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Toward the Accurate Identification of Network Applications
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“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 [320]
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Cites
Snort - Lightweight Intrusion Detection for Networks
Roesch, M. (1999) · Cited by 3,094
Packet-level traffic measurements from the sprint IP backbone
Fraleigh, C., Moon, S. B., Lyles, B., Cotton, C., Khan, M., Moll, D., Rockell, R., Seely, T., & Diot, C. (2003) · IEEE Network · doi:10.1109/mnet.2003.1248656 · Cited by 639
The CoralReef Software Suite as a Tool for System and Network Administrators
Moore, D., Keys, K., Koga, R., Lagache, E., & claffy, K. (2001) · Cited by 114
Architecture of a network monitor
Moore, A. W., Hall, J., Kreibich, C., Harris, E. P., & Pratt, I. (2003) · Cited by 101
Self-citationContent-aware Internet application traffic measurement and analysis
Choi, T., Kim, C., Yoon, S., Park, J., Lee, B. J., Kim, H., Chung, H., & Jeong, T. S. (2004) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms.2004.1317737 · Cited by 45
(2004) · Elsevier eBooks · doi:10.1016/b978-1-932266-82-5.x5000-4 · Cited by 34
Research works printed but not linked: 1
- Moore, A.: Discrete content-based classification — a data set. Technical Report, Intel Research, Cambridge (2005)
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Traffic identification method based on on-line density based spatial clustering algorithm
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Internet traffic classification using MOEA and online refinement in voting on ensemble methods
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A research on identification method for WiFi-based home automation device suites
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On the performance of host-based business process aware QoS management
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Evaluation of Supervised Machine Learning for Classifying Video Traffic
Taylor, F. R. (2016) · NSUWorks (Nova Southeastern University) · Cited by 2
Intrusion detection in networked control systems : from system knowledge to network security
Caselli, M. (2016) · doi:10.3990/1.9789036541770 · Cited by 2
Artificial Immune Systems: Applications, Multi-Class Classification, Optimizations, and Analysis
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Novel applications of Machine Learning to Network Traffic Analysis and Prediction
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Network Traffic Characterization Using (p,n)-grams Packet Representation
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Analysis of Encrypted Malicious Traffic
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Optimised TCP Traf“c Classi“cation with multiple statistical algorithms
Wang, X., & Parish, D. J. (2010) · International Conference on Information, Networking and Automation (ICINA) · doi:10.1109/icina.2010.5636393 · Cited by 1
STAFF: Automated Signature Generationfor Fine-Grained FunctionTraffic Identification
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Employing Federated Semi-supervised Learning for Network Traffic Classification
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Application-level Traffic Identification using SET-based Flow in BcN
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A Statistical-Feature ML Approach to IP Traffic Classification Based on CUDA
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Automatic Transition System Model Identification for Network Applications from Packet Traces
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Application feature extraction by using both dynamic binary tracking and statistical learning
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Behavior Similarity Awared Abnormal Service Identification Mechanism
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Research on Multi-layer Classification Method of Network Traffic Based on Machine Learning
Wang, B. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1176/2/022006 · Cited by 1
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Adekitan, A. I., & Awosope, C. O. A. (2020) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v17.i3.pp1442-1452 · Cited by 1
Impact of Early Estimation of Statistical Flow Features in On-line P2P Classification
Abdalla, B. M. A., Hamdan, M., Khalifa, E. H., Abdallah, A. E., Ismail, I., & Marsono, M. N. (2020) · IEEE Student Conference on Research and Development (SCOReD) · doi:10.1109/scored50371.2020.9250967 · Cited by 1
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Darwish, O., Herzallah, S., Maabreh, M., Al-Eidi, S., Al-Maani, M., & Tashtoush, Y. M. (2022) · International Arab Conference on Information Technology (ACIT) · doi:10.1109/acit57182.2022.9994184 · Cited by 1
