Machine learning based encrypted traffic classification: Identifying SSH and Skype
Abstract
The objective of this work is to assess the robustness of machine learning based traffic classification for classifying encrypted traffic where SSH and Skype are taken as good representatives of encrypted traffic. Here what we mean by robustness is that the classifiers are trained on data from one network but tested on data from an entirely different network. To this end, five learning algorithms — AdaBoost, Support Vector Machine, Naïe Bayesian, RIPPER and C4.5 — are evaluated using flow based features, where IP addresses, source/destination ports and payload information are not employed. Results indicate the C4.5 based approach performs much better than other algorithms on the identification of both SSH and Skype traffic on totally different networks.
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C4.5 DT generalizes well from one network to another for SSH traffic.
“Hence, it generalizes well from one network to another.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [17]
For SSH traffic, C4.5 DT achieves 95.9% DR and 2.8% FPR on the Dalhousie dataset, 97.2% DR and 0.8% FPR on the AMP dataset, and 82.9% DR and 0.5% FPR on the MAWI dataset.
“For SSH traffic, it achieves 95.9% DR and 2.8% FPR on the Dalhousie dataset, 97.2% DR and 0.8% FPR on the AMP dataset, and 82.9% DR and 0.5% FPR on the MAWI dataset.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [17]
Alshammari et al. [17] use supervised AdaBoost, RIPPER, SVM, NB and C4.5 DT classifiers to identify SSH encrypted traffic and Skype traffic at the flow level.
“The identification of Skype traffic at the flow level is also addressed in Alshammari et al. [17] by employing supervised AdaBoost, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), SVM, NB and C4.5 DT classifiers.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [17]
C4.5 DT outperforms the other classifiers for the majority of datasets in Alshammari et al.'s evaluation.
“In the evaluation, C4.5 DT outperform the other classifiers for the majority of datasets.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [17]
C4.5 DT performed well for Skype traffic with 98.4% DR and 7.8% FPR in the Dalhousie dataset.
“The C4.5 DT classifier also performed well for Skype traffic with 98.4% DR and 7.8% FPR in the Dalhousie dataset.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [17]
When trained and tested across datasets, C4.5 DT achieves 83.7% DR and 1.5% FPR.
“Furthermore, when trained and tested across datasets (i.e.across networks), it achieves 83.7% DR and 1.5% FPR.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [17]
Alshammari et al. [17] do not detect the precise applications within a secure session.
“Unfortunately, Alshammari et al. [17] do not detect the precise applications within a secure session.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [17]
Cites
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500 · see citing works
(1992) · Cited by 23,431 · see citing works
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,437 · see citing works
Cohen, W. W. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50023-2 · Cited by 3,890 · see citing works
A TUTORIAL ON SUPPORT VECTOR MACHINES FOR PATTERN RECOGNITION, DATA MINING AND KNOWLEDGE DISCOVERY
Burger, C. J. C. (1998) · Cited by 3,261 · 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
Introduction to Machine Learning
(2019) · Series in machine perception and artificial intelligence · doi:10.1142/9789811201967_0001 · Cited by 1,638 · 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
Karagiannis, T., Papagiannaki, K., & Faloutsos, M. (2005) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/1080091.1080119 · Cited by 997 · see citing works
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 · see citing works
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 · 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
