Skip to main content
SpringerLink
Log in

Revisiting of peer-to-peer traffic: taxonomy, applications, identification techniques, new trends and challenges

  • Review
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

The services provided through peer-to-peer (P2P) architecture involve the transmission of text, images, documents, and multimedia. Especially the distribution of multimedia content like video and audio is mainly demanded by clients and has become the major reason for generating traffic by consuming significant bandwidth. This traffic is mostly generated by P2P applications like Napster, Gnutella, BitTorrent, PPTV, YuppTV, and many more. To use the network bandwidth proficiently, thus classification and identification of this Internet traffic became necessary. Moreover, it is required to classify the specific P2P application traffic, so data distribution over the P2P network can be improved. This survey paper discusses the working of different P2P applications for which traffic is created and raises related issues. The paper deliberates the various techniques and overlays that are used to provide the services over the P2P network. This paper includes the various techniques of feature selection and the machine learning algorithm for the identification and classification of internet traffic. This paper also reviewed the recent developments and highlights the future direction of research work in P2P networks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Data availibility

The author(s) will supply the datasets and materials utilized and/or analyzed in this work.

References

  1. Forouzan BA (2007) Data communications and networking (SIE). Tata McGraw-Hill Education, New York

    Google Scholar 

  2. Thampi SM (2013) A review on P2P video streaming. arXiv preprint arXiv:1304.1235

  3. Tran DA, Hua KA, Do T (2003) Zigzag: an efficient peer-to-peer scheme for media streaming. In IEEE INFOCOM 2003. In: Twenty-second annual joint conference of the IEEE computer and communications societies (IEEE Cat. No. 03CH37428), vol 2, pp 1283–1292. IEEE

  4. Bhatia M, Rai MK (2017) Identifying P2P traffic: a survey. Peer-to-Peer Netw Appl 10(5):1182–1203

    Google Scholar 

  5. Karagiannis T, Broido A, Brownlee N, Claffy K, Faloutsos M (2003) File-sharing in the Internet: a characterization of P2P traffic in the backbone. Tech. Rep., University of California, Riverside, USA

  6. Pal K, Govil MC, Ahmed M (2018) Priority-based scheduling scheme for live video streaming in peer-to-peer network. Multimedia Tools Appl 77(18):24427–24457

    Google Scholar 

  7. Pal K, Govil MC, Ahmed M (2017) Comparative analysis of utilization based hybrid overlay for live video streaming in P2P network. Int J Intell Eng Syst 10(3):343–350

    Google Scholar 

  8. Yang K, Wang B, Zhang Z (2013) A method of identifying P2P live streaming based on union features. In: 2013 IEEE 4th international conference on software engineering and service science. IEEE, pp 426–429

  9. Gomes JV, Inácio PRM, Pereira M, Freire MM, Monteiro PP (2013) Detection and classification of peer-to-peer traffic: a survey. ACM Comput Surv (CSUR) 45(3):1–40

    MATH  Google Scholar 

  10. Moore AW, Papagiannaki K (2005) Toward the accurate identification of network applications. In: International workshop on passive and active network measurement. Springer, Berlin, pp 41–54

  11. Chen Y, Zhang B, Chen C, Chiu DM (2013) Performance modeling and evaluation of peer-to-peer live streaming systems under flash crowds. IEEE/ACM Trans Netw 22(4):1106–1120

    Google Scholar 

  12. Wallach DS (2003) A survey of peer-to-peer security issues. In: 2002 Mext-NSF-JSPS international conference on software security: theories and systems (ISSS)

  13. Androutsellis-Theotokis S, Spinellis D (2004) A survey of peer-to-peer content distribution technologies. ACM Comput Surv (CSUR) 36(4):335–371

    Google Scholar 

  14. Lua EK, Crowcroft J, Pias M, Sharma R, Lim S (2005) A survey and comparison of peer-to-peer overlay network schemes. IEEE Commun Surv Tutor 7(2):72–93

    Google Scholar 

  15. Chothia T, Chatzikokolakis K (2005) A survey of anonymous peer-to-peer file-sharing. In: International conference on embedded and ubiquitous computing. Springer, Berlin, pp. 744–755

  16. Li B, Yin H (2007) Peer-to-peer live video streaming on the internet: issues, existing approaches, and challenges [Peer-to-Peer Multimedia Streaming]. IEEE Commun Mag 45(6):94–99

    Google Scholar 

  17. Xiao R-Y (2008) Survey on anonymity in unstructured peer-to-peer systems. J Comput Sci Technol 23(4):660–671

    Google Scholar 

  18. Liu Y, Guo Y, Liang C (2008) A survey on peer-to-peer video streaming systems. Peer-to-Peer Netw Appl 1(1):18–28

    Google Scholar 

  19. Park H, Yang J, Park J, Kang SG, Choi JK (2008) A survey on peer-to-peer overlay network schemes. In: 2008 10th international conference on advanced communication technology, vol 2. IEEE, pp 986–988

  20. Peltotalo J, Harju J, Jantunen A, Saukko M, Vatamoinen L, Curcio I, Bouazizi I, Hannuksela M (2008) Peer-to-peer streaming technology survey. In: Seventh international conference on networking (ICN 2008). IEEE, pp 342–350

  21. Gupta A, Awasthi LK (2012) Peer-to-peer networks and computation: current trends and future perspectives. Comput Inform 30(3):559–594

    Google Scholar 

  22. Hareesh K, Manjaiah DH (2011) Peer-to-peer live streaming and video on demand design issues and its challenges. Int J Peer Peer Netw 2(4):1

    Google Scholar 

  23. Shen Z, Luo J, Zimmermann R, Vasilakos AV (2011) Peer-to-peer media streaming: insights and new developments. Proc IEEE 99(12):2089–2109

