BlockTag: Design and applications of a tagging system for blockchain analysis

Annotating blockchains with auxiliary data is useful for many applications. For example, e-crime investigations of illegal Tor hidden services, such as Silk Road, often involve linking Bitcoin addresses, from which money is sent or received, to user accounts and related online activities. We present BlockTag, an open-source tagging system for blockchains that facilitates such tasks. We describe BlockTag's design and present three analyses that illustrate its capabilities in the context of privacy research and law enforcement

Interacting with Large Distributed Datasets using Sketch

We present Sketch, a distributed software infrastructure for building interactive tools for exploring large datasets, distributed across multiple machines. We have built three sophisticated applications using this framework: a billion-row spreadsheet, a distributed log browser, and a distributed- systems performance debugging tool. Sketch applications allow interactive and responsive exploration of complex distributed datasets, scaling gracefully to large system sizes. The conflicting constraints of large-scale data and small timescales required by human interaction are difficult to satisfy simultaneously. Sketch exploits a sweet spot in this trade-off by exploiting the observation that the precision of a data view is limited by the resolution of the user?s screen. The system pushes data reduction operations to the data sources. The core Sketch abstraction provides a narrow programming interface; Sketch clients construct a distributed application by stacking modular components with identical interfaces, each providing a useful feature: network transparency, concurrency, fault-tolerance, straggler avoidance, round-trip reduction, distributed aggregation

On profiling bots in social media

Submit request for dataset at https://larc.smu.edu.sg/twitter-bot-profiling</p

Security analysis of malicious socialbots on the web

The open nature of the Web, online social networks (OSNs) in particular, makes it possible to design socialbots—automation software that controls fake accounts in a target OSN, and has the ability to perform basic activities similar to those of real users. In the wrong hands, socialbots can be used to infiltrate online communities, build up trust over time, and then engage in various malicious activities. This dissertation presents an in-depth security analysis of malicious socialbots on the Web, OSNs in particular. The analysis focuses on two main goals: (1) to characterize and analyze the vulnerability of OSNs to cyber attacks by malicious socialbots, social infiltration in particular, and (2) to design and evaluate a countermeasure to efficiently and effectively defend against socialbots. To achieve these goals, we first studied social infiltration as an organized campaign operated by a socialbot network (SbN)—a group of programmable socialbots that are coordinated by an attacker in a botnet-like fashion. We implemented a prototypical SbN consisting of 100 socialbots and operated it on Facebook for 8 weeks. Among various findings, we observed that some users are more likely to become victims than others, depending on factors related to their social structure. Moreover, we found that traditional OSN defenses are not effective at identifying automated fake accounts or their social infiltration campaigns. Based on these findings, we designed Íntegro—an infiltration-resilient defense system that helps OSNs detect automated fake accounts via a user ranking scheme. In particular, Íntegro relies on a novel approach that leverages victim classification for robust graph-based fake account detection, with provable security guarantees. We implemented Íntegro on top of widely-used, open-source distributed systems, in which it scaled nearly linearly. We evaluated Íntegro against SybilRank—the state-of-the-art in graph-based fake account detection—using real-world datasets and a large-scale, production-class deployment at Tuenti, the largest OSN in Spain with more than 15 million users. We showed that Íntegro significantly outperforms SybilRank in ranking quality, allowing Tuenti to detect at least 10 times more fake accounts than their current abuse detection system.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat

To Befriend Or Not? A Model of Friend Request Acceptance on Facebook

ABSTRACT Accepting friend requests from strangers in Facebook-like online social networks is known to be a risky behavior. Still, empirical evidence suggests that Facebook users often accept such requests with high rate. As a first step towards technology support of users in their decisions about friend requests, we investigate why users accept such requests. We conducted two studies of users&apos; befriending behavior on Facebook. Based on 20 interviews with active Facebook users, we developed a friend request acceptance model that explains how various factors influence user acceptance behavior. To test and refine our model, we also conducted a confirmatory study with 397 participants using Amazon Mechanical Turk. We found that four factors significantly impact the receiver&apos;s decision, namely, knowing the requester&apos;s in real world, having common hobbies or interests, having mutual friends, and the closeness of mutual friends. Based on our findings, we offer design guidelines for improving the usability of the corresponding user interfaces