Introduction to Machine Learning
Alpaydin, E., Learning, I. T. M., & Chen, M.-B. (2010) · Cited by 650 · 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
An Analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for Network Anomaly Detection
Mahoney, M. V., & Chan, P. K. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-45248-5_13 · Cited by 536 · see citing works
Haffner, P., Sen, S. B., Spatscheck, O., & Wang, D. (2005) · doi:10.1145/1080173.1080183 · Cited by 376 · see citing works
On Inferring Application Protocol Behaviors in Encrypted Network Traffic
Wright, C. V., Monrose, F., Masson, G. M., & Chan, P. (2006) · Cited by 221 · see citing works
Early Recognition of Encrypted Applications
Bernaille, L., & Teixeira, R. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-71617-4_17 · Cited by 200 · see citing works
Dynamic application-layer protocol analysis for network intrusion detection
Dreger, H., Feldmann, A., Mai, M., Paxson, V., & Sommer, R. (2006) · MPG.PuRe (Max Planck Society) · Cited by 180 · see citing works
Introduction to Machine Learning
Tiwari, A. K. (2019) · IGI Global eBooks · doi:10.4018/978-1-7998-0414-7.ch003 · Cited by 131 · see citing works
Detailed Analysis of Skype Traffic
Bonfiglio, D., Mellia, M., Meo, M., & Rossi, D. (2008) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2008.2008927 · Cited by 109 · see citing works
HMM profiles for network traffic classification
Wright, C. V., Monrose, F., & Masson, G. M. (2004) · doi:10.1145/1029208.1029211 · Cited by 93 · see citing works
Zhang⋆, Y., & Paxson, V. (2000) · Cited by 78 · see citing works
Behavioral authentication of server flows
Early, J. P., Brodley, C. E., & Rosenberg, C. (2004) · Annual Computer Security Applications Conference · doi:10.1109/csac.2003.1254309 · Cited by 68 · see citing works
A flow based approach for SSH traffic detection
Alshammari, R., & Zincir-Heywood, N. (2007) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2007.4414006 · Cited by 63 · see citing works
Self-citationInvestigating Two Different Approaches for Encrypted Traffic Classification
Alshammari, R., & Zincir-Heywood, N. (2008) · Conference on Privacy, Security and Trust · doi:10.1109/pst.2008.15 · Cited by 58 · see citing works
Self-citationAnalysis and Signature of Skype VoIP Session Traffic
Ehlert, S., Petgang, S., Seminars, E.-M. S., & Petgang, R. (2006) · Cited by 58 · see citing works
Detecting Skype flows in Web traffic
Freire, E. P., Ziviani, A., & Salles, R. M. (2008) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms.2008.4575121 · Cited by 34 · see citing works
Perényi, M., Gefferth, A., Dang, T. D., & Molnár, S. (2007) · Globecom · doi:10.1109/glocom.2007.81 · Cited by 24 · see citing works
Flagged first, then by citations
2 of the 32 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
A survey of methods for encrypted traffic classification and analysis
Velan, P., Čermák, M., Čeleda, P., & Drašar, M. (2015) · International Journal of Network Management · doi:10.1002/nem.1901 · Cited by 387 · see citing works
Network traffic classification: Techniques, datasets, and challenges
Azab, A., Khasawneh, M., Alrabaee, S., Choo, K.-K. R., & Sarsour, M. (2022) · Digit. Commun. Networks · doi:10.1016/j.dcan.2022.09.009 · Cited by 247 · see citing works
Identification of Encrypted Traffic Through Attention Mechanism Based Long Short Term Memory
Yao, H., Liu, C., Zhang, P., Wu, S. Y., Jiang, C., & Yu, S. (2019) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2940675 · Cited by 212 · see citing works
Markov chain fingerprinting to classify encrypted traffic
Korczyński, M., & Duda, A. (2014) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2014.6848005 · Cited by 196 · see citing works
Lashkari, A. H., Kaur, G., & Rahali, A. (2020) · International Conference on Communication and Network Security (ICCNS) · doi:10.1145/3442520.3442521 · Cited by 164 · see citing works
Can encrypted traffic be identified without port numbers, IP addresses and payload inspection?