    Google Scholar 

  24. Zhang X, Hassanein H (2012) A survey of peer-to-peer live video streaming schemes-an algorithmic perspective. Comput Netw 56(15):3548–3579

    Google Scholar 

  25. Shang-Fu G, Jian-Lei Z (2012) A survey of reputation and trust mechanism in peer-to-peer network. In: 2012 international conference on industrial control and electronics engineering. IEEE, pp 116–119

  26. Navimipour NJ, Rahmani AM, Navin AH, Hosseinzadeh M (2014) Resource discovery mechanisms in grid systems: a survey. J Netw Comput Appl 41:389–410

    Google Scholar 

  27. Samuvelraj G, Nalini N (2014) A survey of self organizing trust method to avoid malicious peers from peer to peer network. In: 2014 international conference on green computing communication and electrical engineering (ICGCCEE). IEEE, pp 1–4

  28. Felber P, Kropf P, Schiller E, Serbu S (2013) Survey on load balancing in peer-to-peer distributed hash tables. IEEE Commun Surv Tutor 16(1):473–492

    Google Scholar 

  29. Xin-Xin Z, Zhen-Wan Z, Peng K, Ren-Jie S (2015) A survey of resource discovery in mobile peer-to-peer networks. In: 2015 fifth international conference on communication systems and network technologies. IEEE, pp 122–125

  30. BitTorrent. https://www.bittorrent.com/ (2019). Accessed on 10 Oct 2019

  31. Pouwelse JA, Garbacki P, Wang J, Bakker A, Yang J, Iosup A, Epema DHJ, Reinders M, Van Steen MR, Sips HJ (2008) TRIBLER: a social-based peer-to-peer system. Concurr Comput Pract Exp 20(2):127–138

    Google Scholar 

  32. Ripeanu M (2001) Peer-to-peer architecture case study: Gnutella network. In: Proceedings first international conference on peer-to-peer computing. IEEE, pp 99–100

  33. Kulbak Y, Bickson D (2005) The eMule protocol specification. eMule project. http://sourceforge.net

  34. VUZE. https://www.vuze.com/ (2020). Online; Accessed 05 April 2020

  35. eDonkey2000. http://www.edonkey2000.com/ (2019). Online; Accessed on 10 Oct 2019

  36. Napster. http://www.napster.com/ (2019). Online; Accessed on 10 Oct 2019

  37. Sherwood R, Lee S, Bhattacharjee B (2006) Cooperative peer groups in NICE. Comput Netw 50(4):523–544

    MATH  Google Scholar 

  38. Tran DA, Hua KA, Do T (2003) Zigzag: an efficient peer-to-peer scheme for media streaming. In: IEEE INFOCOM 2003. Twenty-second annual joint conference of the IEEE computer and communications societies (IEEE cat. No. 03CH37428), vol 2. IEEE, pp 1283–1292

  39. Castro M, Druschel P, Kermarrec A-M, Nandi A, Rowstron A, Singh A (2003) Splitstream: High-bandwidth multicast in cooperative environments. ACM SIGOPS Oper Syst Rev 37(5):298–313

    Google Scholar 

  40. Liao X, Jin H, Liu Y, Ni LM, Deng D (2006) Anysee: Peer-to-peer live streaming. In: Proceedings IEEE INFOCOM 2006. In: 25TH IEEE international conference on computer communications. IEEE, pp 1–10

  41. Magharei N, Rejaie R (2009) Prime: Peer-to-peer receiver-driven mesh-based streaming. IEEE/ACM Trans Netw 17(4):1052–1065

    Google Scholar 

  42. Byun H, Lee M (2009) Hypo: a peer-to-peer based hybrid overlay structure. In: 2009 11th international conference on advanced communication technology, vol 1. IEEE, pp 840–844

  43. PPTV. http://www.pplive.com (2020). Online; Accessed 05 April 2020

  44. iQIYI. https://www.iq.com/ (2020). Online; Accessed 05 April 2020

  45. Hotstar. https://www.hotstar.com/in (2020). Online; Accessed 05 April 2020

  46. Funshion. http://www.fun.tv/ (2020). Online; Accessed 05 April 2020

  47. Gill P, Arlitt M, Li Z, Mahanti A (2007) Youtube traffic characterization: a view from the edge. In: Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, pp 15–28

  48. Youtube. https://www.youtube.com (2020). Online; Accessed 03 April 2020

  49. YuppTV. https://www.yupptv.com/ (2020). Online; Accessed 05 April 2020

  50. Biryukov A, Khovratovich D, Pustogarov I (2014) Deanonymisation of clients in Bitcoin P2P network. In: Proceedings of the 2014 ACM SIGSAC conference on computer and communications security, pp 15–29

  51. King S, Nadal S (2012) Ppcoin: Peer-to-peer crypto-currency with proof-of-stake. self-published paper, August 19, no. 1

  52. Jami. https://jami.net/ (2020). Online; Accessed 05 April 2020

  53. Heckmann O, Bock A (2002) The edonkey 2000 protocol. Rapport technique, Multimedia Communications Lab, Darmstadt University of Technology 13

  54. Liang J, Kumar R, Ross KW (2006) The FastTrack overlay: a measurement study. Comput Netw 50(6):842–858

    Google Scholar 

  55. Liang J, Kumar R, Ross KW (2005) The Kazaa overlay: a measurement study. Comput Netw J 49(6)

  56. DC++. https://dcplusplus.sourceforge.io/ (2019). Online; Accessed on 10 Oct 2019

  57. Application Usage and Threat Report. Palo Alto Networks. https://blog.paloaltonetworks.com/app-usage-risk-report-visualization/. Retrieved on 30 April 2020

