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Classifying internet one-way traffic

Eduard Glatz, Xenofontas Dimitropoulos
2012 Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems - SIGMETRICS '12  
Previous work primarily analyzed IBR traffic to large unused IP address blocks called network telescopes.  ...  After thoroughly validating our classifier, we use it to analyze a massive data-set that covers 7.41 petabytes of traffic from a large backbone network to shed light into the composition of one-way traffic  ...  Acknowledgements We are grateful to SWITCH for providing us precious data for studying one-way traffic.  ... 
doi:10.1145/2254756.2254821 dblp:conf/sigmetrics/GlatzD12 fatcat:yy7kfnoerbgy7has6ik733esjy

Online hybrid traffic classifier for Peer-to-Peer systems based on network processors

Zhenxiang Chen, Bo Yang, Yuehui Chen, Ajith Abraham, Crina Grosan, Lizhi Peng
2009 Applied Soft Computing  
Some of the currently proposed P2P traffic classification methods, such as mapping of used IP-addresses or monitoring port numbers, are not reliable.  ...  This paper proposes a NPs-based online hybrid traffic classifier to identify active P2P traffic. The NPs-based Applied Soft Computing 9 (2009) 685-694  ...  Known transport-layer port numbers used to be an accurate and efficient way for traffic classification.  ... 
doi:10.1016/j.asoc.2008.09.010 fatcat:jzy426xgmjhrngnaqz3qy6ogpa

Adaptive Worm Detection Model Based on Multi Classifiers

T. S. Barhoom, H. A. Qeshta
2013 2013 Palestinian International Conference on Information and Communication Technology  
played important role to obtain an efficient classification model based on these features.  ...  Most recent researches were presented "Worms Detection" approaches based on classification techniques in data mining as an efficient ways to increase the security of networks.  ... 
doi:10.1109/picict.2013.20 fatcat:dbkghgrpibadtbzxaybv5pu4ii

Unconstrained endpoint profiling (googling the internet)

Ionut Trestian, Supranamaya Ranjan, Aleksandar Kuzmanovi, Antonio Nucci
2008 Computer communication review  
In this paper, we introduce a novel approach for profiling and classifying endpoints.  ...  classification capabilities when other schemes literally fall apart.  ...  One example is BLINC [29] , which uses a graphlet based approach to classify network traffic. Issues with such an approach are the following.  ... 
doi:10.1145/1402946.1402991 fatcat:oxtbrohldzhwlfxhx5yreyvzhi

Unconstrained endpoint profiling (googling the internet)

Ionut Trestian, Supranamaya Ranjan, Aleksandar Kuzmanovi, Antonio Nucci
2008 Proceedings of the ACM SIGCOMM 2008 conference on Data communication - SIGCOMM '08  
In this paper, we introduce a novel approach for profiling and classifying endpoints.  ...  classification capabilities when other schemes literally fall apart.  ...  One example is BLINC [29] , which uses a graphlet based approach to classify network traffic. Issues with such an approach are the following.  ... 
doi:10.1145/1402958.1402991 dblp:conf/sigcomm/TrestianRKN08 fatcat:oxwwqzjsjzhlzjablutolo7zye

Googling the Internet: Profiling Internet Endpoints via the World Wide Web

Ionut Trestian, Supranamaya Ranjan, Aleksandar Kuzmanovic, Antonio Nucci
2010 IEEE/ACM Transactions on Networking  
In this paper, we introduce a novel approach for profiling and classifying endpoints.  ...  high classification capabilities.  ...  We applied our approach to profile endpoints at four different world regions, and provided a unique and comprehensive set of insights about (i) network applications and protocols used in these regions,  ... 
doi:10.1109/tnet.2009.2031175 fatcat:r2h6sozsjbanxay3f4qc66ykti

Network traffic classification techniques and challenges

Noora Al Khater, Richard E Overill
2015 2015 Tenth International Conference on Digital Information Management (ICDIM)  
And where the final published version is provided on the Research Portal, if citing you are again advised to check the publisher's website for any subsequent corrections.  ...  [8] applied ML algorithms, Nearest Neighbours (NN), Linear Discriminate Analysis (LDA) and Quadratic Discriminant Analysis (QDA) to classify IP traffic based on the statistical signature approach.  ...  The EM algorithm is applied to cluster the network traffic into a number of groups and create classifier rules based on the clusters.  ... 
doi:10.1109/icdim.2015.7381869 dblp:conf/icdim/KhaterO15 fatcat:tjabveqzgjaevbrdwqmhrt6gpy

Optimizing Deep Packet Inspection for High-Speed Traffic Analysis

Niccolò Cascarano, Luigi Ciminiera, Fulvio Risso
2010 Journal of Network and Systems Management  
be greatly reduced while even improving the classification precision, making DPI suitable also for high-speed networks.  ...  Deep Packet Inspection (DPI) techniques are considered extremely expensive in terms of processing costs and therefore are usually deployed in edge networks, where the amount of data to be processed is  ...  While security applications tend to use TCP/IP normalization, some earlier works on traffic classification [14, 24, 25] seem to indicate that a packet-based approach is appropriate for all the cases  ... 
doi:10.1007/s10922-010-9181-x fatcat:7ersxz66snc2vatwnknr6xvq3q

