Timely and Continuous Machine-Learning-Based Classification for Interactive IP Traffic
Abstract
Machine Learning (ML) for classifying IP traffic has relied on the analysis of statistics of full flows or their first few packets only. However, automated QoS management for interactive traffic flows requires quick and timely classification well before the flows finish. Also, interactive flows are often long-lived and should be continuously monitored during their lifetime. We propose to achieve this by using statistics derived from sub-flows—a small number of most recent packets taken at any point in a flow's lifetime. Then, the ML classifier must be trained on a set of sub-flows, and we investigate different sub-flow selection strategies. We also propose to augment training datasets so that classification accuracy is maintained even when a classifier mixes up client-to-server and server-to-client directions for applications exhibiting asymmetric traffic characteristics. We demonstrate the effectiveness of our approach with the Naive Bayes and C4.5 Decision Tree ML algorithms, for the identification of first-person-shooter online game and VoIP traffic. Our results show that we can classify both applications with up to 99% Precision and 95% Recall within less than 1 s. Stable results are achieved regardless of where within a flow the classifier captures the packets and the traffic direction.
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The DIFFUSE technique lacks flexibility because its classifier can only recognize application classes that were known a priori.
“Despite the high accuracy, this technique lacks in flexibility, since the classifier can only recognize the application classes that were known a priori.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [337]
For sub-flow size N=25 packets, the C4.5 DT classifier achieved 99.3% median recall and 97% median precision for Enemy Territory traffic, and 95.7% median recall and 99.2% median precision for VoIP traffic.
“However, the C4.5 DT classifier performs better with 99.3% median recall and 97% median precision, and achieved 95.7% median recall and 99.2% median precision, for sub-flow size N = 25 packets for Enemy Territory and VoIP traffic, respectively.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [337]
For traffic classification using NetMate features, both supervised NB and C4.5 DT classifiers perform well when evaluated with missing flow start, missing directionality, or 5% independent random packet loss.
“Both classifiers perform well when evaluated with missing flow start, missing directionality, or 5% independent random packet loss.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [337]
DIFFUSE achieves a high accuracy of 98-99% for Enemy Territory and VoIP traffic replayed across the network while monitoring one or more 1 Gb/s links.
“DIFFUSE achieves a high accuracy of 98-99% for Enemy Territory (online game) and VoIP traffic replayed across the network, while monitoring one or more 1 Gb/s links.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [337]
Erman et al. [137] propose a semi-supervised TCP traffic classification technique that partially overcomes the limitation of a priori knowledge of application class or protocol.
“[137] propose a semi-supervised TCP traffic classification technique that partially overcomes a limitation of [55] and [337]-a priori knowledge of application class or protocol.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [337]
Sub-flows can be extended synthetically or derived from a small sliding window over each flow.
“These sub-flows can be extended synthetically [55] or derived from a small sliding window over each flow [337].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [337]
Cites
Witten, I. H. (2008) · Cited by 9,115 · see citing works
Estimating Continuous Distributions in Bayesian ClassifiersPreprint
John, G. H., & Pat Langley, B. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1302.4964 · Cited by 2,914 · see citing works
A survey of techniques for internet traffic classification using machine learning
Nguyen, T. T. T., & Armitage, G. (2008) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2008.080406 · Cited by 1,664 · see citing works
Self-citationThe macroscopic behavior of the TCP congestion avoidance algorithm
Mathis, M., Semke, J., Mahdavi, J., & Ott, T. J. (1997) · ACM SIGCOMM Computer Communication Review · doi:10.1145/263932.264023 · Cited by 1,496 · see citing works
Internet traffic classification using bayesian analysis techniques
Moore, A. W., & Zuev, D. V. (2005) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/1064212.1064220 · Cited by 1,216 · see citing works
End-to-end packet delay and loss behavior in the internet
