Robust Network Traffic Classification

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Early Online Classification of Encrypted Traffic Streams using Multi-fractal Features

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CTTGAN: Traffic Data Synthesizing Scheme Based on Conditional GAN

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Active fuzzing for testing and securing cyber-physical systems

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Microservice Fingerprinting and Classification using Machine Learning

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Robust Online Learning against Malicious Manipulation and Feedback Delay With Application to Network Flow Classification

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High performance traffic classification based on message size sequence and distribution

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Towards Unknown Traffic Identification via Embeddings and Deep Autoencoders

Zhao, S., Zhang, Y., & Sang, Y. (2019) · doi:10.1109/ict.2019.8798803 · Cited by 24

SHAPE: A Simultaneous Header and Payload Encoding Model for Encrypted Traffic Classification

Dai, J., Xu, X., Gao, H., Wang, X., & Xiao, F. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3213758 · Cited by 24

Distinguishing Between Smartphones and IoT Devices via Network Traffic

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Multidimensional behavioral profiling of internet-of-things in edge networks

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Network traffic discrimination improvement in software defined network (SDN) with deep autoencoder and ensemble method

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Sustaining the High Performance of AI-Based Network Traffic Classification Models

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Machine Learning with partially labeled Data for Indoor Outdoor Detection

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Noise-Resistant Statistical Traffic Classification

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VPN-Encrypted Network Traffic Classification Using a Time-Series Approach

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Robust network traffic identification with graph matching

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IoTTFID: An Incremental IoT Device Identification Model Based on Traffic Fingerprint

Hao, Q., & Zheng, R. (2023) · IEEE Access · doi:10.1109/access.2023.3284542 · Cited by 21

AAE-DSVDD: A one-class classification model for VPN traffic identification

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Unknown network protocol classification method based on semi-supervised learning

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SFE-GACN: A novel unknown attack detection under insufficient data via intra categories generation in embedding space

Liu, A., Wang, Y., & Li, T. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102262 · Cited by 21

Active fuzzing for testing and securing cyber-physical systems

Chen, Y., Xuan, B., POSKITT, C. M., Sun, J., & Zhang, F. (2020) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 21

EZAC: Encrypted Zero-day Applications Classification using CNN and K-Means

Li, Y., Lu, Y., & Li, S. (2021) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd49262.2021.9437716 · Cited by 20

Combine intra- and inter-flow: A multimodal encrypted traffic classification model driven by diverse features

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

Big Data Analytics for Classification of Network Enabled Devices

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A self-adaptive network traffic classification system with unknown flow detection

Jing, R., Kong, X., Lin, G., Yuan, D., & Hu, H. (2017) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/compcomm.2017.8322736 · Cited by 20

Towards Unknown Traffic Identification Using Deep Auto-Encoder and Constrained Clustering

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Towards a Profiling View for Unsupervised Traffic Classification by Exploring the Statistic Features and Link Patterns

Qin, M., Lei, K., Bai, B. B., & Zhang, G. (2019) · ACM SIGCOMM Workshop on Network Meets AI & ML (NetAI) · doi:10.1145/3341216.3342213 · Cited by 20

Privacy-preserving searchable encryption in the intelligent edge computing

Chen, Q., Fan, K., Zhang, K., Wang, H., Li, H., & Yang, Y. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.09.012 · Cited by 20

Network Traffic Classification Based on Deep Learning

Li, J., & Pan, Z. (2020) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2020.11.001 · Cited by 20

Taurus: An Intelligent Data Plane.Preprint

Swamy, T., Rucker, A., Shahbaz, M., & Olukotun, K. (2020) · arXiv (Cornell University) · Cited by 19

DFE: Deep Flow Embedding for Robust Network Traffic Classification

Wang, Z., Zhang, A., Li, H.-C., Yin, Y., Chen, W., Lam, C., Mak, P. U., Vai, M. I., Gao, Y., & Pun, S. H. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3535577 · Cited by 18

Application-aware QoS routing in SDNs using machine learning techniques

Zheng, W., Yang, M., Zhang, C., Zheng, Y., Wu, Y., Zhang, Y., & Li, J. (2021) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-021-01262-8 · Cited by 18

CMFTC: Cross Modality Fusion Efficient Multitask Encrypt Traffic Classification for Efficient Management of IIoT

Dai, J., Xu, X., Gao, H., & Xiao, F. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3279427 · Cited by 18

