P2P Traffic Classification using Advanced Heuristic Rules and Analysis of Decision Tree Algorithms (개선된 휴리스틱 규칙 및 의사 결정 트리 분석을 이용한 P2P 트래픽 분류 기법)

예우지엔; Kyungsan CHO (조경산)

P2P Traffic Classification using Advanced Heuristic Rules and Analysis of Decision Tree Algorithms

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2014, 19(3), pp.45-54
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science

예우지엔 ¹, Kyungsan CHO ¹

¹단국대학교

Accredited

ABSTRACT

In this paper, an improved two-step P2P traffic classification scheme is proposed to overcomethe limitations of the existing methods. The first step is a signature-based classifier at thepacket-level. The second step consists of pattern heuristic rules and a statistics-based classifier atthe flow-level. With pattern heuristic rules, the accuracy can be improved and the amount oftraffic to be classified by statistics-based classifier can be reduced. Based on the analysis ofdifferent decision tree algorithms, the statistics-based classifier is implemented with REPTree. Inaddition, the ensemble algorithm is used to improve the performance of statistics-based classifier. Through the verification with the real datasets, it is shown that our hybrid scheme provides higheraccuracy and lower overhead compared to other existing schemes.

KEYWORDS

P2P traffic classification, hybrid scheme, signature-based, heuristic rules, decision tree

Citation status

* References for papers published after 2023 are currently being built.

[confproc] Myung-Yoon Lee / 2006 / SPNS realization for secure P2P Service / Korea Multimedia Society : 67~70

[journal] 유재학 / 2010 / 다중 클래스 SVM을 이용한 계층적 인터넷 애플리케이션 트래픽의 분류 / 한국지능시스템학회 논문지 / 한국지능시스템학회 20(1) : 7~14

[journal] 엄남경 / 2008 / Flow-based P2P Traffic Identification using SVM / 한국컴퓨터정보학회논문지 / 한국컴퓨터정보학회 13(3) : 123~130

[confproc] Yu-Shui Geng / 2009 / The Research of P2P Traffic Identification Technology / Proc. of International Conference on E-Business and Information System Security : 1~4

[confproc] Subhabrata Sen / 2004 / Accurate, scalable in network identification of P2P traffic using application signature / Proc. the 13th international conference on World Wide Web : 512~521

[confproc] Alok Madhukar / 2006 / A longitudinal study of P2P traffic classification / Proc. 4th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems : 179~188

[journal] Xin-Bin Liu / 2008 / Automated mining of packet signatures for traffic identification application layer with apriori algorithm / Journal on Communications 29(12) : 51~59

[journal] G´eza Szab´o / 2008 / On the Validation of Traffic Classification algorithms / Passive and Active Network Measurement Lecture Notes in Computer Science : 72~81

[confproc] Thomas Karagiannis / 2004 / Transport layer identification of P2P traffic / Proc. the 4th ACM SIGCOMMConference on Internet Measurement : 121~134

[journal] Yaou Zhao / 2014 / Hierarchical real-time network traffic classification based on ECOC / TELKOMNIKA Indonesian Journal of Electrical Engineering 12(2) : 1551~1560

[confproc] Aiqing Zhu / 2014 / A P2P Network Traffic Classification Method Based on C4.5 Decision Tree Algorithm / Proc. of the 9th International Symposium on Linear Drives for Industry Applications 4 : 373~379

[journal] Wujian Ye / Hybrid P2P traffic classification with heuristic rules and machine learning / Soft Computing Journal

[web] / Pruning / http://en.wikipedia.org/wiki/Pruning_(decision_trees)

[journal] S. B. Kotsiantis / 2013 / Decision trees: a recent overview / Artificial Intelligence Review 39(4) : 261~283

[book] Oded Maimon / 2010 / Data Mining and Knowledge Discovery Handbook / Springer

[journal] Marina Skurichina / 2002 / Bagging, Boosting and the Random Subspace Method for Linear Classifiers / Pattern Analysis and Applications 5(2) : 121~135

[journal] S. Kotsiantis / 2011 / Combining bagging, boosting, rotation forest and random subspace methods / Artificial Intelligence Review 35(3) : 223~240

[journal] Tin Kam Ho / 1998 / The Random Subspace Method for Constructing Decision Forests / IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8) : 832~844

[journal] Robert E. Banfield / 2007 / A comparison of decision tree ensemble creation techniques / IEEE Transactions on Pattern Analysis and Machine Intelligence 29(1) : 173~180

[journal] Tianyan Jiang / 2011 / Improved Bagging Algorithm for Pattern Recognition in UHF Signals of Partial Discharges / Energies 4(7) : 1087~1101

[journal] Zhen-Xiang Chen / 2009 / Online Hybrid traffic classifier for Peer-to Peer Systems based on Network Processors / Applied Soft Computing 9(2) : 685~694

[journal] Jun Li / 2009 / Hybrid Internet Traffic Classification Technique / Journal of Electronics(China) 26(1) : 101~112

[journal] Ram Keralapura / 2010 / A novel self-learning architecture for p2p traffic classification in high speed networks / Computer Networks 54(7) : 1055~1068

[confproc] Wujian Ye / 2013 / Two-Step P2P Traffic Classification with Connection Heuristics / Proc. of IMIS2013-Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing : 135~141

[web] / JPcap / http://www.eden.rutgers.edu/~muscarim/jpcap/index.html

[web] / Weka / http://www.cs.waikato.ac.nz/ml/weka/

[journal] Thuy T. T. Nguyen / 2008 / A survey of techniques for internet traffic classification using machine learning / Proc. of IEEE Communications Surveys and Tutorials 10(4) : 56~76

[web] / Precision and recall / http://en.wikipedia.org/wiki/Recall_and_precision

[journal] F. Gringoli / 2009 / GT : picking up the truth from the ground for Internet traffic / ACM SIGCOMM Computer Communication Review 39(5) : 12~18

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65