9,568 research outputs found
The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis
In recent years, mobile devices (e.g., smartphones and tablets) have met an
increasing commercial success and have become a fundamental element of the
everyday life for billions of people all around the world. Mobile devices are
used not only for traditional communication activities (e.g., voice calls and
messages) but also for more advanced tasks made possible by an enormous amount
of multi-purpose applications (e.g., finance, gaming, and shopping). As a
result, those devices generate a significant network traffic (a consistent part
of the overall Internet traffic). For this reason, the research community has
been investigating security and privacy issues that are related to the network
traffic generated by mobile devices, which could be analyzed to obtain
information useful for a variety of goals (ranging from device security and
network optimization, to fine-grained user profiling).
In this paper, we review the works that contributed to the state of the art
of network traffic analysis targeting mobile devices. In particular, we present
a systematic classification of the works in the literature according to three
criteria: (i) the goal of the analysis; (ii) the point where the network
traffic is captured; and (iii) the targeted mobile platforms. In this survey,
we consider points of capturing such as Wi-Fi Access Points, software
simulation, and inside real mobile devices or emulators. For the surveyed
works, we review and compare analysis techniques, validation methods, and
achieved results. We also discuss possible countermeasures, challenges and
possible directions for future research on mobile traffic analysis and other
emerging domains (e.g., Internet of Things). We believe our survey will be a
reference work for researchers and practitioners in this research field.Comment: 55 page
Hypersparse Neural Network Analysis of Large-Scale Internet Traffic
The Internet is transforming our society, necessitating a quantitative
understanding of Internet traffic. Our team collects and curates the largest
publicly available Internet traffic data containing 50 billion packets.
Utilizing a novel hypersparse neural network analysis of "video" streams of
this traffic using 10,000 processors in the MIT SuperCloud reveals a new
phenomena: the importance of otherwise unseen leaf nodes and isolated links in
Internet traffic. Our neural network approach further shows that a
two-parameter modified Zipf-Mandelbrot distribution accurately describes a wide
variety of source/destination statistics on moving sample windows ranging from
100,000 to 100,000,000 packets over collections that span years and continents.
The inferred model parameters distinguish different network streams and the
model leaf parameter strongly correlates with the fraction of the traffic in
different underlying network topologies. The hypersparse neural network
pipeline is highly adaptable and different network statistics and training
models can be incorporated with simple changes to the image filter functions.Comment: 11 pages, 10 figures, 3 tables, 60 citations; to appear in IEEE High
Performance Extreme Computing (HPEC) 201
Intrusion detection mechanisms for VoIP applications
VoIP applications are emerging today as an important component in business
and communication industry. In this paper, we address the intrusion detection
and prevention in VoIP networks and describe how a conceptual solution based on
the Bayes inference approach can be used to reinforce the existent security
mechanisms. Our approach is based on network monitoring and analyzing of the
VoIP-specific traffic. We give a detailed example on attack detection using the
SIP signaling protocol
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
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