Alshammari, R., & Zincir-Heywood, N. (2010) · Computer Networks · doi:10.1016/j.comnet.2010.12.002 · Cited by 153 · see citing works
Self-citationAutomated bug assignment: Ensemble-based machine learning in large scale industrial contexts
Jonsson, L., Borg, M., Broman, D., Sandahl, K., Eldh, S., & Runeson, P. (2015) · Empirical Software Engineering · doi:10.1007/s10664-015-9401-9 · Cited by 145 · see citing works
A method for classification of network traffic based on C5.0 Machine Learning Algorithm
Bujlow, T., Riaz, T. M., & Pedersen, J. (2012) · International Conference on Computing, Networking and Communications · doi:10.1109/iccnc.2012.6167418 · Cited by 153 · see citing works
An innovative approach for real-time network traffic classification
Dias, K. L., Pongelupe, M. A., Caminhas, W. M., & de Errico, L. (2019) · Computer Networks · doi:10.1016/j.comnet.2019.04.004 · Cited by 131 · see citing works
Service Usage Classification with Encrypted Internet Traffic in Mobile Messaging Apps
Fu, Y., Xiong, H., Lu, X., Yang, J., & Chen, C. (2016) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2016.2516020 · Cited by 127 · see citing works
A review on machine learning–based approaches for Internet traffic classification
Salman, O., Elhajj, I. H., Kayssi, A., & Chehab, A. (2020) · Annals of Telecommunications · doi:10.1007/s12243-020-00770-7 · Cited by 131 · see citing works
Attention-based bidirectional GRU networks for efficient HTTPS traffic classification
Liu, X., You, J., Wu, Y., Li, T., Li, L., Zhang, Z., & Ge, J. (2020) · Information Sciences · doi:10.1016/j.ins.2020.05.035 · Cited by 118 · see citing works
Inductive Intrusion Detection in Flow-Based Network Data Using One-Class Support Vector Machines
Winter, P., Hermann, E., & Zeilinger, M. (2011) · IFIP International Conference on New Technologies, Mobility and Security (NTMS) · doi:10.1109/ntms.2011.5720582 · Cited by 98 · see citing works
A Survey on Encrypted Traffic Classification
Cao, Z., Xiong, G., Zhao, Y. T., Li, Z., & Guo, L. (2014) · Communications in computer and information science · doi:10.1007/978-3-662-45670-5_8 · Cited by 96 · 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
Early Classification of Network Traffic through Multi-classification
Dainotti, A., Pescapè, A., & Sansone, C. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-20305-3_11 · Cited by 83 · see citing works
Flow-Based Anomaly Detection Using Neural Network Optimized with GSA Algorithm
Jadidi, Z., Muthukkumarasamy, V., Sithirasenan, E., & Sheikhan, M. (2013) · IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW) · doi:10.1109/icdcsw.2013.40 · Cited by 76 · see citing works
Accelerating Decision Tree Based Traffic Classification on FPGA and Multicore Platforms
Tong, D., Qu, Y. R., & Prasanna, V. K. (2017) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2017.2714661 · Cited by 76 · see citing works
Cryptomining makes noise: Detecting cryptojacking via Machine Learning
Caprolu, M., Raponi, S., Oligeri, G., & Pietro, R. D. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.02.016 · Cited by 75 · see citing works
A Look Behind the Curtain: Traffic Classification in an Increasingly Encrypted Web
Akbari, I., Salahuddin, M. A., Aniva, L., Limam, N., Boutaba, R., Mathieu, B., Moteau, S., & Tuffin, S. (2021) · Proceedings of the ACM on Measurement and Analysis of Computing Systems · doi:10.1145/3447382 · Cited by 72 · see citing works
Jorgensen, S., Holodnak, J. T., Dempsey, J., de Souza, K., Raghunath, A., Rivet, V., DeMoes, N., Alejos, A., & Wollaber, A. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3244168 · Cited by 72 · see citing works
Flow-based anomaly detection in high-speed links using modified GSA-optimized neural network
Sheikhan, M., & Jadidi, Z. (2012) · Neural Computing and Applications · doi:10.1007/s00521-012-1263-0 · Cited by 67 · see citing works
A multi-level framework to identify HTTPS services
Shbair, W. M., Cholez, T., Francois, J., & Chrisment, I. (2016) · NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium · doi:10.1109/noms.2016.7502818 · Cited by 69 · see citing works
Chen, J., Lv, T., Cai, S., Song, L., & Yin, S. (2023) · Information and Software Technology · doi:10.1016/j.infsof.2023.107166 · Cited by 65 · see citing works
Network malware classification comparison using DPI and flow packet headers
