The DOI matches its Crossref record, with a small difference in title or year; 3 of the 3 references it cites were found in a registry or an index.
FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic
Open accessAbstract
Mobile-application fingerprinting of network traffic is valuable for many security solutions as it provides insights into the apps active on a network.Unfortunately, existing techniques require prior knowledge of apps to be able to recognize them.However, mobile environments are constantly evolving, i.e., apps are regularly installed, updated, and uninstalled.Therefore, it is infeasible for existing fingerprinting approaches to cover all apps that may appear on a network.Moreover, most mobile traffic is encrypted, shows similarities with other apps, e.g., due to common libraries or the use of content delivery networks, and depends on user input, further complicating the fingerprinting process.As a solution, we propose FLOWPRINT, a semi-supervised approach for fingerprinting mobile apps from (encrypted) network traffic.We automatically find temporal correlations among destination-related features of network traffic and use these correlations to generate app fingerprints.Our approach is able to fingerprint previously unseen apps, something that existing techniques fail to achieve.We evaluate our approach for both Android and iOS in the setting of app recognition, where we achieve an accuracy of 89.2%, significantly outperforming stateof-the-art solutions.In addition, we show that our approach can detect previously unseen apps with a precision of 93.5%, detecting 72.3% of apps within the first five minutes of communication.
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“However, these methods are not applicable to the newly emerging encrypted techniques ( e.g. TLS 1.3) since the plaintext becomes more sparse or obfuscated.”
From ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification (2022) · cites this paper as [37]
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A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation
Goutte, C., & Gaussier, E. (2005) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-31865-1_25 · Cited by 2,285
Robust Smartphone App Identification via Encrypted Network Traffic Analysis
Taylor, V. F., Spolaor, R., Conti, M., & Martinovic, I. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2737970 · Cited by 439 · cited 24×
“Here, we compare our approach against AppScanner [62], a supervised state-of-theart technique to recognize apps in network traffic.”
BackgroundResultInfluentialAdaptive encrypted traffic fingerprinting with bi-directional dependence
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Sorted by citations
Items 201–300 of 381
DeepMetricCorr: Fast flow correlation for data center networks with deep metric learning
Liu, Z.-Y., Shen, D., Bao, J., Dong, F., & You, J. (2023) · Comput. Networks · doi:10.1016/j.comnet.2023.109904 · Cited by 2
Encrypted DNS Traffic Analysis for Service Intention Inferring
Zang, X., Gong, J., Wang, M., & Gao, P. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3223902 · Cited by 2
“We compare our proposal with other similar alternatives [6], [9], [11] and [14] to validate its superiority,”
MethodResultInfluentialWho is DNS serving for? A human-software perspective of modeling DNS services
Qu, J., Ma, X.-B., & Liu, W.-M. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110279 · Cited by 2
The Effect of Network Environment on Traffic Classification
Khesal, A. R., & Teimouri, M. (2022) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke57176.2022.9960138 · Cited by 2
“On the other hand, using content delivery networks (CDNs) or third-party services such as authentication and advertisement services is a common practice in many smartphone applications [4].”
BackgroundInfluentialClustering Mobile Apps based on Design and Manufacturing Genre
Sun, W., Wu, S.-Y., & Xue, Z. (2020) · 2020 IEEE 6th International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc51575.2020.9344944 · Cited by 2
“[5] creates real-time app fingerprints from the encrypted network traffic of mobile devices.”
BackgroundRaza, S. M., & Caballero, J. (2020) · arXiv.org · doi:10.48550/arxiv.2010.11627 · Cited by 2
TrafficAudio: Audio Representation for Lightweight Encrypted Traffic Classification in IoT
Chen, Y., Wang, Y., Li, R.-N., Xiao, Y.-J., Liu, L.-C., Li, J.-L., Jia, Y., & Gu, Z.-Q. (2026) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2026.3651599 · Cited by 2
“Van Ede et al. [26] proposed FlowPrint for fingerprinting mobile apps from encrypted network traffic.”
