The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Unexpected means of protocol inference
Abstract
Network managers are inevitably called upon to associate network traffic with particular applications. Indeed, this operation is critical for a wide range of management functions ranging from debugging and security to analytics and policy support. Traditionally, managers have relied on application adherence to a well established global port mapping: Web traffic on port 80, mail traffic on port 25 and so on. However, a range of factors - including firewall port blocking, tunneling, dynamic port allocation, and a bloom of new distributed applications - has weakened the value of this approach. We analyze three alternative mechanisms using statistical and structural content models for automatically identifying traffic that uses the same application-layer protocol, relying solely on flow content. In this manner, known applications may be identified regardless of port number, while traffic from one unknown application will be identified as distinct from another. We evaluate each mechanism's classification performance using real-world traffic traces from multiple sites.
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
“These models are good at inferring discriminators for binary, textual and structural content [286].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [286]
In the citing paper
“Ma et al. [286] show that payload-based traffic classification can be performed without any a priori knowledge of the application classes using unsupervised clustering.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [286]
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 [286]
In the citing paper
“In evaluation, the PD-based protocol models resulted in the lowest total misclassification error, under 5%.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [286]
Citations
Cites
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 37,139
Identification of common molecular subsequences
Smith, T. F., & Waterman, M. S. (1981) · Journal of Molecular Biology · doi:10.1016/0022-2836(81)90087-5 · Cited by 9,962
Kademlia: A Peer-to-Peer Information System Based on the XOR Metric
Maymounkov, P., & Mazières, D. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45748-8_5 · Cited by 3,092
Bro: a system for detecting network intruders in real-time
Paxson, V. (1999) · Computer Networks · doi:10.1016/s1389-1286(99)00112-7 · Cited by 2,636
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
A distance measure between attributed relational graphs for pattern recognition
Sanfeliu, A., & Fu, K. (1983) · IEEE Transactions on Systems, Man and Cybernetics · doi:10.1109/tsmc.1983.6313167 · Cited by 1,142
Karagiannis, T., Papagiannaki, K., & Faloutsos, M. (2005) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/1080091.1080119 · Cited by 997
Accurate, scalable in-network identification of p2p traffic using application signatures
Sen, S. B., Spatscheck, O., & Wang, D. (2004) · International World Wide Web Conference (WWW) · doi:10.1145/988672.988742 · Cited by 811
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
An Analysis of the Skype Peer-to-Peer Internet Telephony Protocol
Baset, S., & Schulzrinne, H. G. (2006) · doi:10.1109/infocom.2006.312 · Cited by 736
Flagged first, then by citations
Research works printed but not linked: 3
- 10.5555/129837 · DOI
- 10.5555/646334.687801 · DOI
- G. Voss , A. Schröder , W. Müller-Wittig , and B. Schmidt . Using Graphics Hardware to Accelerate Biological Sequence Analysis . In Proc. of IEEE Tencon , Melbourne, Australia , 2005 . G. Voss, A. Schröder, W. Müller-Wittig, and B. Schmidt. Using Graphics Hardware to Accelerate Biological Sequence Analysis. In Proc. of IEEE Tencon, Melbourne, Australia, 2005.
Cited by
Sorted by citations
Items 101–200 of 203
On Metrics to Distinguish Skype Flows from HTTP Traffic
Freire, E. P., Ziviani, A., & Salles, R. M. (2009) · Journal of Network and Systems Management · doi:10.1007/s10922-009-9120-x · Cited by 13
A data mining approach for analysis of worm activity through automatic signature generation
Zurutuza, U., Uribeetxeberria, R., & Zamboni, D. (2008) · doi:10.1145/1456377.1456394 · Cited by 13
NetADHICT: a tool for understanding network traffic
Inoue, H., Jansens, D., Hijazi, A., & Somayaji, A. B. (2007) · Cited by 13
Traffic classification for managing Applications’ networking profiles
Alizadeh, H., & Zúquete, A. (2016) · Security and Communication Networks · doi:10.1002/sec.1516 · Cited by 12
Fast and Memory-Efficient Traffic Classification with Deep Packet Inspection in CMP Architecture
Liu, T., Sun, Y., & Guo, L. (2010) · IEEE International Conference on Networking, Architecture and Storage (NAS) · doi:10.1109/nas.2010.43 · Cited by 12
