60,892 research outputs found
A traffic classification method using machine learning algorithm
Applying concepts of attack investigation in IT industry, this idea has been developed to design
a Traffic Classification Method using Data Mining techniques at the intersection of Machine
Learning Algorithm, Which will classify the normal and malicious traffic. This classification will
help to learn about the unknown attacks faced by IT industry. The notion of traffic classification
is not a new concept; plenty of work has been done to classify the network traffic for
heterogeneous application nowadays. Existing techniques such as (payload based, port based
and statistical based) have their own pros and cons which will be discussed in this
literature later, but classification using Machine Learning techniques is still an open field to explore and has provided very promising results up till now
Characterizing User-to-User Connectivity with RIPE Atlas
Characterizing the interconnectivity of networks at a country level is an
interesting but non-trivial task. The IXP Country Jedi is an existing prototype
that uses RIPE Atlas probes in order to explore interconnectivity at a country
level, taking into account all Autonomous Systems (AS) where RIPE Atlas probes
are deployed. In this work, we build upon this basis and specifically focus on
"eyeball" networks, i.e. the user-facing networks with the largest user
populations in any given country, and explore to what extent we can provide
insights on their interconnectivity. In particular, with a focused user-to-user
(and/or user-to-content) version of the IXP Country Jedi we work towards
meaningful statistics and comparisons between countries/economies. This is
something that a general-purpose probe-to-probe version is not able to capture.
We present our preliminary work on the estimation of RIPE Atlas coverage in
eyeball networks, as well as an approach to measure and visualize user
interconnectivity with our Eyeball Jedi tool.Comment: In Proceedings of the Applied Networking Research Workshop (ANRW '17
Characterization of P2P IPTV Traffic: Scaling Analysis
P2P IPTV applications arise on the Internet and will be massively used in the
future. It is expected that P2P IPTV will contribute to increase the overall
Internet traffic. In this context, it is important to measure the impact of P2P
IPTV on the networks and to characterize this traffic. Dur- ing the 2006 FIFA
World Cup, we performed an extensive measurement campaign. We measured network
traffic generated by broadcasting soc- cer games by the most popular P2P IPTV
applications, namely PPLive, PPStream, SOPCast and TVAnts. From the collected
data, we charac- terized the P2P IPTV traffic structure at different time
scales by using wavelet based transform method. To the best of our knowledge,
this is the first work, which presents a complete multiscale analysis of the
P2P IPTV traffic. Our results show that the scaling properties of the TCP
traffic present periodic behavior whereas the UDP traffic is stationary and
lead to long- range depedency characteristics. For all the applications, the
download traffic has different characteristics than the upload traffic. The
signaling traffic has a significant impact on the download traffic but it has
negligible impact on the upload. Both sides of the traffic and its granularity
has to be taken into account to design accurate P2P IPTV traffic models.Comment: 27p, submitted to a conferenc
Passive characterization of sopcast usage in residential ISPs
Abstract—In this paper we present an extensive analysis of traffic generated by SopCast users and collected from operative networks of three national ISPs in Europe. After more than a year of continuous monitoring, we present results about the popularity of SopCast which is the largely preferred application in the studied networks. We focus on analysis of (i) application and bandwidth usage at different time scales, (ii) peer lifetime, arrival and departure processes, (iii) peer localization in the world. Results provide useful insights into users ’ behavior, including their attitude towards P2P-TV application usage and the conse-quent generated load on the network, that is quite variable based on the access technology and geographical location. Our findings are interesting to Researchers interested in the investigation of users ’ attitude towards P2P-TV services, to foresee new trends in the future usage of the Internet, and to augment the design of their application. I
Characterization of ISP Traffic: Trends, User Habits, and Access Technology Impact
In the recent years, the research community has increased its focus on network monitoring which is seen as a key tool to understand the Internet and the Internet users. Several studies have presented a deep characterization of a particular application, or a particular network, considering the point of view of either the ISP, or the Internet user. In this paper, we take a different perspective. We focus on three European countries where we have been collecting traffic for more than a year and a half through 5 vantage points with different access technologies. This humongous amount of information allows us not only to provide precise, multiple, and quantitative measurements of "What the user do with the Internet" in each country but also to identify common/uncommon patterns and habits across different countries and nations. Considering different time scales, we start presenting the trend of application popularity; then we focus our attention to a one-month long period, and further drill into a typical daily characterization of users activity. Results depict an evolving scenario due to the consolidation of new services as Video Streaming and File Hosting and to the adoption of new P2P technologies. Despite the heterogeneity of the users, some common tendencies emerge that can be leveraged by the ISPs to improve their servic
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