Bolot, J.-C. (1993) · doi:10.1145/166237.166265 · Cited by 729 · see citing works
Traffic classification using clustering algorithms
Erman, J., Arlitt, M. F., & Mahanti, A. (2006) · doi:10.1145/1162678.1162679 · Cited by 712 · see citing works
Williams, N., Zander, S., & Armitage, G. J. (2006) · ACM SIGCOMM Computer Communication Review · doi:10.1145/1163593.1163596 · Cited by 698 · see citing works
Self-citationRahbar, A. G., & Yang, O. W. W. (2008) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2008.080408 · Cited by 668 · see citing works
Traffic classification on the fly
Bernaille, L., Teixeira, R., Akodkenou, I., Soule, A., & Salamatian, K. (2006) · ACM SIGCOMM Computer Communication Review · doi:10.1145/1129582.1129589 · Cited by 585 · see citing works
Flow Clustering Using Machine Learning Techniques
McGregor, A., Hall, M., Lorier, P., & Brunskill, J. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24668-8_21 · Cited by 521 · see citing works
Automated traffic classification and application identification using machine learning
Zander, S., Nguyen, T., & Armitage, G. (2005) · The IEEE Conference on Local Computer Networks 30th Anniversary (LCN'05)l · doi:10.1109/lcn.2005.35 · Cited by 517 · see citing works
Self-citationBayesian Neural Networks for Internet Traffic Classification
Auld, T., Moore, A. W., & Gull, S. F. (2007) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.883010 · Cited by 494 · see citing works
Roughan, M., Sen, S., Spatscheck, O., & Duffield, N. (2004) · Proceedings of the 4th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1028788.1028805 · Cited by 494 · see citing works
Internet traffic classification demystified: myths, caveats, and the best practices
Kim, H., Claffy, K., Fomenkov, M., Barman, D., Faloutsos, M., & Lee, K. (2008) · Proceedings of the 2008 ACM CoNEXT Conference on - CONEXT '08 · doi:10.1145/1544012.1544023 · Cited by 485 · see citing works
A Survey on Internet Traffic Identification
Callado, A. C., Kamienski, C., Szabó, G., Gero, B. P., Kelner, J., Fernandes, S., & Sadok, D. (2009) · IEEE Communications Surveys and Tutorials · doi:10.1109/surv.2009.090304 · Cited by 421 · see citing works
Haffner, P., Sen, S. B., Spatscheck, O., & Wang, D. (2005) · doi:10.1145/1080173.1080183 · Cited by 376 · see citing works
Characterizing residential broadband networks
Dischinger, M., Haeberlen, A., Gummadi, K. P., & Saroiu, S. (2007) · ACM Internet Measurement Conference (IMC) · doi:10.1145/1298306.1298313 · Cited by 341 · see citing works
Identifying and discriminating between web and peer-to-peer traffic in the network core
Erman, J., Mahanti, A., Arlitt, M., & Williamson, C. (2007) · Proceedings of the 16th international conference on World Wide Web · doi:10.1145/1242572.1242692 · Cited by 213 · see citing works
Efficient application identification and the temporal and spatial stability of classification schema
Li, W., Canini, M., Moore, A. W., & Bolla, R. (2008) · Computer Networks · doi:10.1016/j.comnet.2008.11.016 · Cited by 158 · see citing works
A Machine Learning Approach for Efficient Traffic Classification
Li, W., & Moore, A. W. (2007) · 2007 15th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems · doi:10.1109/mascots.2007.2 · Cited by 154 · see citing works
End-to-end packet delay and loss behavior in the internet
Bolot, J.-C. (1993) · ACM SIGCOMM Computer Communication Review · doi:10.1145/167954.166265 · Cited by 136 · see citing works
Thu, T. N. T., & Armitage, G. J. (2006) · Conference on Local Computer Networks · doi:10.1109/lcn.2006.322122 · Cited by 123 · see citing works
Self-citationQRP05-4: Internet Traffic Identification using Machine Learning
Erman, J., Mahanti, A., & Arlitt, M. F. (2006) · Globecom · doi:10.1109/glocom.2006.443 · Cited by 106 · see citing works
Jiang, W., & Schulzrinne, H. G. (2002) · International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV) · doi:10.1145/507670.507681 · Cited by 98 · see citing works
Instability of decision tree classification algorithms
Li, R.-H., & Belford, G. G. (2002) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/775047.775131 · Cited by 93 · see citing works
Semi-supervised network traffic classification
Erman, J., Mahanti, A., Arlitt, M. F., Cohen, I., & Williamson, C. L. (2007) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/1254882.1254934 · Cited by 93 · see citing works
Armitage, G., Claypool, M., & Branch, P. (2006) · Wiley eBooks · doi:10.1002/047003047x · Cited by 73 · see citing works
Self-citationUsing Unsupervised Learning to Guide Resampling in Imbalanced Data Sets
Nickerson, A., Japkowicz, N., & Milios, E. (2001) · Cited by 66 · see citing works
