8,051 research outputs found
FraudDroid: Automated Ad Fraud Detection for Android Apps
Although mobile ad frauds have been widespread, state-of-the-art approaches
in the literature have mainly focused on detecting the so-called static
placement frauds, where only a single UI state is involved and can be
identified based on static information such as the size or location of ad
views. Other types of fraud exist that involve multiple UI states and are
performed dynamically while users interact with the app. Such dynamic
interaction frauds, although now widely spread in apps, have not yet been
explored nor addressed in the literature. In this work, we investigate a wide
range of mobile ad frauds to provide a comprehensive taxonomy to the research
community. We then propose, FraudDroid, a novel hybrid approach to detect ad
frauds in mobile Android apps. FraudDroid analyses apps dynamically to build UI
state transition graphs and collects their associated runtime network traffics,
which are then leveraged to check against a set of heuristic-based rules for
identifying ad fraudulent behaviours. We show empirically that FraudDroid
detects ad frauds with a high precision (93%) and recall (92%). Experimental
results further show that FraudDroid is capable of detecting ad frauds across
the spectrum of fraud types. By analysing 12,000 ad-supported Android apps,
FraudDroid identified 335 cases of fraud associated with 20 ad networks that
are further confirmed to be true positive results and are shared with our
fellow researchers to promote advanced ad fraud detectionComment: 12 pages, 10 figure
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
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