Boukhtouta, A., Mokhov, S. A., Lakhdari, N.-E., Debbabi, M., & Paquet, J. (2015) · Journal of Computer Virology and Hacking Techniques · doi:10.1007/s11416-015-0247-x · Cited by 60 · see citing works
Scalable Architecture for SDN Traffic Classification
Hayes, M., Ng, B., Pekár, A., & Seah, W. K. G. (2017) · IEEE Systems Journal · doi:10.1109/jsyst.2017.2690259 · Cited by 59 · see citing works
A Comparison of three machine learning techniques for encrypted network traffic analysis
Arndt, D. J., & Zincir-Heywood, N. (2011) · IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA) · doi:10.1109/cisda.2011.5945941 · Cited by 57 · see citing works
Applications of machine learning in cryptography
Alani, M. M. (2019) · International Conference on Cryptography, Security and Privacy (CSP) · doi:10.1145/3309074.3309092 · Cited by 56 · see citing works
Identification of VoIP encrypted traffic using a machine learning approach
Alshammari, R., & Zincir-Heywood, N. (2015) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2014.03.013 · Cited by 54 · see citing works
Self-citationQuantifying the accuracy of the ground truth associated with Internet traffic traces
Dusi, M., Gringoli, F., & Salgarelli, L. (2010) · Computer Networks · doi:10.1016/j.comnet.2010.11.006 · Cited by 54 · see citing works
Abnormal Traffic Detection: Traffic Feature Extraction and DAE-GAN With Efficient Data Augmentation
Li, Z., Chen, S., Dai, H., Xu, D., Chu, C., & Xiao, B. (2022) · IEEE Transactions on Reliability · doi:10.1109/tr.2022.3204349 · Cited by 53 · see citing works
TLS/SSL Encrypted Traffic Classification with Autoencoder and Convolutional Neural Network
Yang, Y., Kang, C., Gou, G., Li, Z., & Xiong, G. (2018) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc/smartcity/dss.2018.00079 · Cited by 47 · see citing works
Sharma, A., & Lashkari, A. H. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110984 · Cited by 54 · see citing works
Improved KNN Algorithm for Fine-Grained Classification of Encrypted Network Flow
Ma, C., Du, X., & Cao, L. (2020) · Electronics · doi:10.3390/electronics9020324 · Cited by 43 · see citing works
Special Issue on Advanced Persistent Threat
Chen, J., Su, C., Yeh, K., & Yung, M. M. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.11.005 · Cited by 43 · see citing works
An investigation on the identification of VoIP traffic: Case study on Gtalk and Skype
Alshammari, R., & Zincir-Heywood, N. (2010) · International Conference on Network and Service Management (CNSM) · doi:10.1109/cnsm.2010.5691210 · Cited by 42 · see citing works
Self-citationReinforcement Learning for Intrusion Detection: More Model Longness and Fewer Updates
dos Santos, R. R., Viegas, E. K., Santin, A. O., & Cogo, V. V. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3207094 · Cited by 47 · see citing works
Using Traffic Analysis to Identify the Second Generation Onion Router
Barker, J. H., Hannay, P., & Szewczyk, P. (2011) · IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) · doi:10.1109/euc.2011.76 · Cited by 39 · see citing works
A Heuristic Statistical Testing Based Approach for Encrypted Network Traffic Identification
Niu, W., Zhuo, Z., Zhang, X., Du, X., Yang, G., & Guizani, M. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2894290 · Cited by 39 · see citing works
Bianchi, G., Bonola, M., Pontarelli, S., Sanvito, D., Capone, A., & Cascone, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.01977 · Cited by 38 · 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
High throughput and programmable online trafficclassifier on FPGA
Tong, D., Sun, L., Matam, K. K., & Prasanna, V. K. (2013) · ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA) · doi:10.1145/2435264.2435307 · Cited by 32 · see citing works
Schmidt, B., Al‐Fuqaha, A., Gupta, A. K., & Kountanis, D. I. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.01.016 · Cited by 32 · see citing works
GCN-ETA: High-Efficiency Encrypted Malicious Traffic Detection
Zheng, J., Zeng, Z., & Feng, T. (2022) · Security and Communication Networks · doi:10.1155/2022/4274139 · Cited by 32 · see citing works
Traffic Classification Based on Zero-Length Packets
Kampeas, J., Cohen, A., & Gurewitz, O. (2018) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2018.2825881 · Cited by 30 · see citing works
Can We Identify NAT Behavior by Analyzing Traffic Flows?