Zhao, L., Wu, C., & Yu, A. (2026) · IEEE Transactions on Networking · doi:10.1109/ton.2026.3674624 · Cited by 1
FlowRefiner: A Robust Traffic Classification Framework against Label Noise
Zhan, M.-W., Zhao, R.-J., Deng, X.-W., Xue, Z., Li, Q., Liu, Z., Cheng, G., & Xu, K. (2025) · Neural Information Processing Systems · doi:10.52202/085713-4270 · Cited by 2
“We conduct our experiments on four real-world traffic datasets: ISCXVPN (45), Cross-Platform (46), USTC-TFC (16), and Malware (47), each representing different traffic scenarios.”
Research on Encrypted Traffic Detection Based on Key Features
Chen, F.-J., Bai, J., & Gao, W. (2024) · IEEE Access · doi:10.1109/access.2023.3347806 · Cited by 2
“Van et al. [9] proposed a novel application fingerprinting method that is used to extract application fingerprints from encrypted traffic and identify the corresponding application through fingerprint matching.”
MethodThe Colossus with Feet of Clay: Debunking Encrypted Traffic Classifiers under PQC Evolution
Li, B.-Z., Meng, L.-J., Song, R., Pan, C., Pu, T.-J., Ma, Z., Jiang, Y.-P., Cui, L., & Hao, Z.-Y. (2026) · doi:10.48550/arxiv.2608.22683 · Cited by 1
New Trains, Same Rails: Fingerprinting Cryptocurrency Software via Network Requests
Muzammil, M., Starov, O., Ma, Z., & Nikiforakis, N. (2026) · 2026 IFIP Networking Conference (IFIP Networking) · doi:10.23919/ifipnetworking70592.2026.11579262 · Cited by 1
“Related research has also shown that application usage can be inferred from encrypted network traffic by analyzing packet-level metadata such as sizes and timing, with applications to mobile app identification [25], VPN detection [28], and cryptomining activity [23].”
Wang, T., Xie, X., Wang, W., Wang, C., & Cui, Y. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2605.17575 · Cited by 1
“For NetMamba+ and NetTrans+, we adopt publicly available pre-trained checkpoints trained on the Browser [11] and Kitsune [52] datasets.”
MambaNetBurst: Direct Byte-level Network Traffic Classification without Tokenization or Pretraining
Kulatilleke, G. K., Layeghy, S., Baktash, M., & Portmann, M. (2026) · arXiv.org · doi:10.48550/arxiv.2605.11034 · Cited by 1
“We compare against classical feature-based approaches (AppScanner [40], FlowPrint [41]), supervised deep learning baselines (FS-Net [42], TFE-GNN [43]), pre-trained Trans-formers (ET-BERT [2], YaTC [3]), and a Mamba-based base-line (NetMamba [7]).”
Qin, J., Han, X.-Y., Li, Y.-P., Han, D.-Q., Qiao, W., Yang, X.-B., Cui, Z.-L., Jiang, B., & Lu, Z.-G. (2026) · IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp55912.2026.11463318 · Cited by 1
VQFlow: A Benchmark Dataset for Encrypted Video Streaming Traffic across QoS Configurations
Zhao, Z., Li, Z., Li, T., & Zhang, F. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770854.3785690 · Cited by 1
“BeautyBurst [37] 86.4 71.2 BPP-NN [29] 88.1 73.5 ITP-KNN [38] 85.2 68.9 MarkovChain [39] 87.6 70.4 EVS2vec [40] 90.7 76.1 FlowPrint [41] 83.9 69.7 ET-BERT [42] 93.4 79.5 evaluates whether the content identity (title index) of a video can be inferred from encrypted traffic traces collected under…”
InfluentialGranulNet: A unified framework for traffic identification using multi-grained feature fusion
Qin, J., Han, X.-Y., Li, Y., Wang, D., Cui, S., Jiang, B., Lu, Z.-G., & Liu, B.-X. (2026) · Comput. Networks · doi:10.1016/j.comnet.2026.112120 · Cited by 1
PaT: An enhanced pretrained framework via Mamba-2 for network traffic analysis
Song, Z.-J., Ma, Z.-Y., Yu, Q.-M., Xie, X., Wang, Y.-L., & Yang, G.-Z. (2026) · Comput. Networks · doi:10.1016/j.comnet.2026.112307 · Cited by 1
Mean Masked Autoencoder with Flow-Mixing for Encrypted Traffic Classification
Liu, X., Fu, X.-W., Huang, F., & Zhang, L. (2026) · arXiv.org · doi:10.48550/arxiv.2603.29537 · Cited by 1
“4) Comparison Methods: We comprehensively evaluate and compare MMAE against diverse state-of-the-art methods, specifically including the following paradigms: • Traditional Machine Learning: AppScanner [35] and FlowPrint [37], which rely on hand-crafted statistical features for classification.”