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
Automatic Protocol Signature Generation Framework for Deep Packet Inspection
Szabó, G., Turányi, Z. R., Toka, L., Molnár, S., & Santos, A. (2011) · International ICST Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS) · doi:10.4108/icst.valuetools.2011.245606 · Cited by 12
Implementation Issues of Early Application Identification
Bernaille, L., & Teixeira, R. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-76809-8_14 · Cited by 11
A nonparametric approach to the automated protocol fingerprint inference
Wang, Y., Yun, X., Zhang, Y., Chen, L., & Wu, G. (2017) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.10.009 · Cited by 11
Identification of Private ICS Protocols Based on Raw Traffic
Zhai, L., Zheng, Q., Zhang, X., Hu, H., Yin, W., Zeng, Y., & Wu, T. (2021) · Symmetry · doi:10.3390/sym13091743 · Cited by 11
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
Futuristic Analysis of Machine Learning Based Routing Protocols in Wireless Ad Hoc Networks
Kaushik, S., Tripathi, K., Gupta, R., & Mahajan, P. (2021) · International Conference on Computational Intelligence and Communication Technologies (CCICT) · doi:10.1109/ccict53244.2021.00067 · Cited by 11
A cascade forest approach to application classification of mobile traces
Liu, Y., Zhang, S., Ding, B. Y., Li, X., & Wang, Y. (2018) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc.2018.8377311 · Cited by 10
Jaiswal, R. C., & Lokhande, S. D. (2014) · Smart innovation, systems and technologies · doi:10.1007/978-3-319-07350-7_64 · Cited by 10
Grammar and model extraction for security applications using dynamic program binary analysis
Song, D. X., & Bayerri, J. C. (2010) · Cited by 10
Internet traffic clustering with constraints
Wang, Y., Xiang, Y., Zhang, J., & Yu, S. (2012) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc.2012.6314275 · Cited by 9
FDPHI: Fast Deep Packet Header Inspection for Data Traffic Classification and Management
Alkhalidi, N. A., & Yaseen, F. A. (2021) · International journal of intelligent engineering and systems · doi:10.22266/ijies2021.0831.33 · Cited by 8
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
Iliofotou, M., & Mitzenmacher, M. (2007) · Cited by 9
AjaxTracker: active measurement system for high-fidelity characterization of AJAX applications
Lee, M., Kompella, R. R., & Singh, S. R. (2010) · Cited by 8
FingerprinTV: Fingerprinting Smart TV Apps
Varmarken, J., Aaraj, J. A., Trimananda, R., & Markopoulou, A. (2022) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2022-0088 · Cited by 12
Classification with Intelligent Systems for Internet Traffic in Enterprise Networks
(2016) · International journal of computing, communication and instrumentation engineering · doi:10.15242/ijccie.iae0116008 · Cited by 8
P2P Traffic Identification Based on the Signatures of Key Packets
Wang, P., Guan, X., & Qin, T. (2009) · IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) · doi:10.1109/camad.2009.5161471 · Cited by 8
Digital forensics in VoIP networks
François, J., State, R., Engel, T., & Festor, O. (2010) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2010.5711450 · Cited by 8
On the impacts of human interactions in MMORPG traffic
Szabó, G., Veres, A., & Molnár, S. (2009) · Multimedia Tools and Applications · doi:10.1007/s11042-009-0298-4 · Cited by 7
IDENTIFICATION AND MANAGEMENT OF SESSIONS GENERATED BY INSTANT MESSAGING AND PEER-TO-PEER SYSTEMS
CHEN, Z., Delis, A., & WEI, P. (2008) · International Journal of Cooperative Information Systems · doi:10.1142/s0218843008001750 · Cited by 7
Classification of Correlated Internet Traffic Flows
Zhang, J., Chen, C., Xiang, Y., & Zhou, W. (2012) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom.2012.105 · Cited by 7
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 8
Fuller, J. D., Kasturi, R. P., Sikder, A. K., Xu, H., Arik, B., Verma, V., Asdar, E., & Saltaformaggio, B. (2021) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3460120.3484537 · Cited by 7
Yuan, Z., Xue, Y., & van der Schaar, M. (2015) · ACM Special Interest Group on Data Communication · doi:10.1145/2785956.2789997 · Cited by 7
Statistical traffic classification by boosting support vector machines
Sena, G. G., & Belzarena, P. (2012) · doi:10.1145/2382016.2382019 · Cited by 7
The Extensive Challenges of Internet Application Measurement
Arlitt, M. F., & Williamson, C. L. (2007) · IEEE Network · doi:10.1109/mnet.2007.364257 · Cited by 7
Hierarchical Real-time Network Traffic Classification Based on ECOC
Zhao, Y., Xie, X. M., & Jiang, M. (2013) · TELKOMNIKA Indonesian Journal of Electrical Engineering · doi:10.11591/telkomnika.v12i2.3877 · Cited by 6