Gokcen, Y., Foroushani, V. A., & Zincir-Heywood, N. (2014) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw.2014.28 · Cited by 30 · see citing works
Chen, J., Xie, H., Cai, S., Song, L., Geng, B., & Guo, W. (2024) · Computers & Security · doi:10.1016/j.cose.2024.104083 · Cited by 29 · see citing works
Unveiling Skype encrypted tunnels using GP
Alshammari, R., & Zincir-Heywood, N. (2010) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2010.5586288 · Cited by 29 · see citing works
Self-citationLearning from imbalanced data for encrypted traffic identification problem
Vu, L., Tra, D. V., & Nguyen, Q. U. (2016) · International Symposium on Information and Communication Technology · doi:10.1145/3011077.3011132 · Cited by 29 · see citing works
Estimation of urban route travel time distribution using Markov chains and pair-copula construction
Yun, M., Qin, W., Yang, X., & Liang, F. (2019) · Transportmetrica B Transport Dynamics · doi:10.1080/21680566.2019.1637798 · Cited by 28 · see citing works
A Model for Detecting Tor Encrypted Traffic using Supervised Machine Learning
Almubayed, A., Hadi, A. M., & Atoum, J. O. (2015) · International Journal of Computer Network and Information Security · doi:10.5815/ijcnis.2015.07.02 · Cited by 26 · see citing works
Secure shell (ssh) traffic analysis with flow based features using shallow and deep networks
Vinayakumar, R., Soman, K. P., & Poornachandran, P. (2017) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2017.8126143 · Cited by 25 · see citing works
isAnon: Flow-Based Anonymity Network Traffic Identification Using Extreme Gradient Boosting
Cai, Z., Jiang, B., Lü, Z., Liu, J., & Ma, P. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851964 · Cited by 24 · see citing works
Volunteer-based system for classification of traffic in computer networks
Bujlow, T., Balachandran, K., Riaz, T. M., & Pedersen, J. M. (2011) · Telecommunications Forum (TELFOR) · doi:10.1109/telfor.2011.6143528 · Cited by 24 · see citing works
Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols
Al-Obeidat, F. N., & El-Alfy, E.-S. M. (2017) · Personal and Ubiquitous Computing · doi:10.1007/s00779-017-1096-z · 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
A Multi-Label Multi-View Learning Framework for In-App Service Usage Analysis
Fu, Y., Liu, J., Li, X., & Xiong, H. (2018) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3151937 · Cited by 23 · see citing works
An investigation on identifying SSL traffic
McCarthy, C. S., & Zincir-Heywood, N. (2011) · IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA) · doi:10.1109/cisda.2011.5945943 · Cited by 23 · see citing works
Lee, T. R., Teh, J. S., Jamil, N., Yan, J. L. S., & Chen, J. (2021) · IEEE Access · doi:10.1109/access.2021.3116468 · Cited by 23 · see citing works
A Classification Algorithm for Network Traffic based on Improved Support Vector Machine
Ding, L., Yu, F., Peng, S., & Xu, C. (2013) · Journal of Computers · doi:10.4304/jcp.8.4.1090-1096 · Cited by 22 · see citing works
Survey: Intrusion Detection Systems in Encrypted Traffic
Kovanen, T., David, G., & Hämäläinen, T. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46301-8_23 · Cited by 22 · see citing works
Automated classifier generation for application-level mobile traffic identification
Choi, Y., Chung, J., Park, B.-C., & Hong, J. W. (2012) · 2012 IEEE Network Operations and Management Symposium · doi:10.1109/noms.2012.6212032 · Cited by 22 · see citing works
A Frontier: Dependable, Reliable and Secure Machine Learning for Network/System Management
Le, D. C., & Zincir-Heywood, N. (2020) · Journal of Network and Systems Management · doi:10.1007/s10922-020-09512-5 · Cited by 21 · see citing works
Wang, X.-B., Yuan, Q.-J., Wang, Y.-J., Gou, G.-P., Gu, C., Yu, G., & Xiong, G. (2024) · Comput. Networks · doi:10.1016/j.comnet.2024.110403 · Cited by 19 · see citing works
Network traffic classification in encrypted environment: A case study of Google Hangout
Datta, J., Kataria, N., & Hubballi, N. (2015) · National Conference on Communications · doi:10.1109/ncc.2015.7084879 · Cited by 20 · see citing works
Entropy-based Prediction of Network Protocols in the Forensic Analysis of DNS TunnelsPreprint