A nonlinear, recurrence-based approach to traffic classification
Palmieri, F., & Fiore, U. (2008) · Computer Networks · doi:10.1016/j.comnet.2008.12.015 · Cited by 61 · see citing works
Cisco Architecture for Lawful Intercept in IP Networks
Baker, F., Foster, B., & Sharp, C. (2004) · doi:10.17487/rfc3924 · Cited by 61 · see citing works
Semi-supervised network traffic classification
Erman, J., Mahanti, A., Arlitt, M. F., Cohen, I., & Williamson, C. L. (2007) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/1269899.1254934 · Cited by 52 · see citing works
Rapid identification of Skype traffic flows
Branch, P., Heyde, A. A., & Armitage, G. J. (2009) · International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV) · doi:10.1145/1542245.1542266 · Cited by 39 · see citing works
Self-citationGA-Based Internet Traffic Classification Technique for QoS Provisioning
Park, J. H., Tyan, H.-R., & Kuo, C. ‐. J. (2006) · Information Hiding · doi:10.1109/iih-msp.2006.103 · Cited by 32 · see citing works
Early traffic classification using support vector machines
Sena, G. G., & Belzarena, P. (2009) · doi:10.1145/1636682.1636693 · Cited by 31 · see citing works
Clustering to Assist Supervised Machine Learning for Real-Time IP Traffic Classification
Nguyen, T. T. T., & Armitage, G. (2008) · International Conference on Communications · doi:10.1109/icc.2008.1095 · Cited by 28 · see citing works
Self-citationGA-Based Internet Traffic Classification Technique for QoS Provisioning
Park, J., Tyan, H.-R., & Kuo, C. ‐. J. (2006) · doi:10.1109/iih-msp.2006.264991 · Cited by 28 · see citing works
An Architecture for Automated Network Control of QoS over Consumer Broadband Links
Stewart, L., Armitage, G. J., Branch, P., & Zander, S. (2005) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2005.301139 · Cited by 25 · see citing works
Self-citationRodríguez-Teja, F., Martinez-Cagnazzo, C., & Castro, E. G. (2010) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1145/1815396.1815572 · Cited by 20 · see citing works
Practical machine learning based multimedia traffic classification for distributed QoS management
Zander, S., & Armitage, G. J. (2011) · Conference on Local Computer Networks · doi:10.1109/lcn.2011.6115322 · Cited by 20 · see citing works
Self-citationSynthetic sub-flow pairs for timely and stable IP traffic identification
Nguyen, T. T. T., & Armitage, G. J. (2006) · Swinburne figshare (Swinburne University of Technology) · doi:10.25916/sut.26275438 · Cited by 18 · see citing works
Self-citationProbabilistic graphical models for semi-supervised traffic classification
Rotsos, C., Gael, J. V., Moore, A. W., & Zoubin Ghahramani, A. (2010) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1145/1815396.1815569 · Cited by 18 · see citing works
Outsourcing automated QoS control of home routers for a better online game experience
But, J., Armitage, G. J., & Stewart, L. (2008) · IEEE Communications Magazine · doi:10.1109/mcom.2008.4689209 · Cited by 16 · see citing works
Self-citationRapid Identification of BitTorrent traffic
But, J., Branch, P., & Le, T. (2010) · doi:10.1109/lcn.2010.5735770 · Cited by 13 · see citing works
Self-citationPerformance analysis of the ANGEL system for automated control of game traffic prioritisation
But, J., Thu, T. N. T., Stewart, L., Williams, N., & Armitage, G. J. (2007) · Annual Workshop on Network and Systems Support for Games (NetGames) · doi:10.1145/1326257.1326279 · Cited by 12 · see citing works
Self-citationAutomated network games enhancement layer
But, J., Williams, N., Zander, S., Stewart, L., & Armitage, G. J. (2006) · Annual Workshop on Network and Systems Support for Games (NetGames) · doi:10.1145/1230040.1230073 · Cited by 7 · see citing works
Self-citationOptimizing statistical classifiers of network traffic
Crotti, M., Gringoli, F., & Salgarelli, L. (2010) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1145/1815396.1815570 · Cited by 3 · see citing works
Design of DIFFUSE v0.1: dIstributed firewall and flow-shaper using statistical evidence
Zander, S., & Armitage, G. J. (2010) · Swinburne figshare (Swinburne University of Technology) · doi:10.25916/sut.26254961 · Cited by 2 · see citing works
Self-citationA two-level gain stochastic disturbance observer with hysteresis
Renna, T. G., Barkana, I., & Kalata, P. R. (2002) · IEEE International Conference on Systems Engineering · doi:10.1109/icsyse.1990.203102 · Cited by 2 · see citing works
Flagged first, then by citations
4 of the 53 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036 · see citing works
Robust Network Traffic Classification
Zhang, J., Chen, X., Xiang, Y., Zhou, W., & Wu, J. (2015) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2014.2320577 · Cited by 391 · see citing works
