Active Data Collection Techniques to Understand Online Scammers and Cybercriminals

Nigerian scam, also known as advance fee fraud or 419 scam, is a prevalent form of online fraudulent activity that causes financial loss to individuals and businesses. Nigerian scam has evolved from simple non-targeted email messages to more sophisticated scams targeted at users of classifieds, dating and other websites. Even though such scams are observed and reported by users frequently, the community’s understanding of Nigerian scams is limited since the scammers operate “underground”. To better understand the underground Nigerian scam ecosystem and seek effective methods to deter Nigerian scam and cybercrime in general, we conduct a series of active and passive measurement studies. Relying upon the analysis and insight gained from the measurement studies, we make four contributions: (1) we analyze the taxonomy of Nigerian scam and derive long-term trends in scams; (2) we provide an insight on Nigerian scam and cybercrime ecosystems and their underground operation; (3) we propose a payment intervention as a potential deterrent to cybercrime operation in general and evaluate its effectiveness; and (4) we offer active and passive measurement tools and techniques that enable in-depth analysis of cybercrime ecosystems and deterrence on them. We first created and analyze a repository of more than two hundred thousand user-reported scam emails, stretching from 2006 to 2014, from four major scam reporting websites. We select ten most commonly observed scam categories and tag 2,000 scam emails randomly selected from our repository. Based upon the manually tagged dataset, we train a machine learning classifier and cluster all scam emails in the repository. From the clustering result, we find a strong and sustained upward trend for targeted scams and downward trend for non-targeted scams. We then focus on two types of targeted scams: sales scams and rental scams targeted users on Craigslist. We built an automated scam data collection system and gathered large-scale sales scam emails. Using the system we posted honeypot ads on Craigslist and conversed automatically with the scammers. Through the email conversation, the system obtained additional confirmation of likely scam activities and collected additional information such as IP addresses and shipping addresses. Our analysis revealed that around 10 groups were responsible for nearly half of the over 13,000 total scam attempts we received. These groups used IP addresses and shipping addresses in both Nigeria and the U.S. We also crawled rental ads on Craigslist, identified rental scam ads amongst the large number of benign ads and conversed with the potential scammers. Through in-depth analysis of the rental scams, we found seven major scam campaigns employing various operations and monetization methods. We also found that unlike sales scammers, most rental scammers were in the U.S. The large-scale scam data and in-depth analysis provide useful insights on how to design effective deterrence techniques against cybercrime in general. We study underground DDoS-for-hire services, also known as booters, and measure the effectiveness of undermining a payment system of DDoS Services. Our analysis shows that the payment intervention can have the desired effect of limiting cybercriminals’ ability and increasing the risk of accepting payments.

THERE GOES THE ELECTORAL NEIGHBORHOOD: LOCAL NETWORKS, ATTRIBUTION OF RESPONSIBILITY, AND THE (UN) FAIRNESS OF ELECTIONS

During the last two decades there have been but a handful of recorded cases of electoral fraud in Latin America. However, survey research consistently shows that often citizens do not trust the integrity of the electoral process. This dissertation addresses the puzzle by explaining the mismatch between how elections are conducted and how the process is perceived. My theoretical contribution provides a double-folded argument. First, voters’ trust in their community members (“the local experience”) impacts their level of confidence in the electoral process. Since voters often find their peers working at polling stations, negative opinions about them translate into negative opinions about the election. Second, perceptions of unfairness of the system (“the global effect”) negatively impact the way people perceive the transparency of the electoral process. When the political system fails to account for social injustice, citizens lose faith in the mechanism designed to elect representatives -and ultimately a set of policies. The fact that certain groups are systematically disregarded by the system triggers the notion that the electoral process is flawed. This is motivated by either egotropic or sociotropic considerations. To test these hypotheses, I employ a survey conducted in Costa Rica, El Salvador, Honduras, and Guatemala during May/June 2014, which includes a population-based experiment. I show that Voters who trust their peers consistently have higher confidence in the electoral process. Whereas respondents who were primed about social unfairness (treatment) expressed less confidence in the quality of the election. Finally, I find that the local experience is predominant over the global effect. The treatment has a statistically significant effect only for respondents who trust their community. Attribution of responsibility for voters who are skeptics of their peers is clear and simple, leaving no room for a more diffuse mechanism, the unfairness of the political system. Finally, now I extend analysis to the Latin America region. Using data from LAPOP that comprises four waves of surveys in 22 countries, I confirm the influence of the “local experience” and the “global effect” as determinants of the level of confidence in the electoral process.