Greg Durrett, Jonathan K. Kummerfeld, Taylor Berg-Kirkpatrick, Rebecca Portnoff, Sadia Afroz, Damon McCoy, Kirill Levchenko, Vern Paxson. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017.

Sites for online classified ads selling sex are widely used by human traffickers to support their pernicious business. The sheer quantity of ads makes manual exploration and analysis unscalable. In addition, discerning whether an ad is advertising a trafficked victim or an independent sex worker is a very difficult task. Very little concrete ground truth (i.e., ads definitively known to be posted by a trafficker) exists in this space. In this work, we develop tools and techniques that can be used separately and in conjunction to group sex ads by their true owner (and not the claimed author in the ad). Specifically, we develop a machine learning classifier that uses stylometry to distinguish between ads posted by the same vs. different authors with 90% TPR and 1% FPR. We also design a linking technique that takes advantage of leakages from the Bitcoin mempool, blockchain and sex ad site, to link a subset of sex ads to Bitcoin public wallets and transactions. Finally, we demonstrate via a 4-week proof of concept using Backpage as the sex ad site, how an analyst can use these automated approaches to potentially find human traffickers.

Underground forums are widely used by criminals to buy and sell a host of stolen items, datasets, resources, and criminal services. These forums contain important resources for understanding cybercrime. However, the number of forums, their size, and the domain expertise required to understand the markets makes manual exploration of these forums unscalable. In this work, we propose an automated, top-down approach for analyzing underground forums. Our approach uses natural language processing and machine learning to automatically generate high-level information about underground forums, first identifying posts related to transactions, and then extracting products and prices. We also demonstrate, via a pair of case studies, how an analyst can use these automated approaches to investigate other categories of products and transactions. We use eight distinct forums to assess our tools: Antichat, Blackhat World, Carders, Darkode, Hack Forums, Hell, L33tCrew and Nulled. Our automated approach is fast and accurate, achieving over 80% accuracy in detecting post category, product, and prices.

Most laptops and personal computers have webcams with LED indicators to notify users when they are recording. Because hackers use surreptitiously captured webcam recordings to extort users, we explored the effectiveness of these indicators under varying circumstances and how they could be improved. We observed that, on average, fewer than half of our participants (45%) noticed the existing indicator during computer-based tasks. When seated in front of the computer performing a paper-based task, only 5% noticed the indicator. We performed a followup experiment to evaluate a new indicator and observed that adding onscreen glyphs had a significant impact on both computer-based and non-computer-based tasks (93% and 59% noticed the new indicator, respectively). We discuss how our results can be integrated into current systems, as well as future ubiquitous computing systems.

In addition to storing a plethora of sensitive personal and work information, smartphones also store sensor data about users and their daily activities. In order to understand users' behaviors and attitudes towards the security of their smartphone data, we conducted 28 qualitative interviews. We examined why users choose (or choose not) to employ locking mechanisms (e.g., PINs) and their perceptions and awareness about the sensitivity of the data stored on their devices. We performed two additional online experiments to quantify our interview results and the extent to which sensitive data could be found in a user's smartphone-accessible email archive. We observed a strong correlation between use of security features and risk perceptions, which indicates rational behavior. However, we also observed that most users likely underestimate the extent to which data stored on their smartphones pervades their identities, online and offline.