92,455 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
RAPTOR: Routing Attacks on Privacy in Tor
The Tor network is a widely used system for anonymous communication. However,
Tor is known to be vulnerable to attackers who can observe traffic at both ends
of the communication path. In this paper, we show that prior attacks are just
the tip of the iceberg. We present a suite of new attacks, called Raptor, that
can be launched by Autonomous Systems (ASes) to compromise user anonymity.
First, AS-level adversaries can exploit the asymmetric nature of Internet
routing to increase the chance of observing at least one direction of user
traffic at both ends of the communication. Second, AS-level adversaries can
exploit natural churn in Internet routing to lie on the BGP paths for more
users over time. Third, strategic adversaries can manipulate Internet routing
via BGP hijacks (to discover the users using specific Tor guard nodes) and
interceptions (to perform traffic analysis). We demonstrate the feasibility of
Raptor attacks by analyzing historical BGP data and Traceroute data as well as
performing real-world attacks on the live Tor network, while ensuring that we
do not harm real users. In addition, we outline the design of two monitoring
frameworks to counter these attacks: BGP monitoring to detect control-plane
attacks, and Traceroute monitoring to detect data-plane anomalies. Overall, our
work motivates the design of anonymity systems that are aware of the dynamics
of Internet routing
Map equation for link community
Community structure exists in many real-world networks and has been reported
being related to several functional properties of the networks. The
conventional approach was partitioning nodes into communities, while some
recent studies start partitioning links instead of nodes to find overlapping
communities of nodes efficiently. We extended the map equation method, which
was originally developed for node communities, to find link communities in
networks. This method is tested on various kinds of networks and compared with
the metadata of the networks, and the results show that our method can identify
the overlapping role of nodes effectively. The advantage of this method is that
the node community scheme and link community scheme can be compared
quantitatively by measuring the unknown information left in the networks
besides the community structure. It can be used to decide quantitatively
whether or not the link community scheme should be used instead of the node
community scheme. Furthermore, this method can be easily extended to the
directed and weighted networks since it is based on the random walk.Comment: 9 pages,5 figure
Fast Multi-Scale Community Detection based on Local Criteria within a Multi-Threaded Algorithm
Many systems can be described using graphs, or networks. Detecting
communities in these networks can provide information about the underlying
structure and functioning of the original systems. Yet this detection is a
complex task and a large amount of work was dedicated to it in the past decade.
One important feature is that communities can be found at several scales, or
levels of resolution, indicating several levels of organisations. Therefore
solutions to the community structure may not be unique. Also networks tend to
be large and hence require efficient processing. In this work, we present a new
algorithm for the fast detection of communities across scales using a local
criterion. We exploit the local aspect of the criterion to enable parallel
computation and improve the algorithm's efficiency further. The algorithm is
tested against large generated multi-scale networks and experiments demonstrate
its efficiency and accuracy.Comment: arXiv admin note: text overlap with arXiv:1204.100
A morphospace of functional configuration to assess configural breadth based on brain functional networks
The best approach to quantify human brain functional reconfigurations in
response to varying cognitive demands remains an unresolved topic in network
neuroscience. We propose that such functional reconfigurations may be
categorized into three different types: i) Network Configural Breadth, ii)
Task-to-Task transitional reconfiguration, and iii) Within-Task
reconfiguration. In order to quantify these reconfigurations, we propose a
mesoscopic framework focused on functional networks (FNs) or communities. To do
so, we introduce a 2D network morphospace that relies on two novel mesoscopic
metrics, Trapping Efficiency (TE) and Exit Entropy (EE), which capture topology
and integration of information within and between a reference set of FNs. In
this study, we use this framework to quantify the Network Configural Breadth
across different tasks. We show that the metrics defining this morphospace can
differentiate FNs, cognitive tasks and subjects. We also show that network
configural breadth significantly predicts behavioral measures, such as episodic
memory, verbal episodic memory, fluid intelligence and general intelligence. In
essence, we put forth a framework to explore the cognitive space in a
comprehensive manner, for each individual separately, and at different levels
of granularity. This tool that can also quantify the FN reconfigurations that
result from the brain switching between mental states.Comment: main article: 24 pages, 8 figures, 2 tables. supporting information:
11 pages, 5 figure
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