  58. OpenNap. http://opennap.sourceforge.net/ (2019). Online; Accessed on 10 Oct 2019

  59. Cascarano N, Ciminiera L, Risso F (2010) Improving cost and accuracy of DPI traffic classifiers. In: Proceedings of the 2010 ACM symposium on applied computing, pp 641–646

  60. Zhang Q, Ma Y, Zhang P, Wang J, Li X (2014) Netflow Based P2P detection in UDP traffic. In: Fifth international conference on intelligent control and information processing. IEEE, pp 250–254

  61. He J, Yang Y, Qiao Y, Deng W (2015) Fine-grained P2P traffic classification by simply counting flows. Front Inf Technol Electron Eng 16(5):391–403

    Google Scholar 

  62. Qin T, Wang L, Zhao D, Zhu M (2016) CUFTI: methods for core users finding and traffic identification in P2P systems. Peer-to-Peer Netw Appl 9(2):424–435

    Google Scholar 

  63. Mohammadi M, Raahemi B, Akbari A, Moeinzadeh H, Nasersharif B (2011) Genetic-based minimum classification error mapping for accurate identifying Peer-to-Peer applications in the internet traffic. Expert Syst Appl 38(6):6417–6423

    Google Scholar 

  64. Jiang D, Tao L (2013) P2P traffic identification research based on the SVM. In: 2013 22nd wireless and optical communication conference. IEEE, pp 683–686

  65. Du Y, Zhang R (2013) Design of a method for encrypted P2P traffic identification using K-means algorithm. Telecommun Syst 53(1):163–168

    Google Scholar 

  66. Liu S-M, Sun Z-X (2015) Active learning for P2P traffic identification. Peer-to-Peer Netw Appl 8(5):733–740

    Google Scholar 

  67. Gong J, Wang W, Wang P, Sun Z (2014) P2P traffic identification method based on an improvement incremental SVM learning algorithm. In: 2014 international symposium on wireless personal multimedia communications (WPMC). IEEE, pp 174–179

  68. Deng S, Luo J, Liu Y, Wang X, Yang J (2014) Ensemble learning model for P2P traffic identification. In: 2014 11th international conference on fuzzy systems and knowledge discovery (FSKD). IEEE, pp 436–440

  69. Korczynski M, Duda A (2014) Markov chain fingerprinting to classify encrypted traffic. In: IEEE INFOCOM 2014-IEEE conference on computer communications. IEEE, pp 781–789

  70. Kumano Y, Ata S, Nakamura N, Nakahira Y, Oka I (2014) Towards real-time processing for application identification of encrypted traffic. In: 2014 international conference on computing, networking and communications (ICNC). IEEE, pp 136–140

  71. Wang D, Zhang L, Yuan Z, Xue Y, Dong Y (2014) Characterizing application behaviors for classifying p2p traffic. In: 2014 international conference on computing, networking and communications (ICNC). IEEE, pp 21–25

  72. Alshammari R, Zincir-Heywood AN (2015) Identification of VoIP encrypted traffic using a machine learning approach. J King Saud Univ Comput Inf Sci 27(1):77–92

    Google Scholar 

  73. Bozdogan C, Gokcen Y, Zincir I (2015) A preliminary investigation on the identification of peer to peer network applications. In: Proceedings of the companion publication of the 2015 annual conference on genetic and evolutionary computation, pp 883–888

  74. Khan RU, Kumar R, Alazab M, Zhang X (2019) A hybrid technique to detect botnets, based on P2P traffic similarity. In: 2019 cybersecurity and cyberforensics conference (CCC). IEEE, pp 136–142

  75. Jamil HA (2019) Feature selection and machine learning classification for live P2P traffic

  76. Zhu Y, Zheng Y (2019) Traffic identification and traffic analysis based on support vector machine. Neural Comput Appl 1–9

  77. Lotfollahi M, Siavoshani MJ, Zade RSH, Saberian M (2020) Deep packet: a novel approach for encrypted traffic classification using deep learning. Soft Comput 24(3):1999–2012

    Google Scholar 

  78. Cao J, Wang D, Zhaoyang Q, Sun H, Li B, Chen C-L (2020) An improved network traffic classification model based on a support vector machine. Symmetry 12(2):301

    Google Scholar 

  79. Bashir A, Huang C, Nandy B, Seddigh N (2013) Classifying P2P activity in Netflow records: a case study on BitTorrent. In: 2013 IEEE international conference on communications (ICC). IEEE, pp 3018–3023

  80. Reddy JM, Hota C (2015) Heuristic-based real-time p2p traffic identification. In: 2015 international conference on emerging information technology and engineering solutions. IEEE, pp 38–43

  81. Bassi JS, Ru LH, Ismail I, Khammas BM, Marsono MN (2016) Online peer-to-peer traffic identification based on complex events processing of traffic event signatures. Jurnal Teknologi 78:7

    Google Scholar 

  82. Yang Z, Li L, Ji Q, Zhu Y (2012) Cocktail method for BitTorrent traffic identification in real time. JCP 7(1):85–95

    Google Scholar 

  83. Sun M-F, Chen J-T (2011) Research of the traffic characteristics for the real time online traffic classification. J China Univ Posts Telecommun 18(3):92–98

    MathSciNet  Google Scholar 

  84. Jie H, Yuexiang Y, Yong Q, Chuan T (2013) Accurate classification of P2P traffic by clustering flows. China Commun 10(11):42–51

    Google Scholar 

  85. Datta J, Kataria N, Hubballi N (2015) Network traffic classification in encrypted environment: a case study of google hangout. In: 2015 twenty first national conference on communications (NCC). IEEE, pp 1–6

  86. Yan J, Wu Z, Luo H, Zhang S (2013) P2P traffic identification based on host and flow behaviour characteristics. Cybern Inf Technol 13(3):64–76

    Google Scholar 

  87. Ye W, Cho K (2014) Hybrid P2P traffic classification with heuristic rules and machine learning. Soft Comput 18(9):1815–1827

