Joe Hewett

dblp:340/0229 · DBLP profile ↗
← Back
2ranked-venue papers
2as first author
2since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 2 · 2 first-author · 2 since 2021Software engineering, systems software and programming languages · 2 · 2 first-author · 2 since 2021
YearPublicationVenuePosition
2022 Towards a Dependable Energy Market: Proof of Authority in a Blockchain-based Peer-to-Peer Microgrid
abstract
For reasons of energy security, affordability and environment, the foundations of our energy markets are in flux. A promising approach for addressing some of the challenges faced comes in the form of blockchain-based peer-to-peer (P2P) microgrids, which we argue are a potential solution to many of the pitfalls of existing grid architectures. More specifically, this paper analyses consensus mechanisms that could be employed in blockchain-based microgrids, demonstrating that proof of authority (PoA) is a promising direction when seeking to improve the dependability of the energy market. We go on to specify a viable architecture for a PoA-based microgrid and provide experimental results to demonstrate that PoA is superior to proof of work (PoW) in this context.
Joe Hewett, Mark Etman, Robbie Marseglia, Tomas Mella Pickersgill, Matthew Leeke
PRDC1
2022 Developing a GPT-3-Based Automated Victim for Advance Fee Fraud Disruption
abstract
Advance Fee Fraud (AFF) is amongst the most prevalent and destructive forms of cybercrime. Scammers typically commit AFF by tricking victims into making upfront payments for goods or services that are never provided. These payments are small compared to the alleged gains, and can thus be attractive for victims, particularly if they are vulnerable or in a heightened emotional state. Given that approximately three billion fraudulent emails are sent every day, the scale and impact of AFF demands innovative approaches. In this paper we document the development of an automated victim for AFF. The system leverages GPT-3, a large language model, in conjunction with deliberately engineered prompts to generate plausible responses to AFF emails, allowing fraud to be disrupted and actionable information relating to perpetrators to be obtained.
Joe Hewett, Matthew Leeke
PRDC1