Multi-Task Learning for Intrusion Detection on web logs

Li, B., Lin, Y., & Zhang, S. (2017) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2017.10.011 · Cited by 18

Extending labeled mobile network traffic data by three levels traffic identification fusion

Liu, Z., Wang, R., & Tang, D. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.05.079 · Cited by 18

Efficient Traffic Classification Using Hybrid Deep Learning

Sarhangian, F., Kashef, R. F., & Jaseemuddin, M. (2021) · IEEE International Systems Conference (SysCon) · doi:10.1109/syscon48628.2021.9447072 · Cited by 18

Detecting vulnerabilities in IoT software: New hybrid model and comprehensive data analysis

Mei, H., Lin, G., Da, F., & Zhang, J. (2023) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2023.103467 · Cited by 18

Knowing the unknowns: Network traffic detection with open-set semi-supervised learning

Chen, R., Luo, L., Wang, X., Ren, B., Guo, D., & Zhu, S. F. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110630 · Cited by 17

Augmented-reality-driven medical simulation platform for percutaneous nephrolithotomy with cybersecurity awareness

Tai, Y., Wei, L., Zhou, H., Peng, J., Li, Q., Li, F., Zhang, J., & Shi, J. (2019) · International Journal of Distributed Sensor Networks · doi:10.1177/1550147719840173 · Cited by 17

ADVoIP: Adversarial Detection of Encrypted and Concealed VoIP

Addesso, P., Cirillo, M., Mauro, M. D., & Matta, V. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2922398 · Cited by 17

Consistency measure based simultaneous feature selection and instance purification for multimedia traffic classification

Wu, Z., Dong, Y., Wei, H., & Tian, W. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107190 · Cited by 17

TEST: an End-to-End Network Traffic Classification System With Spatio-Temporal Features Extraction

Zeng, Y., Qi, Z., Chen, W., & Huang, Y. (2019) · IEEE International Conference on Smart Cloud (SmartCloud) · doi:10.1109/smartcloud.2019.00032 · Cited by 17

Mobile network traffic pattern classification with incomplete a priori information

Jin, Z., Liang, Z., Wang, Y., & Meng, W. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.11.003 · Cited by 17

A Traffic Classification Method with Spectral Clustering in SDN

Xiao, P., Liu, N., Li, Y., Lü, Y., Tang, X., Wang, H., & Li, M. (2016) · International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) · doi:10.1109/pdcat.2016.089 · Cited by 16

Towards a multi‐layers anomaly detection framework for analyzing network traffic

Li, B. H., Zhang, S., & Li, K. (2016) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.3955 · Cited by 15

Explaining Class-of-Service Oriented Network Traffic Classification with Superfeatures

Chowdhury, S., Liang, B., & Tizghadam, A. (2019) · doi:10.1145/3359992.3366767 · Cited by 15

Recognizing the content types of network traffic based on a hybrid DNN-HMM model

Tan, X., Xie, Y., Ma, H., Yu, S., & Hu, J. (2019) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.06.004 · Cited by 15

A Complete Review on the Application of Statistical Methods for Evaluating Internet Traffic Usage

Cunha, V. C., Zavala, A. A. Z., Magoni, D., Inácio, P. R. M., & Freire, M. M. (2022) · IEEE Access · doi:10.1109/access.2022.3227073 · Cited by 16

A Survey on SDN and SDCN Traffic Measurement: Existing Approaches and Research Challenges

Islam, M. S., Al-Mukhtar, M., Khan, M. R. K., & Hossain, M. (2023) · Eng—Advances in Engineering · doi:10.3390/eng4020063 · Cited by 16

ExBox

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

Static malware clustering using enhanced deep embedding method

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Robust Online Learning against Malicious Manipulation with Application to Network Flow Classification

Li, Y., Liang, B., & Tizghadam, A. (2021) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom42981.2021.9488890 · Cited by 14

KeyClass : Efficient keyword matching for network traffic classification

Hubballi, N., & Khandait, P. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.12.021 · Cited by 14

Traffic classification at the radio spectrum level using deep learning models trained with synthetic data

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Protocol clustering of unknown traffic based on embedding of protocol specification

Li, J., Cheng, G., Chen, Z., & Zhao, P. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103575 · Cited by 13

Accurate Identification of Internet Video Traffic Using Byte Code Distribution Features

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Exploiting the Remote Server Access Support of CoAP Protocol

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The ascent of network traffic classification in the dark net: A survey

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Flow aggregation for SDN‐based delay‐insensitive traffic control in mobile core networks