Shafiq, M., Tian, Z., Bashir, A. K., Jolfaei, A., & Yu, X. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102177 · Cited by 227 · see citing works
Bian, K., & Priyadarshi, R. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10110-w · Cited by 139 · see citing works
Protocol misidentification made easy with format-transforming encryption
Dyer, K. P., Coull, S. E., Ristenpart, T., & Shrimpton, T. (2013) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2508859.2516657 · Cited by 120 · see citing works
Data Fusion-Based Machine Learning Architecture for Intrusion Detection
Khan, M. A., Ghazal, T. M., Lee, S.-W., & Rehman, A. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.020173 · Cited by 114 · see citing works
Internet Traffic Classification Using Constrained Clustering
Wang, Y., Xiang, Y., Zhang, J., Zhou, W., Wei, G., & Yang, L. T. (2014) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2013.307 · Cited by 109 · see citing works
DRL-OR: Deep Reinforcement Learning-based Online Routing for Multi-type Service Requirements
Liu, C., Xu, M., Yang, Y., & Geng, N. (2021) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom42981.2021.9488736 · Cited by 100 · see citing works
Conti, M., Mancini, L. V., Spolaor, R., & Verde, N. V. (2015) · ACM Conference on Data and Application Security and Privacy (CODASPY) · doi:10.1145/2699026.2699119 · Cited by 98 · see citing works
Internet Traffic Classification Based on Incremental Support Vector Machines
Sun, G., Chen, T., Su, Y., & Li, C. (2018) · Mobile Networks and Applications · doi:10.1007/s11036-018-0999-x · Cited by 96 · see citing works
Network traffic classification based on transfer learning
Sun, G., Liang, L., Chen, T., Xiao, F., & Lang, F. (2018) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2018.03.005 · Cited by 94 · see citing works
In-Network Machine Learning Using Programmable Network Devices: A Survey
Zheng, C., Hong, X., Ding, D., Vargaftik, S., Ben-Itzhak, Y., & Zilberman, N. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3344351 · Cited by 90 · see citing works
Novel feature selection and classification of Internet video traffic based on a hierarchical scheme
Dong, Y., Zhao, J., & Jin, J. (2017) · Computer Networks · doi:10.1016/j.comnet.2017.03.019 · Cited by 78 · see citing works
Ensemble network traffic classification: Algorithm comparison and novel ensemble scheme proposal
Gómez, S. E., Carro, B., Sánchez-Esguevillas, A., & Hernández-Callejo, L. (2017) · Computer Networks · doi:10.1016/j.comnet.2017.07.018 · Cited by 65 · see citing works
Network traffic classification techniques and challenges
Khater, N. A., & Overill, R. E. (2015) · International Conference on Digital Information Management · doi:10.1109/icdim.2015.7381869 · Cited by 60 · see citing works
Perera, P., Tian, Y., Fidge, C., & Kelly, W. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70087-8_47 · Cited by 53 · see citing works
Effective packet number for early stage internet traffic identification
Peng, L., Yang, B., & Chen, Y. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.12.053 · Cited by 52 · see citing works
Quality of Service Oriented Access Point Selection Framework for Large Wi-Fi Networks
Raschellà, A., Bouhafs, F., Seyedebrahimi, M., Mackay, M., & Shi, Q. (2017) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2017.2678021 · Cited by 50 · see citing works
Effectiveness of Statistical Features for Early Stage Internet Traffic Identification
Peng, L., Yang, B., Chen, Y., & Chen, Z. (2015) · International Journal of Parallel Programming · doi:10.1007/s10766-014-0337-2 · Cited by 49 · see citing works
No NAT'd User Left Behind: Fingerprinting Users behind NAT from NetFlow Records Alone
Verde, N. V., Ateniese, G., Gabrielli, E., Mancini, L. V., & Spognardi, A. (2014) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2014.30 · Cited by 43 · see citing works
Scalable Deep Reinforcement Learning-Based Online Routing for Multi-Type Service Requirements
Liu, C., Wu, P., Xu, M., Yang, Y., & Geng, N. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3284651 · Cited by 41 · see citing works
Online multimedia traffic classification from the QoS perspective using deep learning
Wu, Z., Dong, Y., Qiu, X., & Jin, J. (2022) · Computer Networks · doi:10.1016/j.comnet.2021.108716 · Cited by 39 · see citing works
Li, F., Chung, J., & Claypool, M. K. (2018) · doi:10.1145/3210445.3210448 · Cited by 37 · see citing works
Internet traffic clustering with side information
Wang, Y., Xiang, Y., Zhang, J., Zhou, W., & Xie, B. (2014) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2014.02.008 · Cited by 37 · see citing works
VoIP Traffic Detection in Tunneled and Anonymous Networks Using Deep Learning