HGTB-fusion: An encrypted traffic classification method fusing multimodal features
Kong, X.-Y., Zhao, H.-Y., & Qi, H. (2026) · Expert systems with applications · doi:10.1016/j.eswa.2025.130502 · Cited by 1
Nethira: A Heterogeneity-aware Hierarchical Pre-trained Model for Network Traffic Classification
Lin, C.-G., Zhang, W.-Y., Luo, H., Meng, X., & Zhang, Y.-J. (2026) · IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.48550/arxiv.2601.22494 · Cited by 1
“Traditional rule-based methods [3,9] classify network traffic using manually defined rules by extracting basic attributes such as protocols and port numbers.”
Gu, J., Zhong, Y., & Yu, X. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00470-7 · Cited by 3
“Statistical approaches (Taylor et al. 2016; Van Ede et al. 2020; Gu et al. 2021; Xie et al. 2021) used session-or packet-level statistics (e.g., packet length, inter-arrival time) combined with classical machine learning algorithms.”
Li, L.-X., Qi, Y.-X., Lin, X., & Li, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132290 · Cited by 1
Smartphone User Fingerprinting on Wireless Traffic
Huang, Y., Dong, Z., Yang, X., Zhang, D., Wang, Q., & Wang, Z.-H. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3628474 · Cited by 1
“However, the above techniques required direct connection to access points, routers, or switches to obtain TCP/IP layer traffic, which is difficult to achieve due to the existence of physical isolation and other protective measures.”
Liu, Y., Fu, Z., Zhan, Y., & Xia, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3552666 · Cited by 1
“1) FlowPrint [43]: FlowPrint is a semi-supervised method designed to fingerprint mobile apps based on encrypted network traffic.”
TrafficMAE: A network traffic classification model based on masked autoencoder
Zhu, W.-J., Zhang, H., Pei, C.-H., & Li, J. (2025) · ICT express · doi:10.1016/j.icte.2025.11.004 · Cited by 1
Detecting and Adapting to Stealthy Label-Inversion Drifts via Conditional Distribution Inference
Zhang, X.-L., Xiao, Y., Yin, Q., Li, Z., Wang, X., Zhang, J., Xu, K., Li, Q., & Yin, X.-C. (2025) · International Symposium on Recent Advances in Intrusion Detection · doi:10.1109/raid67961.2025.00052 · Cited by 1
Yin, J., Cui, L., Hao, Z., Sun, J., Liu, P., & Yun, X. (2025) · IEEE International Conference on Network Protocols (ICNP) · doi:10.1109/icnp65844.2025.11192383
“These features capture fundamental patterns and have shown effectiveness [5]–[7].”
Zeng, F.-Y., Man, D., Zhao, Y., Liu, Y., Wang, H.-R., & Yang, W. (2025) · Comput. Networks · doi:10.1016/j.comnet.2025.111636 · Cited by 1
Insight of Command & Control Behavior: Identifying Red-Team Traffic from Sub-flow Aspect
Wang, Q., & Yang, G. (2025) · International Symposium on Computers and Communications · doi:10.1109/iscc65549.2025.11326287 · Cited by 1
SemiAF: Semi-Supervised App Fingerprinting on Unknown Traffic via Graph Neural Network
Lei, X.-D., Wang, Y.-J., Liu, L., Huang, J.-J., Shi, J.-Y., & Yang, L. (2025) · Dependable Systems and Networks · doi:10.1109/dsn64029.2025.00051 · Cited by 1
“We compared the performance of SemiAF with FOAP [11], FG-Net [7], FS-Net [5], EVS2vec [30], Flowprint [10] on three datasets.”
