My research touches on a number of areas, and I admit to being something of a dabbler -- I know a little bit about a few areas, and probably not enough about any one in particular. My primary areas of interest tend to be applications in online privacy, cybersecurity and understanding cybercrime. Recently I have been paying most attention to online fraud and social engineering.
My aim is to approach these security subjects from a data-driven perspective. Typically this involves the use of machine-learning tools, and in particular linguistic data is often a focus of a lot of my effort.
Publication | Author | Venue | Year | BIB | |
---|---|---|---|---|---|
AMoC: A multifaceted machine learning-based toolkit for analysing cybercriminal communities on the darknet | 3 | BDACCIP | 2021 | ↓ | ↓ |
The impact of adverse events in darknet markets: An anomaly detection approach | 3 | WACCO | 2021 | ↓ | ↓ |
Resource networks of pet scam websites | 2 | eCrime | 2020 | ↓ | ↓ |
The best laid plans or lack thereof: Security decision-making of different stakeholder groups | 3 | TSE | 2020 | ↓ | ↓ |
"So if Mr Blue Head here clicks the link..." Risk thinking in cyber security decision making | 3 | TOPS | 2020 | ↓ | ↓ |
Pets without PETs: on pet owners' under-estimation of privacy concerns in pet wearables | 2 | PETS | 2020 | ↓ | ↓ |
Identifying unintended harms of cybersecurity countermeasures | 3 | eCrime | 2019 | ↓ | ↓ |
Data, data, everywhere: quantifying software developers’ privacy attitudes | 2 | STAST | 2019 | ↓ | ↓ |
Automatically dismantling online dating fraud | 2 | TIFS | 2019 | ↓ | ↓ |
The geography of online dating fraud | 1 | ConPro | 2018 | ↓ | ↓ |
Data quality measures for identity resolution | 1 | - | 2018 | ↓ | ↓ |
Data exfiltration: a review of external attack vectors and countermeasures | 2 | JNCA | 2018 | ↓ | ↓ |
Scamming the scammers: towards automatic detection of persuasion in advance fee frauds | 1 | CyberSafety | 2017 | ↓ | ↓ |
Ethical and social challenges with developing automated methods to detect and warn potential victims of mass-marketing fraud | 2 | CyberSafety | 2017 | ↓ | ↓ |
Panning for gold: automatically analysing online social engineering attack surfaces | 1 | Computers & Security | 2017 | ↓ | ↓ |
Sampling labelled profile data for identity resolution | 1 | BigData | 2016 | ↓ | ↓ |
Inferring semantic mapping between policies and code: the clue is in the language | 2 | ESSoS | 2016 | ↓ | ↓ |
Discovering unknown known security requirements | 4 | ICSE | 2016 | ↓ | ↓ |
Sonar phishing: pinpointing highly vulnerable victims for social engineering attacks | 2 | S&P Posters | 2015 | ↓ | ↓ |
A systematic survey of online data mining technology intended for law enforcement | 1 | CSUR | 2015 | ↓ | ↓ |
A service-independent model for linking online profile information | 1 | JISIC | 2014 | ↓ | ↓ |
Collaborative filtering as an investigative tool for peer-to-peer filesharing networks | 1 | ASE Science Journal | 2012 | ↓ | ↓ |