Reverse Engineering for UAV Control Protocol Based on Detection Data
Yang, Z. X., Xiantai, G., Weidong, J., Haowen, X., & Zeng, L. (2017) · International Conference on Multimedia and Image Processing (ICMIP) · doi:10.1109/icmip.2017.30 · Cited by 6
A Detection and Classification of Peer-to-Peer Traffic: A Survey
Gomes, J. V., Inácio, P. R. M., Pereira, M., & Monteiro, P. P. (2011) · Cited by 6
RocketTC: A high throughput traffic classification architecture
Zhou, Z., Song, T., & Fu, W. (2012) · 2012 International Conference on Computing, Networking and Communications (ICNC) · doi:10.1109/iccnc.2012.6167454 · Cited by 5
A Markov Random Field Approach to Automated Protocol Signature Inference
Zhang, Y., Xu, T., Wang, Y., Sun, J., & Zhang, X. (2015) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-28865-9_25 · Cited by 5
Gautam, B. P., & Shiratori, N. (2020) · International Symposium on Computing and Networking Workshops (CANDARW) · doi:10.1109/candarw51189.2020.00079 · Cited by 5
Internet Traffic Classification by Aggregating Correlated Naive Bayes Predictions
(2023) · NeuroQuantology · doi:10.48047/nq.2021.19.5.nq21080 · Cited by 5
Enforcing Security with Behavioral Fingerprinting
François, J., State, R., Engel, T., & Festor, O. (2011) · HAL · Cited by 5
Exploiting intra-packet dependency for fine-grained protocol format inference
Huang, Q., Lee, P. P. C., & Zhang, Z. (2015) · IFIP Networking Conference (IFIP Networking) · doi:10.1109/ifipnetworking.2015.7145325 · Cited by 5
Extracting Information from Unknown Protocols On CampusNet
Yu, Z., Huang, Y., Shao-zhong, G. U. O., Zhou, B., & Ren, H. (2007) · IEEE International Symposium on Information Technologies and Applications in Education · doi:10.1109/isitae.2007.4409343 · Cited by 5
Discovering Packet Structure through Lightweight Hierarchical Clustering
Hijazi, A., Inoue, H., Matrawy, A., van Oorschot, P. C., & Somayaji, A. B. (2008) · International Conference on Communications · doi:10.1109/icc.2008.15 · Cited by 5
Noise-Tolerant and Optimal Segmentation of Message Formats for Unknown Application-Layer Protocols
Yu, S. (2013) · Cited by 5
Subspace clustering via graph auto-encoder network for unknown encrypted traffic recognition
Yang, R., Yu, A., Cai, L., & Meng, D. (2022) · Cybersecurity · doi:10.1186/s42400-022-00131-y · Cited by 4
P2P traffic identification based on transfer learning
Cai, L., Jing, X., Sun, S., Huang, H. L., Chen, N., & Lu, Y. (2013) · IEEE International Conference on Granular Computing (GrC) · doi:10.1109/grc.2013.6740374 · Cited by 4
A modular two-layer system for accurate and fast traffic classification
Hajikarami, F., Berenjkoub, M., & Manshaei, M. H. (2014) · International ISC Conference on Information Security and Cryptology (ISCISC) · doi:10.1109/iscisc.2014.6994039 · Cited by 4
Application-Oblivious L7 Parsing Using Recurrent Neural Networks
Li, H., Bian, Z., Zhang, P., Sun, Z., Hu, C., Fu, Q., Pan, T., & Lv, J. (2020) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2020.3000430 · Cited by 4
Network Traffic Content Identification Based on Time-Scale Signal Modeling
Yu, L., Xie, Y., Tang, S., Yu, S., Liu, X., & Hu, J. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3186665 · Cited by 4
Classification of unknown Internet traffic applications using Multiple Neural Network algorithm
Mohamed, A. A., Osman, A. H., & Motwakel, A. (2020) · doi:10.1109/iccis49240.2020.9257715 · Cited by 4
Machine Learning and keyword-matching integrated Protocol Identification
Cai, X., Zhang, R., & Wang, B. (2010) · IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT) · doi:10.1109/icbnmt.2010.5704888 · Cited by 4
Move Statistics-Based Traffic Classifiers Online
Wang, Y., & Yu, S. (2008) · doi:10.1109/csse.2008.911 · Cited by 4
Modeling application-based charging management with traffic detection function in 3GPP
Sou, S., & Hsieh, H.-Y. (2015) · Computer Networks · doi:10.1016/j.comnet.2015.08.017 · Cited by 3
Cocktail method for BitTorrent traffic identification in real time
Yang, Z., Li, L., Ji, Q., & Zhu, Y. (2012) · Journal of Computers · doi:10.4304/jcp.7.1.85-95 · Cited by 3
Active Build-Model Random Forest Method for Network Traffic Classification
Munther, A., Othman, R. R., Nizam, S., Sabri, N., & Anbar, M. (2014) · Cited by 3
Performance Evaluation for Four Supervised Classifiers in Internet Traffic Classification
Munther, A., Mohammed, I. J., Anbar, M., & Hilal, A. M. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-2693-0_12 · Cited by 3
Features analysis of internet traffic classification using interpretable machine learning models