Detecting BHP Flood Attacks in OBS Networks: A Machine Learning Prospective

2019 International Journal of Science and Applied Information Technology  
A classification technique learns models by applying them to a large historical data set derived from an edge node's performance during a simulation run.  ...  A powerful and promising approach in identifying misbehaving edge nodes causing BHP flooding attacks is Machine Learning (ML), and in particular, classification techniques.  ...  The IP traffic classification problem was studied in the context of ML by [15] .  ... 
doi:10.30534/ijsait/2019/26862019 fatcat:2rhntwiwcnga7grr2cfhlqo6be

BLINC

Thomas Karagiannis, Konstantina Papagiannaki, Michalis Faloutsos
2005 Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications - SIGCOMM '05  
Second, it can be tuned to balance the accuracy of the classification versus the number of successfully classified traffic flows.  ...  We present a fundamentally different approach to classifying traffic flows according to the applications that generate them.  ...  Acknowledgments The authors are thankful to Dr. Andrew Moore for facilitating this study and Dr. Petros Faloutsos for his valuable suggestions and constructive criticism.  ... 
doi:10.1145/1080091.1080119 dblp:conf/sigcomm/KaragiannisPF05 fatcat:bi3pfq3tzrb3jckl47zekiwfle

BLINC

Thomas Karagiannis, Konstantina Papagiannaki, Michalis Faloutsos
2005 Computer communication review  
Second, it can be tuned to balance the accuracy of the classification versus the number of successfully classified traffic flows.  ...  We present a fundamentally different approach to classifying traffic flows according to the applications that generate them.  ...  Acknowledgments The authors are thankful to Dr. Andrew Moore for facilitating this study and Dr. Petros Faloutsos for his valuable suggestions and constructive criticism.  ... 
doi:10.1145/1090191.1080119 fatcat:25f3bwsakvhhrmd3x2b5gno7hi

Finding New Varieties of Malware with the Classification of Network Behavior

Mitsuhiro HATADA, Tatsuya MORI
2017 IEICE transactions on information and systems  
An enormous number of malware samples pose a major threat to our networked society.  ...  In this paper, we present a traffic model for malware that can classify network behaviors of malware and identify new varieties of malware.  ...  [18] , an efficient malware classification system was presented that based on the protocol-aware and state-space features with potential input for various classification methods.  ... 
doi:10.1587/transinf.2016icp0019 fatcat:w2lnpqaaenad7ha636dxmn63ju

Automated Big Traffic Analytics for Cyber Security [article]

Yuantian Miao, Zichan Ruan, Lei Pan, Yu Wang, Jun Zhang, Yang Xiang
2018 arXiv   pre-print
traffic classification, and efficiency of classifiers.  ...  In terms of big data's three characteristics --- volume, variety and velocity, we review three state of the art techniques to mitigate the key challenges including real-time traffic classification, unknown  ...  Such an IDS checks the suspected source IP and ports, and if this traffic instance is classified as intrusion, then the tear-down command is expected to be sent to the Tor exit routers.  ... 
arXiv:1804.09023v1 fatcat:vh3agewlkrc2ji4kcrg4si26sm

A survey of techniques for internet traffic classification using machine learning

Thuy T.T. Nguyen, Grenville Armitage
2008 IEEE Communications Surveys and Tutorials  
This survey paper looks at emerging research into the application of Machine Learning (ML) techniques to IP traffic classification -an inter-disciplinary blend of IP networking and data mining techniques  ...  We also discuss a number of key requirements for the employment of ML-based traffic classifiers in operational IP networks, and qualitatively critique the extent to which the reviewed works meet these  ...  ACKNOWLEDGMENTS We would like to thank the anonymous reviewers for their very helpful comments and feedbacks to improve the manuscript.  ... 
doi:10.1109/surv.2008.080406 fatcat:etj5rci3lvcspb3ullpkxvltpm

Unsupervised host behavior classification from connection patterns

Guillaume Dewaele, Yosuke Himura, Pierre Borgnat, Kensuke Fukuda, Patrice Abry, Olivier Michel, Romain Fontugne, Kenjiro Cho, Hiroshi Esaki
2010 International Journal of Network Management  
Although many attempts described in the literature were devoted to flow or application classifications, these approaches are not always adaptable to the operational constraints of traffic monitoring (expected  ...  A novel host behavior classification approach is proposed as a preliminary step toward traffic classification and anomaly detection in network communication.  ...  First, an original 9D feature vector has been defined and shown to characterize accurately and efficiently traffic at the host behavior.  ... 
doi:10.1002/nem.750 fatcat:ahk5b733tbhmhnybogrgvcdkma
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