    Google Scholar 

  88. Ye W, Cho K (2017) P2P and P2P botnet traffic classification in two stages. Soft Comput 21(5):1315–1326

    Google Scholar 

  89. Sen S, Spatscheck O, Wang D (2004) Accurate, scalable in-network identification of p2p traffic using application signatures. In: Proceedings of the 13th international conference on World Wide Web, pp 512–521

  90. Madhukar A, Williamson C (2006) A longitudinal study of P2P traffic classification. In: 14th IEEE international symposium on modeling, analysis, and simulation. IEEE, pp 179–188

  91. Perenyi M, Dang TD, Gefferth A, Molnár S (2006) Identification and analysis of peer-to-peer traffic. J Commun 1(7):36–46

    Google Scholar 

  92. Crotti M, Dusi M, Gringoli F, Salgarelli L (2007) Traffic classification through simple statistical fingerprinting. ACM SIGCOMM Comput Commun Rev 37(1):5–16

    Google Scholar 

  93. Crotti M, Gringoli F, Pelosato P, Salgarelli L (2006) A statistical approach to IP-level classification of network traffic. In: 2006 IEEE international conference on communications, vol 1. IEEE, pp 170–176

  94. Gomes JVP, Inácio PRM, Freire MM, Pereira M, Monteiro PP (2008) Analysis of peer-to-peer traffic using a behavioural method based on entropy. In: 2008 IEEE international performance, computing and communications conference. IEEE, pp 201–208

  95. Hurley J, Garcia-Palacios E, Sezer S (2009) Classification of P2P and HTTP using specific protocol characteristics. In: Meeting of the European network of universities and companies in information and communication engineering. Springer, Berlin, pp 31–40

  96. Branch PA, Heyde A, Armitage GJ (2009) Rapid identification of Skype traffic flows. In: Proceedings of the 18th international workshop on Network and operating systems support for digital audio and video, pp 91–96

  97. Liu H, Lei Yu (2005) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17(4):491–502

    Google Scholar 

  98. Fahad A, Tari Z, Khalil I, Habib I, Alnuweiri H (2013) Toward an efficient and scalable feature selection approach for internet traffic classification. Comput Netw 57(9):2040–2057

    Google Scholar 

  99. Dhote Y, Shikha A, Anjana JD (2015) A survey on feature selection techniques for internet traffic classification. In: 2015 international conference on computational intelligence and communication networks (CICN). IEEE, pp 1375–1380

  100. Dash M, Liu H (1997) Feature selection for classification. Intell Data Anal 1(3):131–156

    Google Scholar 

  101. Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1–2):273–324

    MATH  Google Scholar 

  102. Su C-T, Hsu J-H (2005) An extended chi2 algorithm for discretization of real value attributes. IEEE Trans Knowl Data Eng 17(3):437–441

    Google Scholar 

  103. Abdalla BMA, Jamil HA, Hamdan M, Bassi JS, Ismail I, Marsono MN (2017) Multi-stage feature selection for on-line flow peer-to-peer traffic identification. In: Asian simulation conference. Springer, Singapore, pp 509–523

  104. Liu H, Setiono R (1995) Chi2: Feature selection and discretization of numeric attributes. In: Proceedings of 7th IEEE international conference on tools with artificial intelligence. IEEE, pp 388–391

  105. Jolliffe IT (1986) Principal components in regression analysis. In: Principal component analysis. Springer, New York, pp 129–155

  106. Principal Component Analysis. https://www.edureka.co/blog/principal-component-analysis/ (2020). Online; Accessed on 08 March 2020

  107. Frohlich H, Chapelle O, Scholkopf B (2003) Feature selection for support vector machines by means of genetic algorithm. In: Proceedings. 15th IEEE international conference on tools with artificial intelligence. IEEE, pp 142–148

  108. Deshpande A, Kumar M (2018) Artificial intelligence for big data: complete guide to automating big data solutions using artificial intelligence techniques. Packt Publishing Ltd

  109. Juang C-F (2004) A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans Syst Man Cybernet Part B (Cybernet) 34(2):997–1006

    Google Scholar 

  110. Oreski S, Oreski G (2014) Genetic algorithm-based heuristic for feature selection in credit risk assessment. Expert Syst Appl 41(4):2052–2064

    Google Scholar 

  111. Sanz H, Valim C, Vegas E, Oller JM, Reverter F (2018) SVM-RFE: selection and visualization of the most relevant features through non-linear kernels. BMC Bioinform 19(1):1–18

    Google Scholar 

  112. Animesh A, Ament Z, West JA, Stanley E, Griffin JL (2016) Distributed online flash-crowd detection in P2P swarming systems. BMC Bioinform 17(15):37–49

    Google Scholar 

  113. Unler A, Murat A (2010) A discrete particle swarm optimization method for feature selection in binary classification problems. Eur J Oper Res 206(3):528–539

    MATH  Google Scholar 

  114. Liu Y, Wang G, Chen H, Dong H, Zhu X, Wang S (2011) An improved particle swarm optimization for feature selection. J Bionic Eng 8(2):191–200

    Google Scholar 

  115. Mohemmed AW, Zhang M, Johnston M (2009) Particle swarm optimization based adaboost for face detection. In: 2009 IEEE congress on evolutionary computation. IEEE, pp 2494–2501

  116. Xue B, Zhang M, Browne WN (2012) Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans Cybernet 43(6):1656–1671

    Google Scholar 

  117. Zhang G (2011) Quantum-inspired evolutionary algorithms: a survey and empirical study. J Heurist 17(3):303–351

    MATH  Google Scholar 

  118. Biswas T, Kuila P, Ray AK, Sarkar M (2019) Gravitational search algorithm based novel workflow scheduling for heterogeneous computing systems. Simul Model Pract Theory 96:101932