Islam, F. U., Liu, G., Zhai, J., & Liu, W. (2021) · IEEE Access · doi:10.1109/access.2021.3073967 · Cited by 35 · see citing works
Radio Frequency Traffic Classification Over WLAN
Kornycky, J., Abdul-Hameed, O., KONDOZ, A. M., & Barber, B. C. (2016) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2016.2562259 · Cited by 34 · see citing works
Why are My Flows Different? A Tutorial on Flow Exporters
Vormayr, G., Fabini, J., & Zseby, T. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2989695 · Cited by 34 · see citing works
Application traffic classification using payload size sequence signature
Shim, K., Ham, J., Sija, B. D., & Kim, M. (2017) · International Journal of Network Management · doi:10.1002/nem.1981 · Cited by 29 · see citing works
Flexible neural trees based early stage identification for IP traffic
Chen, Z., Peng, L., Gao, C., Yang, B., Chen, Y., & Li, J. (2015) · Soft Computing · doi:10.1007/s00500-015-1902-3 · Cited by 27 · see citing works
Li, Y., Liang, B., & Tizghadam, A. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3087268 · Cited by 25 · see citing works
Frame Aggregation in Central Controlled 802.11 WLANs: The Latency Versus Throughput Tradeoff
Saldaña, J., Ruíz-Mas, J., & Almodovar, J. (2017) · IEEE Communications Letters · doi:10.1109/lcomm.2017.2741940 · Cited by 24 · see citing works
Internet traffic classification using machine learning approach: Datasets validation issues
Ibrahim, H. A. H., Zuobi, O. R. A. L., Al-Namari, M. A., MohamedAli, G., & Abdalla, A. A. A. (2016) · Conference of Basic Sciences and Engineering Studies (SGCAC) · doi:10.1109/sgcac.2016.7458022 · Cited by 23 · see citing works
Noise-Resistant Statistical Traffic Classification
Wang, B., Zhang, J., Zhang, Z., Pan, L., Xiang, Y., & Xia, D. (2017) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2017.2735996 · Cited by 22 · see citing works
A Survey on Requirements of Future Intelligent Networks: Solutions and Future Research Directions
Husen, A., Chaudary, M. H., & Ahmad, F. (2022) · ACM Computing Surveys · doi:10.1145/3524106 · Cited by 22 · see citing works
A low complexity real-time Internet traffic flows neuro-fuzzy classifier
Rizzi, A., Iacovazzi, A., Baiocchi, A., & Colabrese, S. (2015) · Computer Networks · doi:10.1016/j.comnet.2015.09.011 · Cited by 19 · see citing works
BalancedBoost: A hybrid approach for real-time network traffic classification
Wei, H., Sun, B., & Jing, M. (2014) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn.2014.6911833 · Cited by 19 · see citing works
Low complexity, high performance neuro-fuzzy system for Internet traffic flows early classification
Rizzi, A., Colabrese, S., & Baiocchi, A. (2013) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc.2013.6583538 · Cited by 18 · see citing works
A dynamic access point allocation algorithm for dense wireless LANs using potential game
Raschellà, A., Bouhafs, F., Mackay, M., Shi, Q., Ortín, J., Gállego, J. R., & Canales, M. (2019) · Computer Networks · doi:10.1016/j.comnet.2019.106991 · Cited by 18 · see citing works
Effective Packet Number for 5G IM WeChat Application at Early Stage Traffic Classification
Shafiq, M., & Yu, X. (2017) · Mobile Information Systems · doi:10.1155/2017/3146868 · Cited by 18 · see citing works
Robust network traffic identification with unknown applications
Zhang, J., Chen, C., Xiang, Y., & Zhou, W. (2013) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/2484313.2484366 · Cited by 17 · see citing works
ADVoIP: Adversarial Detection of Encrypted and Concealed VoIP
Addesso, P., Cirillo, M., Mauro, M. D., & Matta, V. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2922398 · Cited by 17 · see citing works
Mobile network traffic pattern classification with incomplete a priori information
Jin, Z., Liang, Z., Wang, Y., & Meng, W. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.11.003 · Cited by 17 · see citing works
Liao, L. X., Chao, H., & Chen, M. (2020) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2020.07.008 · Cited by 16 · see citing works
Network traffic clustering using Random Forest proximities
Wang, Y., Xiang, Y., & Zhang, J. (2013) · International Conference on Communications · doi:10.1109/icc.2013.6654829 · Cited by 16 · see citing works
Scalable classification of QoS for real-time interactive applications from IP traffic measurements
Middleton, S. E., & Modafferi, S. (2016) · Computer Networks · doi:10.1016/j.comnet.2016.04.017 · Cited by 15 · see citing works
A centralized framework for smart access point selection based on the Fittingness Factor
Raschellà, A., Bouhafs, F., Seyedebrahimi, M., Mackay, M., & Shi, Q. (2016) · doi:10.1109/ict.2016.7500386 · Cited by 15 · see citing works
