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Traffic classification for managing Applications’ networking profiles
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/nem.1855openalex-snapshot
A multilevel taxonomy and requirements for an optimal traffic‐classification model
inferredcites →https://doi.org/10.1002/sec.191openalex-snapshot
Two‐stage selective sampling for anomaly detection: analysis and evaluation
inferredcites →https://doi.org/10.1002/sec.224openalex-snapshot
A generic sampling framework for improving anomaly detection in the next generation network
inferredcites →https://doi.org/10.1002/sec.564openalex-snapshot
Improving intrusion detection for imbalanced network traffic
inferredcites →https://doi.org/10.1007/978-3-319-04918-2_10openalex-snapshot
Is Our Ground-Truth for Traffic Classification Reliable?
inferredcites →https://doi.org/10.1007/978-3-540-24668-8_21openalex-snapshot
Flow Clustering Using Machine Learning Techniques
inferredcites →https://doi.org/10.1007/978-3-540-31966-5_4openalex-snapshot
Toward the Accurate Identification of Network Applications
inferredcites →https://doi.org/10.1007/978-3-540-71617-4_17openalex-snapshot
Early Recognition of Encrypted Applications
inferredcites →https://doi.org/10.1007/978-3-642-36784-7openalex-snapshot
Data Traffic Monitoring and Analysis
inferredcites →https://doi.org/10.1007/978-3-642-36784-7_6openalex-snapshot
Reviewing Traffic Classification
inferredcites →https://doi.org/10.1007/978-3-642-40316-3_29openalex-snapshot
Traffic Classification Approach Based on Support Vector Machine and Statistic Signature
inferredcites →https://doi.org/10.1007/s10796-008-9131-2openalex-snapshot
An SVM-based machine learning method for accurate internet traffic classification
inferredcites →https://doi.org/10.1016/j.comnet.2008.11.016openalex-snapshot
Efficient Application Identification and the Temporal Stability of Classification Schema
inferredcites →https://doi.org/10.1016/j.comnet.2010.12.004openalex-snapshot
Abacus: Accurate behavioral classification of P2P-TV traffic
inferredcites →https://doi.org/10.1016/j.comnet.2011.01.020openalex-snapshot
Graption: A graph-based P2P traffic classification framework for the internet backbone
inferredcites →https://doi.org/10.1016/j.comnet.2013.04.005openalex-snapshot
Toward an efficient and scalable feature selection approach for internet traffic classification
inferredcites →https://doi.org/10.1016/j.cose.2013.04.007openalex-snapshot
Botnet detection based on traffic behavior analysis and flow intervals
inferredcites →https://doi.org/10.1016/j.ins.2012.12.039openalex-snapshot
Application traffic classification at the early stage by characterizing application rounds
inferredcites →https://doi.org/10.1016/j.jnca.2012.12.020openalex-snapshot
A survey of network flow applications
inferredcites →https://doi.org/10.1016/s0004-3702(03)00079-1openalex-snapshot
Consistency-based search in feature selection
inferredcites →https://doi.org/10.1016/s0004-3702(97)00043-xopenalex-snapshot
Wrappers for feature subset selection
inferredcites →https://doi.org/10.1016/s0004-3702(97)00063-5openalex-snapshot
Selection of relevant features and examples in machine learning
inferredcites →https://doi.org/10.1016/s1005-8885(08)60158-2openalex-snapshot
Real-time feature selection in traffic classification
inferredcites →https://doi.org/10.1109/discex.2003.1194880openalex-snapshot
Detecting novel scans through pattern anomaly detection
inferredcites →https://doi.org/10.1109/glocom.2009.5425781openalex-snapshot
Using Network Motifs to Identify Application Protocols
inferredcites →https://doi.org/10.1109/icc.2008.1095openalex-snapshot
Clustering to Assist Supervised Machine Learning for Real-Time IP Traffic Classification
inferredcites →https://doi.org/10.1109/icc.2012.6364951openalex-snapshot
Identification of source applications for enhanced traffic analysis and anomaly detection
inferredcites →https://doi.org/10.1109/icccn.2012.6289207openalex-snapshot
CUTE: Traffic Classification Using TErms
inferredcites →https://doi.org/10.1109/icdcs.2009.34openalex-snapshot
High-Speed Flow Nature Identification
inferredcites →https://doi.org/10.1109/imsaa.2011.6156362openalex-snapshot
Packet tagging system for enhanced traffic profiling
inferredcites →https://doi.org/10.1109/iwcmc.2012.6314275openalex-snapshot
Internet traffic clustering with constraints
inferredcites →https://doi.org/10.1109/iwmn.2015.7322980openalex-snapshot
Traffic classification and verification using unsupervised learning of Gaussian Mixture Models
inferredcites →https://doi.org/10.1109/lcn.2005.35openalex-snapshot
Automated traffic classification and application identification using machine learning
inferredcites →https://doi.org/10.1109/lcn.2006.322122openalex-snapshot
Training on multiple sub-flows to optimise the use of Machine Learning classifiers in real-world IP networks
inferredcites →https://doi.org/10.1109/mnet.2012.6135854openalex-snapshot
Issues and future directions in traffic classification
inferredcites →https://doi.org/10.1109/noms.2008.4575130openalex-snapshot
Towards automated application signature generation for traffic identification
inferredcites →https://doi.org/10.1109/surv.2008.080406openalex-snapshot
A survey of techniques for internet traffic classification using machine learning
inferredcites →https://doi.org/10.1109/surv.2008.080408openalex-snapshot