    Google Scholar 

  119. Taradeh M, Mafarja M, Heidari AA, Faris H, Aljarah I, Mirjalili S, Fujita H (2019) An evolutionary gravitational search-based feature selection. Inf Sci 497:219–239

    Google Scholar 

  120. Zimmermann H-J (2012) Fuzzy sets, decision making, and expert systems, vol 10. Springer, Berlin

    Google Scholar 

  121. Zadeh Lotfi A (1965) Fuzzy sets. Inf Control 8(3):338–353

    MATH  Google Scholar 

  122. Raja S, Ramaiah S (2017) An efficient fuzzy-based hybrid system to cloud intrusion detection. Int J Fuzzy Syst 19(1):62–77

    Google Scholar 

  123. Liu D, Lung C-H (2011) P2P traffic identification and optimization using fuzzy c-means clustering. In: 2011 IEEE international conference on fuzzy systems (FUZZ-IEEE 2011). IEEE, pp 2245–2252

  124. Ansari MdSA, Pal K, Govil P, Govil MC (2023) A fuzzy based hierarchical flash crowd controller for live video streaming in P2P network. Peer-to-Peer Netw Appl 1–22

  125. Chakraborty B (2008) Feature subset selection by particle swarm optimization with fuzzy fitness function. In: 2008 3rd international conference on intelligent system and knowledge engineering, vol 1. IEEE, pp 1038–1042

  126. Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106

    Google Scholar 

  127. Hall MA, Smith LA (1998) Practical feature subset selection for machine learning, pp 181–191

  128. Yang Y, Pedersen JO (1997) A comparative study on feature selection in text categorization. ICML 97(412–420):35

    Google Scholar 

  129. Singh A, Thakur N, Sharma A (2016) A review of supervised machine learning algorithms. In: 2016 3rd international conference on computing for sustainable global development (INDIACom). IEEE

  130. Caruana R, Niculescu-Mizil A (2006) An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd international conference on machine learning

  131. Catak F, Bilgem T (2015) Genetic algorithm based feature selection in high dimensional text dataset classification. WSEAS Trans Inf Sci Appl 12(28):290–296

    Google Scholar 

  132. Svetnik V et al (2003) Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 43(6):1947–1958

    Google Scholar 

  133. Ma Z, Kaban A (2013) K-Nearest-Neighbours with a novel similarity measure for intrusion detection. In: 2013 13th UK workshop on computational intelligence (UKCI). IEEE

  134. Leung KM (2007) Naive bayesian classifier. Polytechnic University Department of Computer Science/Finance and Risk Engineering, pp 123–156

  135. KDD99, KDD Cup 1999 Data (1999) Online: Accessed 26 July 2020. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html

  136. Moustafa N, Slay J (2015) UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: 2015 military communications and information systems conference (MilCIS). IEEE, pp 1–6

  137. Moustafa N, Slay J (2016) The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set. Inf Secur J A Global Perspect 25(1–3):18–31

    Google Scholar 

  138. Garcia S, Grill M, Stiborek J, Zunino A (2014) An empirical comparison of botnet detection methods. Comput Secur 45:100–123

    Google Scholar 

  139. Gringoli F, Salgarelli L, Dusi M, Cascarano N, Risso F, Claffy KC (2009) GT: picking up the truth from the ground for internet traffic. ACM SIGCOMM Comput Commun Rev 39(5):12–18

    Google Scholar 

  140. Carela-Español V, Bujlow T, Barlet-Ros P (2014) Is our ground-truth for traffic classification reliable?. In: International conference on passive and active network measurement. Springer, Cham, pp 98–108

  141. Moore A, Zuev D, Crogan M (2013) Discriminators for use in flow-based classification

  142. Zhang H, Gang L, Qassrawi MT, Zhang Yu, Xiangzhan Yu (2012) Feature selection for optimizing traffic classification. Comput Commun 35(12):1457–1471

    Google Scholar 

  143. CSE-CIC-IDS2018 dataset. Online: Accessed 26 July 2020. https://www.unb.ca/cic/datasets/ids-2018.html

  144. Ansari Md, Alam S, Pal K, Govil P, Govil MC, Awasthi LK (2022) A statistical analysis of SAMPARK dataset for peer-to-peer traffic and selfish-peer identification. Multimedia Tools Appl 1–29

  145. Mahoney MV, Chan PK (2003) An analysis of the 1999 DARPA/Lincoln Laboratory evaluation data for network anomaly detection. In: International workshop on recent advances in intrusion detection. Springer, Berlin, pp 220–237

  146. Frank E, Hall M, Holmes G, Kirkby R, Pfahringer B, Witten IH, Trigg L (2009) Weka-a machine learning workbench for data mining. In: Data mining and knowledge discovery handbook. Springer, Boston, pp 1269–1277

  147. Weka 3: Data mining software in java. Online Accessed 25 July 25 2020. https://www.cs.waikato.ac.nz/~ml/weka/

  148. Fu W, Fu W (2019) Accelerator control data mining with WEKA. No. BNL-212159-2019-COPA. Brookhaven National Lab.(BNL), Upton, NY (United States)

  149. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  150. Kramer O (2016) Scikit-learn. In: Machine learning for evolution strategies. Springer, Cham, pp 45–53

  151. Nelli F (2018) Machine learning with scikit-learn. In: Python data analytics. Apress, Berkeley, pp 313–347

  152. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M et al (2016) Tensorflow: a system for large-scale machine learning. In: 12th USENIX symposium on operating systems design and implementation (OSDI 16), pp 265–283

  153. Laghari AA, He H, Khan A, Kumar N, Kharel R (2018) Quality of experience framework for cloud computing (QoC). IEEE Access 6:64876–64890

