12,953 research outputs found
Identifying a Criminal's Network of Trust
Tracing criminal ties and mining evidence from a large network to begin a
crime case analysis has been difficult for criminal investigators due to large
numbers of nodes and their complex relationships. In this paper, trust networks
using blind carbon copy (BCC) emails were formed. We show that our new shortest
paths network search algorithm combining shortest paths and network centrality
measures can isolate and identify criminals' connections within a trust
network. A group of BCC emails out of 1,887,305 Enron email transactions were
isolated for this purpose. The algorithm uses two central nodes, most
influential and middle man, to extract a shortest paths trust network.Comment: 2014 Tenth International Conference on Signal-Image Technology &
Internet-Based Systems (Presented at Third International Workshop on Complex
Networks and their Applications,SITIS 2014, Marrakesh, Morocco, 23-27,
November 2014
Search Rank Fraud De-Anonymization in Online Systems
We introduce the fraud de-anonymization problem, that goes beyond fraud
detection, to unmask the human masterminds responsible for posting search rank
fraud in online systems. We collect and study search rank fraud data from
Upwork, and survey the capabilities and behaviors of 58 search rank fraudsters
recruited from 6 crowdsourcing sites. We propose Dolos, a fraud
de-anonymization system that leverages traits and behaviors extracted from
these studies, to attribute detected fraud to crowdsourcing site fraudsters,
thus to real identities and bank accounts. We introduce MCDense, a min-cut
dense component detection algorithm to uncover groups of user accounts
controlled by different fraudsters, and leverage stylometry and deep learning
to attribute them to crowdsourcing site profiles. Dolos correctly identified
the owners of 95% of fraudster-controlled communities, and uncovered fraudsters
who promoted as many as 97.5% of fraud apps we collected from Google Play. When
evaluated on 13,087 apps (820,760 reviews), which we monitored over more than 6
months, Dolos identified 1,056 apps with suspicious reviewer groups. We report
orthogonal evidence of their fraud, including fraud duplicates and fraud
re-posts.Comment: The 29Th ACM Conference on Hypertext and Social Media, July 201
DNA-inspired online behavioral modeling and its application to spambot detection
We propose a strikingly novel, simple, and effective approach to model online
user behavior: we extract and analyze digital DNA sequences from user online
actions and we use Twitter as a benchmark to test our proposal. We obtain an
incisive and compact DNA-inspired characterization of user actions. Then, we
apply standard DNA analysis techniques to discriminate between genuine and
spambot accounts on Twitter. An experimental campaign supports our proposal,
showing its effectiveness and viability. To the best of our knowledge, we are
the first ones to identify and adapt DNA-inspired techniques to online user
behavioral modeling. While Twitter spambot detection is a specific use case on
a specific social media, our proposed methodology is platform and technology
agnostic, hence paving the way for diverse behavioral characterization tasks
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