InfluentialA NAT Network Host Probing Method Through NTP Traffic Analysis
Fu, D., Zhuang, Y., Chen, L., Huang, X.-B., Zhang, H., & Xue, K.-P. (2025) · ICC 2025 - IEEE International Conference on Communications · doi:10.1109/icc52391.2025.11161981 · Cited by 1
“In addition, there exist some methods or systems (such as PACKETPRINT [14], FOAP [15], AppSniffer [16] and FlowPrint [17]) for application identification.”
Identifying Synchronous and Asynchronous Communications in IMA Traffic
Thirumurugan, S., Weber, J., Alshammari, R., Zincir-Heywood, N., Nandy, B., & Seddigh, N. (2025) · IEEE/IFIP Network Operations and Management Symposium · doi:10.1109/noms57970.2025.11073614 · Cited by 1
“Ede et al. [3] proposed a semi-supervised model for finger-printing mobile applications from encrypted network traffic using various datasets.”
Gunasekara, R. (2025) · The Web Conference · doi:10.1145/3701716.3715287 · Cited by 1
ET-Former: Robust Transformer-Based Representation for Encrypted Traffic Classification
Wu, C., Zhao, L., Yu, A., Cai, L., & Meng, D. (2025) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd64889.2025.11033479 · Cited by 1
Qiu, X., Yan, G.-H., & Yin, L.-H. (2025) · Applied Sciences · doi:10.3390/app15095089 · Cited by 1
Sun, J.-W., Zhang, B., Li, H., Yuan, L., & Chang, H. (2025) · Journal of Supercomputing · doi:10.1007/s11227-025-07104-1 · Cited by 1
Encryption Traffic Classification Based on Mining Traffic Context and Transport Relationship
Gai, W., Zhang, R., Zhang, H., Guo, Y. H., Yin, J., & Zhang, P. (2025) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc61545.2025.10978781 · Cited by 1
Guo, Z., Lu, B., Wu, W., Guo, M., Liu, Z., & Lin, X. (2025) · 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE) · doi:10.1109/icaace65325.2025.11020246 · Cited by 1
Magnifier: Detecting Network Access via Lightweight Traffic-Based Fingerprints
Li, W.-H., Wang, Q., Bao, H.-F., Zhang, X.-Y., Ying, L., Li, Z.-X., Jin, H.-M., & Wang, S. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3587621 · Cited by 1
“With the growth of network tra ffi c analysis, passive inference of access behavior from network tra ffi c o ff ers a path to bypass the constraints imposed on endpoint-based meth-ods [8], [9], [10], [11], [12], [13].”
InfluentialCao, J., Xu, Z., Bai, Y., Xu, Y.-W., Xiang, Q., & Cheng, G. (2024) · International Conference on Trust, Security and Privacy in Computing and Communications · doi:10.1109/trustcom63139.2024.00146 · Cited by 1
A Reliable Encrypted Traffic Classification Method Based on Attention Mechanisms
Wu, Z.-J., Niu, S., & Yue, M. (2024) · International Conference on Trust, Security and Privacy in Computing and Communications · doi:10.1109/trustcom63139.2024.00181 · Cited by 1
“Many studies [8]–[11] involve high-dimensional feature sets that incur significant computational overhead.”
Interdisciplinary Research Project ‘AI Shield’
van Ede, T., Mulder, T., & Moyakine, E. (2024) · Cyber Security in Networking Conference · doi:10.1109/csnet64211.2024.10851746 · Cited by 1
“Moreover, the research conducted by us in the past has demonstrated that automation can assist security operators in not only monitoring [3] but also triaging of security events [4].”
Classification of Audio Streaming in Network Traffic Based on Machine Learning Methods
Nikbakht, M., & Teimouri, M. (2024) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke65377.2024.10874577 · Cited by 1
“FlowPrint, introduced in 2020 by Thijs van Ede et al. [19], is designed for fingerprinting mobile apps from encrypted network traffic.”
FG-SAT: Efficient Flow Graph for Encrypted Traffic Classification under Environment Shifts
Cui, S., Han, X.-Y., Han, D.-Q., Wang, Z., Wang, W., Li, Y., Jiang, B.-S., Liu, B.-X., & Lu, Z.-G. (2024) · arXiv.org · doi:10.48550/arxiv.2408.14122 · Cited by 1
“Ede et al. [13] highlight the use of temporal correlations among destination-related features for generating application fingerprints.”
