6,886 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
YouTube Traffic Content Analysis in the Perspective of Clip Category and Duration
In this work, we study YouTube traffic characteristics in a medium-sized Swedish residential municipal network that has 2600 mainly FTTH broadband-connected households. YouTube traffic analyses were carried out in the perspective of video clip category and duration, in order to understand their impact on the potential local network caching gains. To the best of our knowledge, this is the first time systematic analysis of YouTube traffic content in the perspective of video clip category and duration in a residential broadband network. Our results show that the requested YouTube video clips from the end users in the studied network were imbalanced in regarding the video categories and durations. The dominating video category was Music, both in terms of the total traffic share as well as the contribution to the overall potential local network caching gain. In addition, most of the requested video clips were between 2-5 min in duration, despite video clips with durations over 15 min were also popular among certain video categories, e.g. film videos
Characterizing Popularity Dynamics of User-generated Videos: A Category-based Study of YouTube
Understanding the growth pattern of content popularity has become a subject of immense interest to
Internet service providers, content makers and on-line advertisers. This understanding is also important for
the sustainable development of content distribution systems. As an approach to comprehend the characteristics of this growth pattern, a significant amount of research has been done in analyzing the popularity
growth patterns of YouTube videos. Unfortunately, no work has been done that intensively investigates the
popularity patterns of YouTube videos based on video object category. In this thesis, an in-depth analysis
of the popularity pattern of YouTube videos is performed, considering the categories of videos.
Metadata and request patterns were collected by employing category-specific YouTube crawlers. The
request patterns were observed for a period of five months. Results confirm that the time varying popularity
of di fferent YouTube categories are conspicuously diff erent, in spite of having sets of categories with very
similar viewing patterns. In particular, News and Sports exhibit similar growth curves, as do Music and
Film.
While for some categories views at early ages can be used to predict future popularity, for some others
predicting future popularity is a challenging task and require more sophisticated techniques, e.g., time-series clustering. The outcomes of these analyses are instrumental towards designing a reliable workload generator, which can be further used to evaluate diff erent caching policies for YouTube and similar sites. In this
thesis, workload generators for four of the YouTube categories are developed. Performance of these workload generators suggest that a complete category-specific workload generator can be developed using time-series clustering. Patterns of users' interaction with YouTube videos are also analyzed from a dataset collected in a local network. This shows the possible ways of improving the performance of Peer-to-Peer video distribution
technique along with a new video recommendation method
Fake View Analytics in Online Video Services
Online video-on-demand(VoD) services invariably maintain a view count for
each video they serve, and it has become an important currency for various
stakeholders, from viewers, to content owners, advertizers, and the online
service providers themselves. There is often significant financial incentive to
use a robot (or a botnet) to artificially create fake views. How can we detect
the fake views? Can we detect them (and stop them) using online algorithms as
they occur? What is the extent of fake views with current VoD service
providers? These are the questions we study in the paper. We develop some
algorithms and show that they are quite effective for this problem.Comment: 25 pages, 15 figure
Analysis of internet traffic in educational network based on users' preferences
The demand for Internet services and network resources in Educational networks are increasing rapidly.Specifically, the revolution of web 2.0 "also referred to as the Read-Write Web" has changed the way of information exchange and distribution. Although web 2.0 has gained attraction in all sectors of the education industry, but it results in high-traffic loads on networks which often leads to the Internet users" dissatisfaction.Therefore, analyzing Internet traffic becomes an urgent need to provide high-quality service, monitoring bandwidth usage.In this study, we focus on analyzing the Internet traffic in Universiti Utara Malaysia (UUM) main campus. We performed measurement analysis form the application level characteristics based on users' preferences.A total of three methodological steps are carried out to meet the objective of this study namely data collection, data analysis and data presentation.The finding shows that social networks are the most web applications visited in UUM.These findings lead to facilitate the enhancement of Educational network performance and Internet bandwidth strategies
A Compression Technique Exploiting References for Data Synchronization Services
Department of Computer Science and EngineeringIn a variety of network applications, there exists significant amount of shared data between two end hosts. Examples include data synchronization services that replicate data from one node to another. Given that shared data may have high correlation with new data to transmit, we question how such shared data can be best utilized to improve the efficiency of data transmission. To answer this, we develop an encoding technique, SyncCoding, that effectively replaces bit sequences of the data to be transmitted with the pointers to their matching bit sequences in the shared data so called references. By doing so, SyncCoding can reduce data traffic, speed up data transmission, and save energy consumption for transmission. Our evaluations of SyncCoding implemented in Linux show that it outperforms existing popular encoding techniques, Brotli, LZMA, Deflate, and Deduplication. The gains of SyncCoding over those techniques in the perspective of data size after compression in a cloud storage scenario are about 12.4%, 20.1%, 29.9%, and 61.2%, and are about 78.3%, 79.6%, 86.1%, and 92.9% in a web browsing scenario, respectively.ope
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