    Google Scholar 

  154. Laghari AA, Jumani AK, Laghari RA (2021) Review and state of art of fog computing. Arch Comput Methods Eng 1–13

  155. Laghari AA, Wu K, Laghari RA, Ali M, Khan AA (2021) A review and state of art of Internet of Things (IoT). Arch Comput Methods Eng 1–19

  156. Mishra P, Varadharajan V, Tupakula U, Pilli ES (2018) A detailed investigation and analysis of using machine learning techniques for intrusion detection. IEEE Commun Surv Tutor 21(1):686–728

    Google Scholar 

  157. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS et al (2016) Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467

  158. Introducing TensorFlow Graphics: Computer Graphics Meets Deep Learning. Online: Accessed 25 July 2020. https://medium.com/tensorflow/introducing-tensorflow-graphics-computer-graphics-meets-deep-learning-c8e3877b7668

  159. Mikut R, Reischl M (2011) Data mining tools. Wiley Interdiscip Rev Data Min Knowl Discov 1(5):431–443

    Google Scholar 

  160. Bartschat A, Reischl M, Mikut R (2019) Data mining tools. Wiley Interdiscip Rev Data Min Knowl Discov 9(4):e1309

    Google Scholar 

  161. Sperotto A, Sadre R, Van Vliet F, Pras A (2009) A labeled data set for flow-based intrusion detection. In: International workshop on IP operations and management. Springer, Berlin, pp 39–50

  162. Olson DL, Delen D (2008) Advanced data mining techniques. Springer, Berlin

    MATH  Google Scholar 

  163. Nguyen Thuy TT, Armitage G (2008) A survey of techniques for internet traffic classification using machine learning. IEEE Commun Surv Tutor 10(4):56–76

    Google Scholar 

  164. Raahemi B, Zhong W, Liu J (2008) Peer-to-peer traffic identification by mining IP layer data streams using concept-adapting very fast decision tree. In: 2008 20th IEEE international conference on tools with artificial intelligence, vol 1. IEEE, pp 525–532

  165. Wang Y (ed) (2008) Statistical techniques for network security: modern statistically-based intrusion detection and protection: modern statistically-based intrusion detection and protection. Igi Global

  166. Karagiannis T, Papagiannaki K, Faloutsos M (2005) BLINC: multilevel traffic classification in the dark. In: Proceedings of the 2005 conference on applications, technologies, architectures, and protocols for computer communications, pp 229–240

  167. Chakraborty B (2002) Genetic algorithm with fuzzy fitness function for feature selection. In: IEEE international symposium on industrial electronics (ISIE’02), vol 1, pp 315–319

  168. Wang X, Yang J, Teng X, Xia W, Jensen R (2007) Feature selection based on rough sets and particle swarm optimization. Pattern Recogn Lett 28(4):459–471

    Google Scholar 

  169. Azevedo GLFBG, Cavalcanti GDC, Filho ECBC (2007) An approach to feature selection for keystroke dynamics systems based on PSO and feature weighting. In: 2007 IEEE congress on evolutionary computation. IEEE, pp 3577–3584

  170. Lin S-W, Ying K-C, Chen S-C, Lee Z-J (2008) Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Syst Appl 35(4):1817–1824

    Google Scholar 

  171. Huang C-L, Dun J-F (2008) A distributed PSO-SVM hybrid system with feature selection and parameter optimization. Appl Soft Comput 8(4):1381–1391

    Google Scholar 

  172. Yang C-S, Chuang L-Y, Li J-C, Yang C-H (2008) Chaotic maps in binary particle swarm optimization for feature selection. In: 2008 IEEE conference on soft computing in industrial applications. IEEE, pp 107–112

  173. Chuang L-Y, Chang H-W, Chung-Jui T, Yang C-H (2008) Improved binary PSO for feature selection using gene expression data. Comput Biol Chem 32(1):29–38

    MATH  Google Scholar 

  174. Chuang L-Y, Tsai S-W, Yang C-H (2011) Improved binary particle swarm optimization using catfish effect for feature selection. Expert Syst Appl 38(10):12699–12707

    Google Scholar 

  175. Abdull HHN, Kasabov N, Shamsuddin SM (2011) Quantum-inspired particle swarm optimization for feature selection and parameter optimization in evolving spiking neural networks for classification tasks. InTech

  176. Wu Q, Ma Z, Fan J, Gang X, Shen Y (2019) A feature selection method based on hybrid improved binary quantum particle swarm optimization. IEEE Access 7:80588–80601

    Google Scholar 

  177. Draper-Gil G, Lashkari AH, Mamun MSI, Ghorbani AA (2016) Characterization of encrypted and vpn traffic using time-related. In: Proceedings of the 2nd international conference on information systems security and privacy (ICISSP), pp 407–414

  178. Saber A, Fergani B, Abbas M (2018) Encrypted traffic classification: combining over-and under-sampling through a PCA-SVM. In: 2018 3rd international conference on pattern analysis and intelligent systems (PAIS). IEEE, pp 1–5

  179. Ansari Md, Alam S, Pal K, Govil MC, Govil P, Srivastava A (2022) P2P traffic identification using machine learning and feature selection techniques. In: Edge analytics. Springer, Singapore, pp 393–407

  180. Ansari Md, Alam S, Pal K, Govil MC, Govil P, Srivastava A (2021) Ensemble machine learning for P2P traffic identification. Int J Comput Digital Syst

  181. Wiradinata T, Suryaputra PA (2016) Clustering and principal feature selection impact for internet traffic classification using K-NN. In: Proceedings of second international conference on electrical systems, technology and information 2015 (ICESTI 2015). Springer, Singapore, pp 75–81

  182. Bhattacharya S, Kaluri R, Singh S, Alazab M, Tariq U (2020) A Novel PCA-Firefly based XGBoost classification model for Intrusion Detection in Networks using GPU. Electronics 9(2):219

