in-situ testing of a computer-based system to enhance community relations through the Neighbourhood Watch scheme.
Book ChapterDOI
Unsupervised Machine Learning for Card Payment Fraud Detection
TL;DR: The unsupervised approach learns the characteristics of normal transactions and then identifies anomalies as potential frauds and can reduce the equal error rate (EER) significantly over previous approaches, for a real-world transaction dataset.