The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Issues and future directions in traffic classification
Abstract
Traffic classification technology has increased in relevance this decade, as it is now used in the definition and implementation of mechanisms for service differentiation, network design and engineering, security, accounting, advertising, and research. Over the past 10 years the research community and the networking industry have investigated, proposed and developed several classification approaches. While traffic classification techniques are improving in accuracy and efficiency, the continued proliferation of different Internet application behaviors, in addition to growing incentives to disguise some applications to avoid filtering or blocking, are among the reasons that traffic classification remains one of many open problems in Internet research. In this article we review recent achievements and discuss future directions in traffic classification, along with their trade-offs in applicability, reliability, and privacy. We outline the persistently unsolved challenges in the field over the last decade, and suggest several strategies for tackling these challenges to promote progress in the science of Internet traffic classification.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Furthermore, relying solely on port numbers has been shown to be ineffective [125,228,320], largely due to the use of dynamic port negotiation, tunneling and misuse of port numbers assigned to well-known applications for obfuscating traffic and avoiding firewalls [54,109,176,286].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [109]
In the citing paper
“Portbased techniques are unreliable and antiquated, largely due to the use of dynamic port negotiation, tunneling and misuse of port numbers assigned to well-known applications for obfuscating traffic and avoiding firewalls [54,109,176,286].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [109]
Citations
Cites
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
Karagiannis, T., Papagiannaki, K., & Faloutsos, M. (2005) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/1080091.1080119 · Cited by 997
Toward the Accurate Identification of Network Applications
Moore, A. W., & Papagiannaki, K. (2005) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-31966-5_4 · Cited by 801
Transport layer identification of P2P traffic
Karagiannis, T., Broido, A., Faloutsos, M., & claffy, K. (2004) · ACM Internet Measurement Conference (IMC) · doi:10.1145/1028788.1028804 · Cited by 708
Self-citationWilliams, N., Zander, S., & Armitage, G. J. (2006) · ACM SIGCOMM Computer Communication Review · doi:10.1145/1163593.1163596 · Cited by 698
Rahbar, A. G., & Yang, O. W. W. (2008) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2008.080408 · Cited by 676
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
Self-citationA 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
Early application identification
Bernaille, L., Teixeira, R., & Salamatian, K. (2006) · Proceedings of the 2006 ACM CoNEXT conference on - CoNEXT '06 · doi:10.1145/1368436.1368445 · Cited by 374
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
Flagged first, then by citations
Research works printed but not linked: 1
- karagiannis, 0, BLlNC: Multilevel Traffic Classification in the Dark, Proc 2005 Conf Apps Technologies Architectures and Protocols for Comp Commun ACM SIGCOMM '05, 229
Cited by
Sorted by citations
Items 601–630 of 630
Li, R., Zhou, Z., Zhang, Z., Guo, J., & Liu, Q. (2025) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc65549.2025.11326005
SecFinder: An IoT Device Identification System Based on Flow-level Traffic in Smart Home
Zhang, N., Chen, S., Liu, S., Deng, X., He, Y., Xiang, W., & Li, M. (2025) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads67057.2025.11323115
Hybrid-Net: A Multimodal Deep Learning Framework for Anonymous Traffic Classification
He, Y. C., Zhong, Y., & Hu, N. (2025) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc67331.2025.00035
Lightweight Intelligent Task Classification Model for Heterogeneous Data in Non-Terrestrial Networks
Qin, Z., Liu, R., Zhao, L., Xu, D., & Wu, T. (2025) · International Conference on Communication and Information Processing (ICCIP) · doi:10.1145/3784833.3784923
GVTMC: A Novel Cryptocurrency Mining Traffic Classification Method
Lai, Y., Liu, H., & Zhang, W. (2025) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom66490.2025.00323
Xu, Y., Wang, J., Chen, Y., Wang, J., Sun, H., & Fang, Z. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.114806
Interpretable Feature Engineering for Robust Mobile Application Traffic ClassificationPreprint