    Google Scholar 

  183. Haase P, Siebes R, Van Harmelen F (2004) Peer selection in peer-to-peer networks with semantic topologies. In International conference on semantics for the networked world. Springer, Berlin, pp 108–125

  184. Raja VR, Parag P, Shakkottai S (2019) Mode-suppression: a simple, stable and scalable chunk-sharing algorithm for P2P networks. arXiv preprint arXiv:1904.04191

  185. Xu D, Hefeeda M, Hambrusch S, Bhargava B (2002) On peer-to-peer media streaming. In: Proceedings 22nd international conference on distributed computing systems. IEEE, pp 363–371

  186. Shen H, Lin Y, Li J (2014) A social-network-aided efficient peer-to-peer live streaming system. IEEE/ACM Trans Netw 23(3):987–1000

    Google Scholar 

  187. Laghari AA, He H, Karim S, Shah HA, Karn NK (2017) Quality of experience assessment of video quality in social clouds. Wirel Commun Mobile Comput 2017

  188. Laghari AA, He H, Memon KA, Laghari RA, Halepoto IA, Khan A (2019) Quality of experience (QoE) in cloud gaming models: a review. Multiagent Grid Syst 15(3):289–304

    Google Scholar 

  189. Krishnan R, Smith MD, Tang Z, Telang R (2004) The impact of free-riding on peer-to-peer networks. In: 37th annual Hawaii international conference on system sciences. Proceedings of the, pp 10-pp. IEEE

  190. Ramaswamy L, Liu L (2003) Free riding: a new challenge to peer-to-peer file sharing systems. In: 36th annual Hawaii international conference on system sciences, 2003. Proceedings of the. IEEE, pp 10–pp

  191. Alotibi B, Alarifi N, Abdulghani M, Altoaimy L (2019) Overcoming free-riding behavior in peer-to-peer networks using points system approach. Procedia Comput Sci 151:1060–1065

    Google Scholar 

  192. Dutta D, Goel A, Govindan R, Zhang H (2003) The design of a distributed rating scheme for peer-to-peer systems. In: Workshop on economics of peer-to-peer systems, vol 264, pp 214–223

  193. Gupta R, Somani AK (2005) Game theory as a tool to strategize as well as predict nodes’ behavior in peer-to-peer networks. In: 11th international conference on parallel and distributed systems (ICPADS’05), vol 1. IEEE, pp 244–249

  194. Hughes D, Coulson G, Walkerdine J (2005) Free riding on Gnutella revisited: the bell tolls?. IEEE Distributed Syst 6(6)

  195. Ansari SA, Pal K, Govil MC, Ahmed M, Chawla T, Choudhary A (2021) Score-based Incentive Mechanism (SIM) for live multimedia streaming in peer-to-peer network. Multimedia Tools Appl 80(13):19263–19290

    Google Scholar 

  196. Zhang Q, Zhu L, Yang X (2018) A new incentive policy for improving data service in P2P networks. Wirel Pers Commun 103(1):231–245

    Google Scholar 

  197. Wu T-Y, Lee W-T, Guizani N, Wang T-M (2014) Incentive mechanism for P2P file sharing based on social network and game theory. J Netw Comput Appl 41:47–55

    Google Scholar 

  198. Zhou R, Hwang K (2007) Powertrust: a robust and scalable reputation system for trusted peer-to-peer computing. IEEE Trans Parallel Distrib Syst 18(4):460–473

    Google Scholar 

  199. Krishnan R et al (2004) The impact of free-riding on peer-to-peer networks. In: 37th annual Hawaii international conference on system sciences, 2004. Proceedings of the. IEEE

  200. Ramaswamy L, Liu L (2003) Free riding: a new challenge to peer-to-peer file sharing systems. In: 36th annual Hawaii international conference on system sciences, 2003. Proceedings of the. IEEE

  201. Samuvelraj G, Nalini N (2014) A survey of self organizing trust method to avoid malicious peers from peer to peer network. In: 2014 international conference on green computing communication and electrical engineering (ICGCCEE). IEEE, pp 1–4

  202. Zhao D, Traore I, Ghorbani A, Sayed B, Saad S, Lu W (2012) Peer to peer botnet detection based on flow intervals. In: IFIP international information security conference. Springer, Berlin, pp 87–102

  203. Beiknejad H, Vahdat-Nejad H, Moodi H (2018) P2P Botnet Detection Based on

  204. Rahbarinia B, Perdisci R, Lanzi A, Li K (2014) Peerrush: mining for unwanted p2p traffic. J Inf Secur Appl 19(3):194–208

    Google Scholar 

  205. Su S-C, Chen Y-R, Tsai S-C, Lin Y-B (2018) Detecting p2p botnet in software defined networks. Secur Commun Netw 2018

  206. Alauthman M, Aslam N, Al-Kasassbeh M, Khan S, Al-Qerem A, Choo K-KR (2020) An efficient reinforcement learning-based Botnet detection approach. J Netw Comput Appl 150:102479

    Google Scholar 

  207. Garg S, Singh AK, Sarje AK, Peddoju SK (2013) Behaviour analysis of machine learning algorithms for detecting P2P botnets. In: 2013 15th international conference on advanced computing technologies (ICACT). IEEE, pp 1–4

  208. Khan RU, Zhang X, Kumar R, Sharif A, Golilarz NA, Alazab M (2019) An adaptive multi-layer botnet detection technique using machine learning classifiers. Appl Sci 9(11):2375

    Google Scholar 

  209. Ali M, Jung LT, Sodhro AH, Laghari AA, Belhaouari SB, Gillani Z (2023) A confidentiality-based data classification-as-a-service (C2aaS) for cloud security. Alex Eng J 64:749–760

