Detecting Anomalies (and Fraud) in Networks
Detecting Anomalies (and Fraud) in Networks:
TARC Workshop: 24th June 2020 13.00-17.30 BST
Network Analysis, Machine Learning and Statistics: How can Policy-Institutes utilize networks to achieve aims such as fraud detection?
There is an explosion of data obtained from systems that can be conceptualized as networks. The collection and analysis of network data plays a key role in a wide range of scientific fields. Examples include, but are not limited to, biology, computer science, sociology and economics.
The analysis of the observed networks requires the ability to examine big data by utilizing advanced data analytical methods.
- How can recently developed Machine Learning and Statistical techniques assist organisations (such as Revenue Authorities) to enhance their understanding of the observed networks?
- Can advanced data analytics improve the detection of anomalies (fraud) in large and complex networks such as Values added Tax (VAT) and Cyber-Security systems?
- Which are the Mathematical and Statistical challenges?
- Which are the most relative tools from the emerging literature that can be used?
These are some of the questions the TARC workshop aimed to provide answers to. Click on the links to download the presentations.
13:00-13:30 Niall Adams, Imperial College London, UK
Anomaly Detection in Enterprise Cyber-Security
13:40 - 14:10 Christos Kotsogiannis and Petros Dellaportas, University of Exeter / University College London, UK
Detecting network anomalies in the Value Added Taxes (VAT) system
14:20-14:50 Michalis Vazirgiannis, LIX Ecole Polytechnique, France
Graph Mining for Fraud detection
15:30-16:00 Simone Gabbriellini, University of Trento, Italy
Network effects on tax compliance
16:10-16:40 Theodoros Rapanos, Södertörn University, Sweden
Imperfect information, social norms, and beliefs in networks
16:50-17:20 Sofia Olhede, The École polytechnique fédérale de Lausanne (EPFL), Switzerland
Modeling Networks and Network Populations
17.30 Workshop close