Homem, I., Papapetrou, P., & Dosis, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.06363 · Cited by 20 · see citing works
Taurus: An Intelligent Data Plane.Preprint
Swamy, T., Rucker, A., Shahbaz, M., & Olukotun, K. (2020) · arXiv (Cornell University) · Cited by 20 · see citing works
AN-Net: an Anti-Noise Network for Anonymous Traffic Classification
Deng, X.-W., Wang, Y.-J., & Xue, Z. (2024) · The Web Conference · doi:10.1145/3589334.3645691 · Cited by 22 · see citing works
Towards Fingerprinting Malicious Traffic
Boukhtouta, A., Lakhdari, N.-E., Mokhov, S. A., & Debbabi, M. (2013) · Procedia Computer Science · doi:10.1016/j.procs.2013.06.073 · Cited by 19 · see citing works
Link-Layer Device Type Classification on Encrypted Wireless Traffic with COTS Radios
Maiti, R. R., Siby, S., Sridharan, R., & Tippenhauer, N. O. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66399-9_14 · Cited by 19 · see citing works
Attribute-Based Zero-Shot Learning for Encrypted Traffic Classification
Hu, Y., Cheng, G., Chen, W., & Jiang, B. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3183247 · Cited by 17 · see citing works
CL-ETC: A Contrastive Learning Method for Encrypted Traffic Classification
Zhao, Z., Guo, Y., Wang, J. H., Wang, H., Zhang, C., & An, C. (2022) · IFIP Networking Conference (IFIP Networking) · doi:10.23919/ifipnetworking55013.2022.9829767 · Cited by 17 · see citing works
Metaheuristic algorithms based Flow Anomaly Detector
Jadidi, Z., Muthukkumarasamy, V., & Sithirasenan, E. (2013) · Asia-Pacific Conference on Communications (APCC) · doi:10.1109/apcc.2013.6766043 · Cited by 17 · see citing works
A Look Behind the Curtain: Traffic Classification in an Increasingly Encrypted Web
Akbari, I., Salahuddin, M. A., Aniva, L., Limam, N., Boutaba, R., Mathieu, B., Moteau, S., & Tuffin, S. (2021) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/3410220.3453921 · Cited by 17 · see citing works
An Efficient Approach for Encrypted Traffic Classification using CNN and Bidirectional GRU
Lin, C. Y., Chen, B.-H., & Lan, W. (2022) · International Conference on Consumer Electronics and Computer Engineering (ICCECE) · doi:10.1109/iccece54139.2022.9712708 · Cited by 17 · see citing works
Bujlow, T., Carela-Español, V., & Barlet‐Ros, P. (2014) · Cited by 16 · see citing works
Deep Learning for Network Traffic ClassificationPreprint
Bayat, N., Jackson, W., & Liu, D. S. H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.12693 · Cited by 16 · see citing works
A new classification method for encrypted internet traffic using machine learning
UĞURLU, M., Doğru, İ. A., & Arslan, R. S. (2021) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.3906/elk-2011-31 · Cited by 15 · see citing works
Applying an Improved Dung Beetle Optimizer Algorithm to Network Traffic Identification
Wu, Q., Xu, H. F., & Liu, M. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.048461 · Cited by 15 · see citing works
Amazon Echo Security: Machine Learning to Classify Encrypted Traffic
Jackson, R. B., & Camp, T. K. (2018) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn.2018.8487332 · Cited by 15 · see citing works
Chakraborty, A., Sanadhya, S., Das, S. R., Kim, D., & Kim, K.-H. (2016) · ACM International Conference on emerging Networking EXperiments and Technologies (CoNEXT) · doi:10.1145/2999572.2999597 · Cited by 14 · see citing works
SFIM: Identify user behavior based on stable features
Wu, H., Wu, Q., Cheng, G., Guo, S., Hu, X., & Yan, S. (2021) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-021-01214-2 · Cited by 14 · see citing works
Output Prediction Attacks on Block Ciphers Using Deep Learning
Kimura, H., Emura, K., Isobe, T., Ito, R., Ogawa, K., & Ohigashi, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16815-4_15 · Cited by 13 · see citing works
Packet size distribution of typical Internet applications
Wu, X., Li, W., Liu, F., & Hua, Y. (2012) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/icwamtip.2012.6413493 · Cited by 13 · see citing works
A method for assessing quality of service in broadband networks
Bujlow, T., Riaz, M., & Pedersen, J. M. (2012) · VBN Forskningsportal (Aalborg Universitet) · Cited by 13 · see citing works