Explaining Class-of-Service Oriented Network Traffic Classification with Superfeatures
Chowdhury, S., Liang, B., & Tizghadam, A. (2019) · doi:10.1145/3359992.3366767 · Cited by 15 · see citing works
A Survey on SDN and SDCN Traffic Measurement: Existing Approaches and Research Challenges
Islam, M. S., Al-Mukhtar, M., Khan, M. R. K., & Hossain, M. (2023) · Eng—Advances in Engineering · doi:10.3390/eng4020063 · Cited by 16 · see citing works
Li, Y., Liang, B., & Tizghadam, A. (2021) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom42981.2021.9488890 · Cited by 14 · see citing works
A novel network traffic classification approach via discriminative feature learning
Zhao, L., Cai, L., Yu, A., Xu, Z. H., & Meng, D. (2020) · ACM Symposium on Applied Computing · doi:10.1145/3341105.3373844 · Cited by 14 · see citing works
Analysis of hidden node problem in LTE networks deployed in unlicensed spectrum
Campos, P., Hernández‐Solana, Á., & Valdovinos, A. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107280 · Cited by 13 · see citing works
A Feasible and Explainable Network Traffic Classifier Utilizing DistilBERT
Shin, C.-Y., Park, J.-T., Baek, U.-J., & Kim, M. (2023) · IEEE Access · doi:10.1109/access.2023.3293105 · Cited by 14 · see citing works
QoS aware radio access technology selection framework in heterogeneous networks using SDN
Raschellà, A., Bouhafs, F., Deepak, G. C., & Mackay, M. (2017) · Journal of Communications and Networks · doi:10.1109/jcn.2017.000098 · Cited by 13 · see citing works
Traffic classification for managing Applications’ networking profiles
Alizadeh, H., & Zúquete, A. (2016) · Security and Communication Networks · doi:10.1002/sec.1516 · Cited by 12 · see citing works
Internet traffic classification based on expanding vector of flow
Ding, L., Liu, J., Qin, T., & Li, H. (2017) · Computer Networks · doi:10.1016/j.comnet.2017.09.015 · Cited by 12 · see citing works
AP selection algorithm based on a potential game for large IEEE 802.11 WLANs
Raschellà, A., Bouhafs, F., Mackay, M., Shi, Q., Ortín, J., Gállego, J. R., & Canales, M. (2018) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms.2018.8406147 · Cited by 12 · see citing works
Fondo‐Ferreiro, P., Mhiri, S., López‐Bravo, C., González-Castaño, F. J., & Gil‐Castiñeira, F. (2019) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2019.2925506 · Cited by 12 · see citing works
Intelligent Wireless Networks: Challenges and Future Research Topics
Abusubaih, M. A. (2021) · Journal of Network and Systems Management · doi:10.1007/s10922-021-09625-5 · Cited by 12 · see citing works
Identifying VoIP traffic in VPN tunnel via Flow Spatio-Temporal Features
Islam, F. U., Liu, G., & Liu, W. (2020) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2020260 · Cited by 11 · see citing works
Feature Evaluation for Early Stage Internet Traffic Identification
Peng, L., Zhang, H., Yang, B., & Chen, Y. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11197-1_39 · Cited by 11 · see citing works
A centralised Wi-Fi management framework for D2D communications in dense Wi-Fi networks
Seyedebrahimi, M., Raschellà, A., Bouhafs, F., Mackay, M., Shi, Q., & Eiza, M. H. (2016) · IEEE Conference on Standards for Communications and Networking · doi:10.1109/cscn.2016.7785187 · Cited by 11 · see citing works
Wang, C., Guan, X., Qin, T., Wang, P., Tao, J., Meng, Y., & Liu, J. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106192 · Cited by 11 · see citing works
SoftSystem: Smart Edge Computing Device Selection Method for IoT Based on Soft Set Technique
Shafiq, M., Tian, Z., Bashir, A. K., Cengiz, K., & Tahir, A. (2020) · Wireless Communications and Mobile Computing · doi:10.1155/2020/8864301 · Cited by 10 · see citing works
On Addressing the Imbalance Problem: A Correlated KNN Approach for Network Traffic Classification
Wu, D., Chen, X., Chen, C., Zhang, J., Xiang, Y., & Zhou, W. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11698-3_11 · Cited by 10 · see citing works
Is the Uplink Enough? Estimating Video Stalls from Encrypted Network Traffic
Loh, F., Wamser, F., Moldovan, C., Zeidler, B., Tsilimantos, D., Valentin, S., & Hoßfeld, T. (2020) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms47738.2020.9110267 · Cited by 10 · see citing works
Optimization of low-efficiency traffic in OpenFlow Software Defined Networks
Saldaña, J., Pascual, F., de Hoz, D., Fernández-Navajas, J., Ruíz-Mas, J., Lopez, D. R., Florez, D., Castell, J. A., & Nunez, M. (2014) · International Symposium on Performance Evaluation of Computer and Telecommunication Systems · doi:10.1109/spects.2014.6879992 · Cited by 9 · see citing works