Khesal, A. R., & Teimouri, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6293191
Ensuring the security of wireless networks using neural networks
Trukhanov, D. A., & Afanasenko, A. G. (2026) · AIP conference proceedings · doi:10.1063/5.0321777
Interpretable Feature Engineering for Robust Mobile Application Traffic ClassificationPreprint
Khesal, A. R., & Teimouri, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6470920
Liang, Z., Sun, R., Li, Z., Zhai, G., & Fang, K. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-8414-7_1
Network Traffic Classification: A Scientific Contribution Towards Statistical Approaches
Manekar, P., Rajput, K. S., Chandrakant, J. R., & Munot, M. V. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-18211-1_25
Quantum-Inspired Autoencoder and LSTM Model for Encrypted Traffic Anomaly Detection
Bompally, S. D. (2026) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa67686.2026.11468129
Btda: A Robust Framework for Encrypted Traffic Classification with Byte-Level TLS Data Augmentation
Wang, D., Yang, X., Cui, Z., Qiao, W., Wu, W.-Y., Huang, Y., Cui, S., Jiang, B., & Lu, Z. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461917
NetScan: Real-Time Network Traffic Monitoring and Security Evaluation Framework
Kher, A., & Bali, M. S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-20603-9_46
AI-Powered DDoS Attack Detection and Mitigation System
Jangam, N., Shirsat, S., Giramkar, S., Gangthade, R., & Mallare, S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-20603-9_5
Encrypted Traffic Classification Based on Two-Phase Feature Fusion
Tan, X.-B., Huang, C., Wang, H., Chen, S.-W., & Zheng, Q. (2026) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2026.3683889
ShortcutCatcher: Making Traffic Classification Reliable
Zhao, Y., Boffa, M., Vassio, L., & Mellia, M. (2026) · Proceedings of the ACM on Networking · doi:10.1145/3808671
Li, Z., Si, C.-X., Zhou, Z., Cheng, Z.-Y., Sun, P.-S., & Ren, Y.-M. (2026) · Comput. Networks · doi:10.1016/j.comnet.2026.112522
Jayawickrama, C., Muhandhiram, B., Bandara, I., Mihiranga, L., Senarathna, A. N., & Wijesooriya, A. (2026) · IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) · doi:10.1109/i2cacis69435.2026.11600342
ST-Mamba: Spatio-Temporal Feature-Based Encrypted Traffic Analysis Using Mamba Network
Zhu, J., Fan, M., Cao, R., Zuo, J., Lu, Y., & Zou, S. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3012-9_14
fpnt: A flexible preprocessing framework for network traffic analysis
Roh, H., & Lee, W. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112610
Autocorrelation-enhanced multi-scale CNN for protocol identification in raw telemetry byte streams
Liu, H., Shen, H., Xiao, Y., Yan, Y., & Guo, J. (2026) · Advances in Space Research · doi:10.1016/j.asr.2026.07.096
AI-Driven Intrusion Detection and Automated Response for Encrypted Network Traffic
Bakshi, A. R., Rajalakshmi, A., Kumar, S. G., & UmaMaheswari, J. (2026) · International Conference on Data Science and Business Systems (ICDSBS) · doi:10.1109/icdsbs69077.2026.11634725
Beyond Application Identification: Fine-Grained Activity Classification in Encrypted Mobile Traffic
Poyrazoglu, R. A., Ozen, D. N., Yildiz, A., Mrah, C. E., Sakar, C. O., & Soyak, E. G. (2026) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu71813.2026.11636590
Gu, Y., & Shen, L. (2026) · Applied Sciences · doi:10.3390/app16178886
SmartNIC-Based Network Traffic Monitoring: Taxonomy, Offloading Models, and Open ChallengesPreprint
Salazar, L., Elizalde, S., Kfoury, E. F., & Crichigno, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7488623
Elhaj-Abdou, M. E. M., & Badawi, W. K. (2026) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-026-00402-5
ANDROID MALWARE CLASSIFICATION APPROACH BASED ON HOST-LEVEL ENCRYPTED TRAFFIC SHAPING
Мухтаржановна, Л. Н., & Ibragimov, J. (2022) · Eurasian Journal of Academic Research · doi:10.70413/ejar-v02-i13-s1668
Metadata
- Authors
- Dainotti, A., Pescape, A., & Claffy, K.
- Affiliations
- University of Naples Federico II (IT)
- ORCID
- Dainotti, Alberto
- Venue
- IEEE Network
- Published
- 2012-01
- Volume
- 26
- Issue
- 1
- Pages
- 35-40
- ISSN
- 0890-8044 (ISSN-L)
- OpenAlex
- W2149600645
- Cited by
- 630
- References
- 16
- Citations by year
- 2026: 24 · 2025: 41 · 2024: 43 · 2023: 46 · 2022: 65 · 2021: 48 · 2020: 54 · 2019: 43 · 2018: 43 · 2017: 34 · 2016: 47 · 2015: 44 · 2014: 46 · 2013: 39 · 2012: 12
- FWCI
- 44.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Network (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Keywords
- network traffic classification, Internet traffic, privacy, service differentiation, network security, network accounting
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