Adjé, E. A., Houndji, V. R., & Dossou, M. (2022) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v11.i3.pp1175-1183 · Cited by 3
Traffic classification combining flow correlation and ensemble classifier
Miao, C. S., Meng, L., Yuan, C., Wang, X. W., & Chang, G. R. (2013) · International Journal of Wireless and Mobile Computing · doi:10.1504/ijwmc.2013.057575 · Cited by 3
Improving matching performance of DPI traffic classifier
Liu, T., Sun, Y., Guo, L., & Fang, B. (2011) · ACM Symposium on Applied Computing · doi:10.1145/1982185.1982296 · Cited by 3
Rodriguez, R., Mostafa, S., & Wang, X. (2017) · ACM Symposium on Access Control Models and Technologies (SACMAT) · doi:10.1145/3078861.3084175 · Cited by 3
File-aware P2P traffic classification
Zhou, Z., & Song, T. (2011) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/glocomw.2011.6162529 · Cited by 2
Kim, H. (2022) · Springer eBooks · doi:10.1007/978-3-030-95041-5_10 · Cited by 2
File-aware P2P traffic classification: An aid to network management
Song, T., & Zhou, Z. (2012) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-012-0172-4 · Cited by 2
Optimal supervised feature extraction in internet traffic classification
Aliakbarian, M. S., Fanian, A., Saleh, F. S., & Gulliver, T. A. (2013) · IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) · doi:10.1109/pacrim.2013.6625457 · Cited by 2
Traffic Classification Using Compact Protocol Fingerprint
Liu, Q., Zhang, J., & Zhao, B. (2012) · International Conference on Industrial Control and Electronics Engineering · doi:10.1109/icicee.2012.47 · Cited by 2
A New Method of Improving the Traditional Traffic Identification and Accuracy
Zhongsheng, W., & Gao, J. (2018) · International Journal of Advanced Network Monitoring and Controls · doi:10.21307/ijanmc-2019-006 · Cited by 2
A Review on Internet Traffic Classification Based on Artificial Intelligence Techniques
Malek, M. P., Naderi, S., & Garakani, H. G. (2022) · International Journal of Information & Communication Technology Research · doi:10.52547/itrc.14.2.1 · Cited by 2
Automatic network traffic classification
Wang, Y. (2013) · Cited by 2
Light-weight, Real-time Internet Traffic Classification
Iqbal, Z., Rahim, R., & Gillani, I. A. (2019) · IEEE International Conference on Advanced Networks and Telecommunications Systems · doi:10.1109/ants47819.2019.9118059 · Cited by 2
Gomes, J. V. (2012) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · Cited by 2
Latency and Throughput Optimization in Modern Networks: A Comprehensive SurveyPreprint
Mirzaeinia, A., Mirzaeinia, M., & Rezgui, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.03715 · Cited by 12
Network Traffic Characterization Using (p,n)-grams Packet Representation
Hijazi, A. (2014) · doi:10.22215/etd/2014-10109 · Cited by 2
Privacy-Preserving Machine Learning for Encrypted Traffic Classification in Secure Cloud Services
Almuhammadi, S., Alnahari, N., & Ba-amer, R. (2025) · IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) · doi:10.1145/3773276.3774875 · Cited by 1
KISSS: Stochastic Packet Inspection for UDP Traffic Classification
Finamore, A., Mellia, M., Meo, M., & Rossi, D. (2009) · PORTO@Iris · Cited by 1
AjaxTracker: A Tool for High Fidelity Characterization of Ajax Applications
Lee, M., Singh, S. R., & Kompella, R. R. (2008) · Purdue e-Pubs (Purdue University System) · Cited by 1
SQUEEZING THE MOST OUT OF TRAFFIC CLASSIFICATION
Szabó, G. (2010) · Cited by 1
Towards automatic identification of network applications
Wang, Y., & Yu, S. (2009) · International Colloquium on Computing, Communication, Control, and Management · doi:10.1109/cccm.2009.5267940 · Cited by 1
An Omnibus Identification of BitTorrent Traffic in a Stub Network
Yang, Z., Li, L., Ji, Q., & Zhu, Y. (2010) · International Symposium on Parallel Architectures, Algorithms and Programming · doi:10.1109/paap.2010.47 · Cited by 1
A New Network Traffic Identification Base on Deep Factorization Machine
Xu, Z., Zhang, J., Zhang, D., & Wei, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-36204-1_17 · Cited by 1
al., M. E. (2020) · International Journal of ADVANCED AND APPLIED SCIENCES · doi:10.21833/ijaas.2021.02.015 · Cited by 1
Preventing encrypted traffic analysis
Borisov, N., & Schear, N. (2011) · Cited by 1
MORSE: A P2P Monitoring and Reverting System
Zhou, Z., & Song, T. (2011) · International Conference on Networking and Distributed Computing (ICNDC) · doi:10.1109/icndc.2011.71 · Cited by 1
Modèles probabilistes et statistiques pour la conception et l'analyse des systèmes de communications
Bermolen, P. (2010) · Americanae (AECID Library)
On the use of context in network intrusion detection systems
Stoica, I. G., & Kannan, J. (2009)
Understanding Internet Traffic: Can We Classify the Unknown?