Singh, R., Srinivasan, M., & Murthy, C. S. R. (2015) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc.2015.7157993 · Cited by 9 · see citing works
Statistical network protocol identification with unknown pattern extraction
Wang, Y., Xue, H., Liu, Y., & Liu, W. (2019) · Annals of Telecommunications · doi:10.1007/s12243-019-00704-y · Cited by 9 · see citing works
Zero-Day Traffic Identification
Zhang, J., Chen, X., Xiang, Y., & Zhou, W. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-03584-0_16 · Cited by 9 · see citing works
Small-Packet Flows in Software Defined Networks: Traffic Profile Optimization
Saldaña, J., de Hoz, D., Fernández-Navajas, J., Ruíz-Mas, J., Pascual, F., Lopez, D. R., Florez, D., Castell, J. A., & Nuñez, M. (2015) · Journal of Networks · doi:10.4304/jnw.10.4.176-187 · Cited by 9 · see citing works
An efficient feature selection method for network video traffic classification
Dong, Y., Yue, Q., & Feng, M. (2017) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct.2017.8359902 · Cited by 9 · see citing works
On the classification of mobile broadband applications
Hsieh, I.-C., Tung, L., & Lin, B. P. (2016) · IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) · doi:10.1109/camad.2016.7790343 · Cited by 8 · see citing works
P2P traffic classification using clustering technology
Tseng, C.-M., Guo-Tai, H., & Liu, T.-J. (2016) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii.2016.7843994 · Cited by 8 · see citing works
Alcock, S., Möller, J.-P., & Nelson, R. (2016) · ACM Internet Measurement Conference (IMC) · doi:10.1145/2987443.2987447 · Cited by 8 · see citing works
NTARC: A Data Model for the Systematic Review of Network Traffic Analysis Research
Iglesias, F., Ferreira, D. C., Vormayr, G., Bachl, M., & Zseby, T. (2020) · Applied Sciences · doi:10.3390/app10124307 · Cited by 8 · see citing works
An, H.-M., Lee, S.-K., Ham, J.-H., & Kim, M. (2015) · Journal of information science and engineering · doi:10.6688/jise.2015.31.5.10 · Cited by 8 · see citing works
Skype Traffic Classification Using Cost Sensitive Algorithms
Azab, A., Layton, R., Alazab, M., & Watters, P. (2013) · Cybercrime and Trustworthy Computing Workshop (CTC) · doi:10.1109/ctc.2013.11 · Cited by 8 · see citing works
Sub-flow packet sampling for scalable ML classification of interactive traffic
Zander, S., Thu, T. N. T., & Armitage, G. J. (2012) · Conference on Local Computer Networks · doi:10.1109/lcn.2012.6423688 · Cited by 8 · see citing works
Self-citationMultimedia traffic classification with mixture of Markov components
Özkan, H., Temelli, R., Gürbüz, Ö., Koksal, O. K., Ipekoren, A. K., Canbal, F., Karahan, B. D., & Kuran, M. Ş. (2021) · Ad Hoc Networks · doi:10.1016/j.adhoc.2021.102608 · Cited by 7 · see citing works
Analysis of VBR coded VoIP for traffic classification
Choudhury, P., Kumar, K. R. P., Athithan, G., & Nandi, S. (2013) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2013.6637152 · Cited by 7 · see citing works
Machine Learning based Traffic Classification using Low Level Features and Statistical Analysis
Kumar, R., & Kaur, T. (2014) · International Journal of Computer Applications · doi:10.5120/18961-0290 · Cited by 7 · see citing works
Practical and configurable network traffic classification using probabilistic machine learning
Chen, J., Breen, J., Phillips, J. M., & der Merwe, J. V. (2021) · Cluster Computing · doi:10.1007/s10586-021-03393-2 · Cited by 7 · see citing works
Towards efficient real-time traffic classifier: A confidence measure with ensemble Deep Learning
Salman, O., Elhajj, I. H., Chehab, A., & Kayssi, A. I. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108684 · Cited by 6 · see citing works
Distributed and Collaborative Lightweight Edge Federated Learning for IoT Zombie Devices Detection
Han, C., Li, T., Chen, Q., Wu, Y., & Qin, J. (2024) · ACM Transactions on Sensor Networks · doi:10.1145/3691634 · Cited by 6 · see citing works
Robust Network Flow Classification against Malicious Feature Manipulation
Li, Y., Liang, B., & Tizghadam, A. (2020) · International Conference on Communications · doi:10.1109/icc40277.2020.9148644 · Cited by 6 · see citing works
Ibrahim, H. A. H., Zuobi, O. R. A. L., Abaker, A. M., & Alzghoul, M. B. (2021) · Applied Sciences · doi:10.3390/app112412113 · Cited by 6 · see citing works
Ibrahim, H. A. H., Nor, S. M., & Jamil, H. A. (2013) · IEEE International Conference on Control System, Computing and Engineering · doi:10.1109/iccsce.2013.6719956 · Cited by 6 · see citing works
Improving scheduling performance in congested networks
Husen, A., Chaudary, M. H., Ahmad, F., Alam, M. I., Sohail, A., & Asif, M. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.754 · Cited by 6 · see citing works
Can a Wi-Fi WLAN support a first person shooter?