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Landing AI on Networks: An Equipment Vendor Viewpoint on Autonomous Driving Networks

Rossi, D., & Zhang, L. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3169988 · Cited by 16

FastFlow: Early Yet Robust Network Flow Classification using the Minimal Number of Time-Series Packets

Babaria, R. J., Lyu, M., Batista, G., & Sivaraman, V. (2025) · Proceedings of the ACM on Measurement and Analysis of Computing Systems · doi:10.1145/3727115 · Cited by 17

Moving centroid based routing protocol for incompletely predictable cyber devices in Cyber-Physical-Social Distributed Systems

Shen, J., Wang, C., Wang, A., Liu, Q., & Xiang, Y. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.10.024 · Cited by 12

Applying Big Data Analytics Into Network Security: Challenges, Techniques and Outlooks

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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

A Framework for Resource-aware Online Traffic Classification Using CNN

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An Approach Based on Knowledge-Defined Networking for Identifying Heavy-Hitter Flows in Data Center Networks

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Enhancing data quality in real-time threat intelligence systems using machine learning

Rodríguez, A., & Okamura, K. (2020) · Social Network Analysis and Mining · doi:10.1007/s13278-020-00707-x · Cited by 12

Traffic classification for efficient load balancing in server cluster using deep learning technique

Punitha, V., & Mala, C. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-020-03613-3 · Cited by 12

Quality Monitoring and Assessment of Deployed Deep Learning Models for Network AIOps

Yang, L., & Rossi, D. (2021) · IEEE Network · doi:10.1109/mnet.001.2100227 · Cited by 12

Adaptive and Lightweight Network Traffic Classification for Edge Devices

Li, F., & Ye, F. (2022) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2022.3165221 · Cited by 11

Open Set Recognition for Malware Traffic via Predictive Uncertainty

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CNN for User Activity Detection Using Encrypted In-App Mobile Data

Pathmaperuma, M. H., Rahulamathavan, Y., Dogan, S., & KONDOZ, A. M. (2022) · Future Internet · doi:10.3390/fi14020067 · Cited by 11

IoT device identification based on network communication analysis using deep learning

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FlowCop: Detecting "Stranger" in Network Traffic Classification

Fu, N., Xu, Y., Zhang, J., Wang, R., & Xu, J. (2018) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn.2018.8487398 · Cited by 11

Addressing the train–test gap on traffic classification combined subflow model with ensemble learning

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

Network Traffic Classification Using Tri-training Based on Statistical Flow Characteristics

Zhao, S., Zhang, Y., & Chang, P. (2017) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom/bigdatase/icess.2017.254 · Cited by 11

Intelligent detection of vulnerable functions in software through neural embedding‐based code analysis

Zeng, P., Lin, G., Zhang, J., & Zhang, Y. (2022) · International Journal of Network Management · doi:10.1002/nem.2198 · Cited by 12

ETCC: Encrypted Two-Label Classification Using CNN

Li, Y., & Lu, Y. (2021) · Security and Communication Networks · doi:10.1155/2021/6633250 · Cited by 10

Preprocessing Method for Encrypted Traffic Based on Semisupervised Clustering

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Byte-Label Joint Attention Learning for Packet-grained Network Traffic Classification

Mao, K., Xiao, X., Hu, G., Luo, X., Zhang, B., & Xia, S. (2021) · International Workshop on Quality of Service · doi:10.1109/iwqos52092.2021.9521316 · Cited by 10

Automate Allocation of Secure Slice in Future Mobile Networks using Machine Learning

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Intelligent Traffic Classification for Detecting DDoS Attacks using SDN/OpenFlow

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Multi-Scale Convolutional Neural Networks optimized by elite strategy dung beetle optimization algorithm for encrypted traffic classification

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A Survey of Applying Reinforcement Learning Techniques to Multicast Routing

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Encrypted Network Traffic Classification with Higher Order Graph Neural Network

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Big network traffic data visualization

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Unknown Traffic Identification Based on Deep Adaptation Networks

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Robust Traffic Classification with Mislabelled Training Samples

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Zero-Day Traffic Identification

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RLCS: A Classification System Based on Random Forest and Logistic Regression for Hybrid Zero-day Traffic

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DACS: A Double-layer Application Classification Scheme for Hybrid Zero-day Traffic

Liang, Y., Wang, F., & Chen, S. (2022) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct56141.2022.10073219 · Cited by 3

An Effective Network Traffic Classification Method with Unknown Flow Detection

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