Contention avoidance and resolution schemes in bufferless all-optical packet-switched networks: a survey
inferredcites →https://doi.org/10.1109/surv.2009.090304openalex-snapshot
A Survey on Internet Traffic Identification
inferredcites →https://doi.org/10.1109/tai.1995.479783openalex-snapshot
Chi2: feature selection and discretization of numeric attributes
inferredcites →https://doi.org/10.1109/tnet.2010.2044046openalex-snapshot
KISS: Stochastic Packet Inspection Classifier for UDP Traffic
inferredcites →https://doi.org/10.1109/tnet.2012.2187305openalex-snapshot
Timely and Continuous Machine-Learning-Based Classification for Interactive IP Traffic
inferredcites →https://doi.org/10.1109/tnet.2012.2219591openalex-snapshot
An Information-Theoretical Approach to High-Speed Flow Nature Identification
inferredcites →https://doi.org/10.1109/tnn.2006.883010openalex-snapshot
Bayesian Neural Networks for Internet Traffic Classification
inferredcites →https://doi.org/10.1109/waim.2008.30openalex-snapshot
Optimizing Traffic Classification Using Hybrid Feature Selection
inferredcites →https://doi.org/10.1111/j.1460-2466.1951.tb00098.xopenalex-snapshot
The Communications Teacher Asks Some Questions
inferredcites →https://doi.org/10.1145/1028788.1028804openalex-snapshot
Transport layer identification of P2P traffic
inferredcites →https://doi.org/10.1145/1028788.1028805openalex-snapshot
Class-of-service mapping for QoS
inferredcites →https://doi.org/10.1145/1064212.1064220openalex-snapshot
Internet traffic classification using bayesian analysis techniques
inferredcites →https://doi.org/10.1145/1071690.1064220openalex-snapshot
Internet traffic classification using bayesian analysis techniques
inferredcites →https://doi.org/10.1145/1129582.1129589openalex-snapshot
Traffic classification on the fly
inferredcites →https://doi.org/10.1145/1162678.1162679openalex-snapshot
Traffic classification using clustering algorithms
inferredcites →https://doi.org/10.1145/1177080.1177123openalex-snapshot
Unexpected means of protocol inference
inferredcites →https://doi.org/10.1145/1198255.1198257openalex-snapshot
Traffic classification through simple statistical fingerprinting
inferredcites →https://doi.org/10.1145/1242572.1242692openalex-snapshot
Identifying and discriminating between web and peer-to-peer traffic in the network core
inferredcites →https://doi.org/10.1145/1298306.1298349openalex-snapshot
Network monitoring using traffic dispersion graphs (tdgs)
inferredcites →https://doi.org/10.1145/1368436.1368445openalex-snapshot
Early application identification
inferredcites →https://doi.org/10.1145/1544012.1544023openalex-snapshot
Internet traffic classification demystified
inferredcites →https://doi.org/10.1145/1555349.1555356openalex-snapshot
Unveiling core network-wide communication patterns through application traffic activity graph decomposition
inferredcites →https://doi.org/10.1145/2713579.2713586openalex-snapshot
Application-Specific Traffic Anomaly Detection Using Universal Background Model
inferredcites →https://doi.org/10.1145/988672.988742openalex-snapshot
Accurate, scalable in-network identification of p2p traffic using application signatures
inferredcites →https://doi.org/10.17487/rfc3924openalex-snapshot
Cisco Architecture for Lawful Intercept in IP Networks
inferredcites →https://doi.org/10.4304/jcm.1.7.36-46openalex-snapshot
Identification and Analysis of Peer-to-Peer Traffic
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-030-76620-7_13openalex-snapshot
Fine-Grained Network Traffic Classification Using Machine Learning: Evaluation and Comparison
inferred← citeshttps://doi.org/10.1007/978-3-031-07012-9_51openalex-snapshot
Integrating Machine Learning Approaches in SDN for Effective Traffic Prediction Using Correlation Analysis
inferred← citeshttps://doi.org/10.1007/s11042-020-09459-4openalex-snapshot
Data representation for CNN based internet traffic classification: a comparative study
inferred← citeshttps://doi.org/10.1007/s11277-021-08435-xopenalex-snapshot
An Optimized K-means Clustering for Improving Accuracy in Traffic Classification
inferred← citeshttps://doi.org/10.1007/s12243-020-00770-7openalex-snapshot
A review on machine learning–based approaches for Internet traffic classification
inferred← citeshttps://doi.org/10.1109/access.2020.2992556openalex-snapshot
Timely Classification and Verification of Network Traffic Using Gaussian Mixture Models
inferred← citeshttps://doi.org/10.1109/comst.2018.2883147openalex-snapshot
Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey
inferred← citeshttps://doi.org/10.1109/cspa48992.2020.9068697openalex-snapshot
Applications and use Cases of Multilevel Granularity for Network Traffic Classification
inferred← citeshttps://doi.org/10.1145/3660853.3660935openalex-snapshot
Enhanced Network Traffic Classification with Machine Learning Algorithms
inferred← citeshttps://doi.org/10.14569/ijacsa.2020.0110150openalex-snapshot
An Artificial Deep Neural Network for the Binary Classification of Network Traffic
inferred← citeshttps://doi.org/10.3233/idt-210080openalex-snapshot
Performance assessment of OppNet routing protocols with real world mobility traces
inferred← citeshttps://doi.org/10.70675/2dc00879z19eez4498za572z28e37697ded4openalex-snapshot
Analyse de trafic HTTPS pour la supervision d'activités utilisateurs
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