Flow-based anomaly detection using semisupervised learning
Jadidi, Z., Muthukkumarasamy, V., Sithirasenan, E., & Singh, K. (2015) · International Conference on Signal Processing and Communication Systems · doi:10.1109/icspcs.2015.7391760 · Cited by 13 · see citing works
Early classification of residential networks traffic using C5.0 machine learning algorithm
Aouini, Z., Kortebi, A., Ghamri-Doudane, Y. M., & Lahsen-Cherif, I. (2018) · IFIP Wireless Days · doi:10.1109/wd.2018.8361693 · Cited by 13 · see citing works
Incremental federated learning for traffic flow classification in heterogeneous data scenarios
Pekár, A., Makara, Á. L., & Biczók, G. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10281-4 · Cited by 12 · see citing works
On the processing time for detection of Skype traffic
del Río, P. M. S., Ramos, J., García‐Dorado, J. L., Aracil, J., Sánchez, A. C., & Cutanda‐Rodriguez, M. (2011) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc.2011.5982805 · Cited by 12 · see citing works
On different ways to classify Internet traffic : a short review of selected publications
Foremski, P. (2013) · Theoretical and Applied Informatics · Cited by 12 · see citing works
Network Traffic Analysis Using Android on a Hybrid Computing Architecture
Barbareschi, M., Mazzeo, A., & Vespoli, A. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-03889-6_16 · Cited by 11 · see citing works
Privacy-preserving deep packet filtering over encrypted traffic in software-defined networks
Lin, Y.-H., Shen, S.-H., Yang, M.-H., Yang, D.-N., & Chen, W.-T. (2016) · International Conference on Communications · doi:10.1109/icc.2016.7510993 · Cited by 11 · see citing works
Simple CART based real-time traffic classification engine on FPGAs
Soylu, T., Erdem, O., Carus, A., & Güner, E. S. (2017) · International Conference on ReConFigurable Computing and FPGAs (ReConFig) · doi:10.1109/reconfig.2017.8279820 · Cited by 11 · see citing works
Network Traffic Identification with Convolutional Neural Networks
Jain, A. V. (2018) · 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.2018.00142 · Cited by 11 · see citing works
Encrypted Traffic Classification Using Graph Convolutional Networks
Mo, S., Wang, Y., Xiao, D., Wu, W., Fan, S., & Shi, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-65390-3_17 · Cited by 11 · see citing works
Zou, A., Yang, W., Tang, C.-W., Lu, J., & Guo, J.-Y. (2024) · Computers & electrical engineering · doi:10.1016/j.compeleceng.2024.109406 · Cited by 11 · see citing works
Volunteer-Based System for Research on the Internet Traffic
Bujlow, T., Balachandran, K., Ligaard, S., Hald, N., Riaz, T., & Pedersen, J. M. (2012) · VBN Forskningsportal (Aalborg Universitet) · Cited by 10 · see citing works
Enabling High Throughput and Virtualization for Traffic Classification on FPGA
Qu, Y. R., & Prasanna, V. K. (2015) · IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM) · doi:10.1109/fccm.2015.20 · Cited by 9 · see citing works
High-throughput traffic classification on multi-core processors
Tong, D., Qu, Y. R., & Prasanna, V. K. (2014) · IEEE International Conference on High Performance Switching and Routing (HPSR) · doi:10.1109/hpsr.2014.6900894 · Cited by 9 · see citing works
- Authors
- Alshammari, R., & Zincir-Heywood, A. N.
- Affiliations
- Dalhousie University (CA)
- ORCID
- Alshammari, Riyad
- Venue
- 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications
- Publisher
- IEEE
- Published
- 2009-07
- Pages
- 1-8
- OpenAlex
- W2158215699
- Cited by
- 202
- References
- 32
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 15 · 2023: 14 · 2022: 17 · 2021: 18 · 2020: 13 · 2019: 11 · 2018: 13 · 2017: 16 · 2016: 10 · 2015: 9 · 2014: 15 · 2013: 17 · 2012: 8
- FWCI
- 5.40
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA) (publishedVersion)
- Funders
- Mitacs
- 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 › Artificial Intelligence › Wireless Signal Modulation Classification
- Keywords
- encrypted traffic classification, classifier robustness, flow-based features, C4.5 decision tree, AdaBoost, support vector machine, naive Bayes, RIPPER, network traffic analysis
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