TPIPD: A Robust Model for Online VPN Traffic Classification
Meng, Y., Qin, T., Wang, H., & Chen, Z. (2022) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom56396.2022.00025 · Cited by 1
Maryam, L. A., & Raheem, A. A. (2020) · Journal of Applied Science Information and Computing · doi:10.59568/jasic-2020-1-1-04 · Cited by 1
TCP-Verkehrsklassifizierung mit Markov-Modellen
Münz, G., Braun, L., Dai, H., & Carle, G. (2010) · PIK - Praxis der Informationsverarbeitung und Kommunikation · doi:10.1515/piko.2010.019 · 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
Intelligent Sensing and Classification in DSR-Based Ad Hoc Networks
Dempsey, T., Şahin, G., & Morton, Y. J. (2009) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e92.d.818 · Cited by 1
Attack Detection based on Statistical Discriminators
do Carmo, M. F. F., Maia, J. E. B., Filho, R. H., & de Sousa, J. N. (2007) · Global Information Infrastructure and Networking Symposium · doi:10.1109/giis.2007.4404186 · Cited by 1
Relational network-service clustering analysis with set evidences
Li, P., Faltings, B. V., Yang, Q., & Hu, D. H. (2010) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/1866423.1866432 · Cited by 1
Application of advanced machine learning techniques to early network traffic classification
Gómez, S. E. (2020) · doi:10.35376/10324/43197 · Cited by 1
Cao, H. (2008) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 1
Zhang, Y., Yu, X., Huang, Y., Zhang, X., Jia, Z., & Qin, X. (2023) · Research Square · doi:10.21203/rs.3.rs-3134884/v1 · Cited by 1
Distributed Network Traffic Classification Based on Heterogeneous Federated EnsemblePreprint
Deng, Q., Liao, N., & Deng, Q. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4820781 · Cited by 1
Machine learning-Enabled Network Traffic Analysis
Aouedi, O. (2022) · theses.fr (ABES) · doi:10.70675/98b92b52z17b2z43b7zb42ez26f74d2fff67 · Cited by 1
An Investigation on Detecting Applications Hidden in SSL Streams using Machine Learning Techniques
McCarthy, C. S. (2010) · Library and Archives Canada (Government of Canada) · Cited by 1
High Performance Network Intrusion Detection: A New Paradigm is Needed
Albrecht, D. R. (2010) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)
Modèles probabilistes et statistiques pour la conception et l'analyse des systèmes de communications
Bermolen, P. (2010) · Americanae (AECID Library)
Network Content Safety Design of Monitoring System
Gu, J., Xiao, B., & Gu, Z. (2012) · Communications in computer and information science · doi:10.1007/978-3-642-34041-3_8
Treatment-based traffic classification for residential wireless networks
Li, F., Claypool, M. K., & Kinicki, R. E. (2015) · 2012 International Conference on Computing, Networking and Communications (ICNC) · doi:10.1109/iccnc.2015.7069334
Research and implementation of mobile internet services identification system
Lan, Y., Tang, Y., & Zou, S. (2015) · International Conference on Computer Science & Education (ICCSE) · doi:10.1109/iccse.2015.7250278
Multiple Classifier Systems in Adversarial Environments: "Challenges and Solutions"
Gargiulo, F. (2009) · Università degli Studi di Napoli Federico II · doi:10.6092/unina/fedoa/3894
Herrmann, D. (2008) · University of Regensburg Publication Server (University of Regensburg) · doi:10.5283/epub.9285
Features Optimization Techniques for Traffic Classifiers
He, J., Yang, Y., Qiao, Y., Jiang, K., & Liu, C. (2012) · International Conference on Multimedia Information Networking and Security · doi:10.1109/mines.2012.112
SEAMS: A Signaling Layer for End-Host-Assisted Middlebox Services
Hummen, R., Ziegeldorf, J. H., Heer, T., Wirtz, H., & Wehrle, K. (2012) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom.2012.250
Evaluation of regular expression match engines for DPI system
Zhang, J., & Wen, Q. (2011) · IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT) · doi:10.1109/icbnmt.2011.6155959
Flow-aggregation Accelerating Strategy for TCP Traffic
Zhang, X., & Ding, W. (2014) · Journal of Networks · doi:10.4304/jnw.9.6.1416-1425
Metadata
- Authors
- Moore, A. W., & Papagiannaki, K.
- Affiliations
- University of Cambridge (GB)
- Intel (United States) (US)
- ORCID
- Moore, Andrew W.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2005
- Pages
- 41-54
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1521553548
- Cited by
- 801
- References
- 6
- Citations by year
- 2026: 10 · 2025: 26 · 2024: 31 · 2023: 25 · 2022: 40 · 2021: 35 · 2020: 41 · 2019: 34 · 2018: 36 · 2017: 28 · 2016: 31 · 2015: 35 · 2014: 39 · 2013: 48 · 2012: 56
- FWCI
- 25.93
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Funders
- Intel Corporation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- network traffic classification, content-based classification, packet payload analysis, firewall bypass, network traffic measurement, packet traces
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