Dainotti, A. (2008) · Università degli Studi di Napoli Federico II · doi:10.6092/unina/fedoa/3370
A Hybrid Classifier with a Binning Method for Network Application Identification
Moon, I., Albalawi, U., Kim, J., Suh, S. C., & Lee, W.-H. (2014) · Journal of Integrated Design and Process Science · doi:10.3233/jid-2014-0011
Automatic mining of distinguishers with unknown protocol format
Wang, Y., Wang, L., Li, X., Zhang, Z., & Guo, L. (2010) · International Conference on Intelligent Computing and Integrated Systems (ICISS) · doi:10.1109/iciss.2010.5655016
Packet classification using the Hidden Markov Model
Khadimi, A. E., Lmater, M. A., Eddabbah, M., & Kayyali, M. S. E. (2011) · doi:10.1109/icmcs.2011.5945705
An Efficient Network Traffic Classification Approach with Bayesian Classifier
Rao, V. R., Kumar, L. P., & Kothalanka, A. (2014)
Generating Statistic Application Signatures for Inference of Unknown Applications
Luo, J., & Yu, S. (2013) · Global Congress on Intelligent Systems · doi:10.1109/gcis.2013.45
De-encapsulation of network packets for network protocol reverse engineering
Watrobski, P., & Summerville, D. H. (2016) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom.2016.7795386
UNDERSTANDING NETWORK TRAFFIC WITH NETADHICT
Inoue, H., Jansens, D., Hijazi, A., & Somayaji, A. B. (2006)
Quantifying the Reversibility of Protocol Format
Xu, Z., You, L., & Zheng, H. (2018) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass.2018.00079
Early Identification of Peer-To-Peer Traffic
Sztaki, M. (2011)
Profiling and Identification of Web Applications in Computer Network
Oudah, H. (2020) · PEARL (University of Plymouth) · doi:10.24382/775
Topic-model based Automatic Signature Extraction of Internet Applications
윤효진 (2013) · Seoul National University Open Repository (Seoul National University)
Dealing with P2P traffic in modern networks: measurement, identification and control
Valenti, S. (2011) · HAL
Acknowledgments / Remerciements
Paris, E. É. D., Réseaux, S. I. E., Valenti, S., Eurecom, E. B., Brescia, S. U., Olivier, E., Paristech, C. T., Friedman, T., Pierre, U., Curie, M., et al. (1991) · University of Toronto Press eBooks · doi:10.3138/9781442671010-003
Yuan, Z., Xue, Y., & van der Schaar, M. (2015) · ACM SIGCOMM Computer Communication Review · doi:10.1145/2829988.2789997
Metadata
- Authors
- Ma, J., Levchenko, K., Kreibich, C., Savage, S., & Voelker, G. M.
- Affiliations
- University of California San Diego (US)
- University of Cambridge (GB)
- Venue
- Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
- Publisher
- ACM
- Published
- 2006-10-25
- Pages
- 313-326
- OpenAlex
- W2148346742
- Cited by
- 204
- References
- 22
- Citations by year
- 2026: 1 · 2025: 1 · 2023: 4 · 2022: 9 · 2021: 5 · 2020: 11 · 2019: 4 · 2018: 7 · 2017: 10 · 2016: 6 · 2015: 12 · 2014: 12 · 2013: 16 · 2012: 17
- FWCI
- 11.50
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Internet Measurement Conference (IMC) (publishedVersion)
- 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
- network traffic classification, structural-content model, port mapping, tunneling, network management, traffic traces
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