    Google Scholar 

  210. Karim S, He H, Laghari AA, Magsi AH, Laghari RA (2021) Quality of service (QoS): measurements of image formats in social cloud computing. Multimedia Tools Appl 80:4507–4532

    Google Scholar 

  211. Laghari AA, He H, Shafiq M, Khan A (2018) Assessment of quality of experience (QoE) of image compression in social cloud computing. Multiagent Grid Syst 14(2):125–143

    Google Scholar 

  212. Ansari MdSA, Chattopadhayay A, Das S (2010) A kernel level vfs logger for building efficient file system intrusion detection system. In: 2010 second international conference on computer and network technology. IEEE, pp 273–279

  213. Fujita S (2019) Flash crowd absorber for P2P video streaming. IEICE Trans Inf Syst 102(2):261–268

    Google Scholar 

  214. Li B, Keung GY, Xie S, Liu F, Sun Y, Yin H (2008) An empirical study of flash crowd dynamics in a p2p-based live video streaming system. In: IEEE GLOBECOM 2008-2008 IEEE global telecommunications conference. IEEE, pp 1–5

  215. Chung TY, Lin O (2011) A batch join scheme for flash crowd reduction in IPTV systems. In: 2011 IEEE 17th international conference on parallel and distributed systems. IEEE, pp 823–828

  216. Huang D, Zhang M, Zheng Y, Chen C, Huang Y (2015) Pre-allocation based flash crowd mitigation algorithm for large-scale content delivery system. Peer-to-Peer Netw Appl 8(3):493–500

    Google Scholar 

  217. Liu F, Li B, Zhong L, Li B, Jin H, Liao X (2011) Flash crowd in P2P live streaming systems: Fundamental characteristics and design implications. IEEE Trans Parallel Distrib Syst 23(7):1227–1239

    Google Scholar 

  218. de Paula Junior U, Drummond LMA, de Oliveira D, Frota Y, Barbosa VC (2015) Handling flash-crowd events to improve the performance of web applications. In Proceedings of the 30th annual ACM symposium on applied computing, pp 769–774

  219. Pruteanu A, D’Acunto L, Dulman S (2013) Distributed online flash-crowd detection in P2P swarming systems. Comput Commun 36(5):533–541

    Google Scholar 

  220. Laghari AA, He H, Khan A, Laghari RA, Yin S, Wang J (2022) Crowdsourcing platform for QoE evaluation for cloud multimedia services. Comput Sci Inf Syst 38

  221. Chen Y, Zhang B, Chen C(2011) Modeling and performance analysis of P2P live streaming systems under flash crowds. In: 2011 IEEE international conference on communications (ICC). IEEE

  222. Menth M, Lehrieder F (2012) Performance of PCN-based admission control under challenging conditions. IEEE/ACM Trans Netw 20(2):422–435

    Google Scholar 

  223. Wu H, Liu J, Jiang H, Sun Y, Li J, Li Z (2012) Bandwidth-aware peer selection for P2P live streaming systems under flash crowds. In: 2012 IEEE 31st international performance computing and communications conference (IPCCC). IEEE, pp 360–367

  224. Rückert J, Richerzhagen B, Lidanski E, Steinmetz R, Hausheer D (2015) Topt: supporting flash crowd events in hybrid overlay-based live streaming. In: 2015 IFIP networking conference (IFIP networking). IEEE, pp 1–9

  225. Wu H, Jiang H, Liu J, Sun Y, Li J, Li Z (2011) How P2P live streaming systems scale quickly under a flash crowd?. In: 30th IEEE international performance computing and communications conference. IEEE, pp 1–8

  226. Liu F et al (2011) Flash crowd in P2P live streaming systems: fundamental characteristics and design implications. IEEE Trans Parallel Distrib Syst 23(7):1227–1239

    Google Scholar 

  227. Miguel EC, Cunha I, Silva CM, Carvalho F, Campos SVA (2017) Resource-constrained P2P streaming overlay construction for efficient joining under flash crowds. In: 2017 IEEE symposium on computers and communications (ISCC). IEEE, pp 639–644

  228. Jia S, Jiang S, Li Y, Zhi X, Wang M (2015) A novel interest detection-based video dissemination algorithm under flash crowd in mobile ad hoc networks. Int J Distrib Sens Netw 11(6):239267

    Google Scholar 

  229. Fujita S (2019) Flash crowd absorber for P2P video streaming. IEICE Trans Inf Syst 102(2):261–268

    Google Scholar 

Download references

Funding

No financial funding was received for the research, authoring, and publication of this article.

Author information

Authors and Affiliations

  1. Department of Computer Science, National Institute of Technology Sikkim, Ravangla, Sikkim, 737 139, India

    Md. Sarfaraj Alam Ansari & Mahesh Chandra Govil

  2. Department of Computer Science, Dr. B R Ambedkar National Institute of Technology, Jalandhar, 144 011, India

    Kunwar Pal

Authors
  1. Md. Sarfaraj Alam Ansari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kunwar Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mahesh Chandra Govil
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors have made an equal contribution to transforming the manuscript into a significant research paper.

Corresponding author

Correspondence to Md. Sarfaraj Alam Ansari.

Ethics declarations

Conflict of interest

No conflicts of interest need to be revealed.

Ethics approval

The content presented here is entirely original and has not been published previously by the authors.

Consent to participate

It is confirmed that all authors have reviewed and consented to the submitted version of the paper, and they have agreed to be listed as co-authors.

Consent for publication

The author(s) hereby provide(s) permission for the Work to be published.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ansari, M.S.A., Pal, K. & Govil, M.C. Revisiting of peer-to-peer traffic: taxonomy, applications, identification techniques, new trends and challenges. Knowl Inf Syst 65, 4479–4536 (2023). https://doi.org/10.1007/s10115-023-01915-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-023-01915-5

Keywords

Search

Navigation