Saldaña, J., de la Cruz, J. L., Sequeira, L., Fernández-Navajas, J., & Ruíz-Mas, J. (2015) · Annual Workshop on Network and Systems Support for Games (NetGames) · doi:10.1109/netgames.2015.7383001 · Cited by 6 · see citing works
Li, H., Kang, H., Liu, C., Wang, R., Li, J., Sun, G., Wang, J., Liang, S., & Mao, S. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3649259 · Cited by 7 · see citing works
A Trust-Based Cooperative System for Efficient Wi-Fi Radio Access Networks
Raschellà, A., Eiza, M. H., Mackay, M., Shi, Q., & Banton, M. (2023) · IEEE Access · doi:10.1109/access.2023.3338177 · Cited by 5 · see citing works
Traffic Identification in Semi-known Network Environment
Chen, X., Zhang, J., Xiang, Y., & Zhou, W. (2013) · IEEE International Conference on Computational Science and Engineering (CSE) · doi:10.1109/cse.2013.91 · Cited by 5 · see citing works
Early Stage Internet Traffic Identification Using Data Gravitation Based Classification
Peng, L., Zhang, H., Yang, B., Chen, Y., & Zhou, X. (2016) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc-picom-datacom-cyberscitec.2016.98 · Cited by 5 · see citing works
Unveiling the End-User Viewport Resolution From Encrypted Video Traces
Belmoukadam, O., & Barakat, C. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3083070 · Cited by 5 · see citing works
Classification of Abnormal Signaling SIP Dialogs Through Deep Learning
Pereira, D., Oliveira, R., & Kim, H. S. (2021) · IEEE Access · doi:10.1109/access.2021.3135195 · Cited by 5 · see citing works
Featuring Real-Time Imbalanced Network Traffic Classification
Amina, S. S. M., Bayati, A., Khoa, N. K., & Cheriet, M. (2018) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/cybermatics_2018.2018.00163 · Cited by 5 · see citing works
User traffic classification for proxy-server based internet access control
Sait, S. Y., Kumar, M. S., & Murthy, H. A. (2012) · International Conference on Signal Processing and Communication Systems · doi:10.1109/icspcs.2012.6507980 · Cited by 4 · see citing works
On the Effectiveness of Packet Sampling for Early Stage Traffic Identification
Peng, L., Zhang, H., Yang, B., Su, M., & Chen, Y. (2016) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/ithings-greencom-cpscom-smartdata.2016.111 · Cited by 4 · see citing works
Traffic Classification Based on Incremental Learning Method
Sun, G., Li, S., Chen, T., Su, Y., & Lang, F. (2018) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-73317-3_40 · Cited by 4 · see citing works
Traffic Pattern Plot: Video Identification in Encrypted Network Traffic
Kamal, A., Bukhari, S. M. A. H., Khan, M. U. S., Maqsood, T., & Fayyaz, M. A. B. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-7663-6_8 · Cited by 3 · see citing works
- Authors
- Nguyen, T. T. T., Armitage, G., Branch, P., & Zander, S.
- Affiliations
- Swinburne University of Technology (AU)
- Venue
- IEEE/ACM Transactions on Networking
- Published
- 2012-12
- Volume
- 20
- Issue
- 6
- Pages
- 1880-1894
- ISSN
- 1063-6692 (ISSN-L)
- 1558-2566
- OpenAlex
- W2031794885
- Cited by
- 156
- References
- 53
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 6 · 2023: 9 · 2022: 5 · 2021: 19 · 2020: 19 · 2019: 11 · 2018: 9 · 2017: 13 · 2016: 14 · 2015: 12 · 2014: 15 · 2013: 14 · 2012: 4
- FWCI
- 14.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE/ACM Transactions on Networking (publishedVersion)
- Murdoch University Research Portal (submittedVersion)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- asymmetric traffic, traffic direction, online games, VoIP traffic, first-person shooter games, QoS management, continuous flow monitoring, data augmentation, naive Bayes, C4.5 decision tree
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