activity
20142026
most citedIdentifying Products in Online Cybercrime Marketplaces: A Dataset for Fine-grained Domain Adaptation

14 citations · 40 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CR2026

Measuring and Evaluating the Performance of Generative AI Models for Scam Detection

Cem Topcuoglu, Seyed Ali Akhavani, Harel Berger +5

Online scams continue to cause substantial financial and personal harm. As a result, detection systems based on Large Language Models (LLMs) have been integrated into security prod…

cs.CR2018★ 2 cited

Towards Automatic Discovery of Cybercrime Supply Chains

Rasika Bhalerao, Maxwell Aliapoulios, Ilia Shumailov +2

Cybercrime forums enable modern criminal entrepreneurs to collaborate with other criminals into increasingly efficient and sophisticated criminal endeavors. Understanding the conne…

cs.NI2018★ 9 cited

A Bestiary of Blocking: The Motivations and Modes behind Website Unavailability

Sadia Afroz, Mobin Javed, Vern Paxson +3

This paper examines different reasons the websites may vary in their availability by location. Prior works on availability mostly focus on censorship by nation states. We look at t…

cs.NI2018★ 9 cited

Exploring Server-side Blocking of Regions

Sadia Afroz, Michael Carl Tschantz, Shaarif Sajid +3

One of the Internet's greatest strengths is the degree to which it facilitates access to any of its resources from users anywhere in the world. However, users in the developing wor…

cs.CL2017★ 14 cited

Identifying Products in Online Cybercrime Marketplaces: A Dataset for Fine-grained Domain Adaptation

Greg Durrett, Jonathan K. Kummerfeld, Taylor Berg-Kirkpatrick +5

One weakness of machine-learned NLP models is that they typically perform poorly on out-of-domain data. In this work, we study the task of identifying products being bought and sol…

cs.CR2015

Reviewer Integration and Performance Measurement for Malware Detection

Brad Miller, Alex Kantchelian, Michael Carl Tschantz +9

We present and evaluate a large-scale malware detection system integrating machine learning with expert reviewers, treating reviewers as a limited labeling resource. We demonstrate…