GBERT: a hybrid BERT and GRU model for enhanced encrypted traffic classification
Zhou, Y.-C., & Yang, W.-J. (2024) · International Conference on Computer Communication and Network Security · doi:10.1117/12.3038155 · Cited by 1
“Recall F1-Score AppScanner [1] 71.82 73.39 72.25 71.97 BIND [9] 75.34 75.83 74.88 74.20 FlowPrint [10] 79.62 80.42 78.12 78.20 FS-Net [11] 72.05 75.02 72.38 71.31 Deeppacket [12] 93.29 93.77 93.06 93.21 PERT [13] 93 In this study, our GBERT model was compared with a variety of state-of-the-art…”
InfluentialSecurity Assessment of IoT Devices: A Contemporary Survey
Pathak, I., Indu, S., & Walia, G. S. (2024) · 2024 First International Conference on Electronics, Communication and Signal Processing (ICECSP) · doi:10.1109/icecsp61809.2024.10698275 · Cited by 1
Li, S.-B., Qiang, Q., Zang, T., Yang, L., & Gao, T. (2024) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc57260.2024.10570907 · Cited by 1
Review and Perspective on Encrypted Traffic Classification Based on Contrastive Learning
Li, Z., Liu, Q., & Yu, K. (2024) · International Conference Civil Engineering and Architecture · doi:10.1109/iccea62105.2024.10604038 · Cited by 1
“Usually, the requirements are diverse, including traffic engineering, network intrusion detection, network monitoring, quality of service management, routing improvement, accounting systems, etc.[2].”
BackgroundIoT Device Identification Base on Inter-flow Correlation Analysis Using Graph Neural Network
Peng, C., Tan, X., Xie, P., Li, M., Wang, H., & Chen, S.-W. (2023) · International Conference on Big Data Computing and Communications · doi:10.1109/bigcom61073.2023.00012 · Cited by 1
“In this paper, we quantify the temporal correlation between flows and flows by calculating the active time cross-correlation [3], as shown in Eq.”
BackgroundMethodEfficient Fingerprinting Attack on Web Applications: An Adaptive Symbolization Approach
Yang, X., Xu, J., & Li, G. (2023) · Electronics · doi:10.3390/electronics12132948 · Cited by 1
Automated Privacy Network Traffic Detection via Self-labeling and Learning
Li, Y., Cui, H., Sun, J.-Y., Zhang, Y., Li, Y.-Q., Meng, G., & Wang, W. (2022) · International Symposium on Computers and Communications · doi:10.1109/iscc55528.2022.9912471 · Cited by 1
“Thijs et al.[11] proposed FLOWPRINT, a semi-supervised approach for encrypted traffic identification.”
BackgroundToward Efficient Distributed Network Security: A Lightweight Multitask Traffic Analysis Framework
Fu, J.-D., Fang, J., Sun, J.-Y., Zhuang, S., Liu, Y.-L., & Lv, Z.-Q. (2026) · IEEE Transactions on Networking · doi:10.1109/ton.2025.3643832 · Cited by 1
“In all experiments, we follow common practice in traffic classification and intrusion detection [15], [18], [23], [24] and operate on fixed-length prefixes of each flow.”
InfluentialBeyond JA4+: Flow Statistics vs. TLS Fingerprinting for Encrypted Malware Detection
Lipcsey-Magyar, M. P., Madarász, A. Á., & Pekár, A. (2026) · Infocommunications journal · doi:10.36244/icj.2026.1.4 · Cited by 1
“Their analysis revealed that traditional machine learning methods often outperform deep learning while requiring fewer computational resources, with acceleration libraries providing up to 68.6× speedup.”
InfluentialSOFA: Service-Oriented Fine-Grained Attack Traffic Detection With Meta Learning
Fang, P., Huang, F.-Y., Zhao, Z.-M., Zhang, F., & Ma, W. (2026) · IEEE Transactions on Networking · doi:10.1109/ton.2025.3648394 · Cited by 1
“The models are ET-BERT [12], Kitsune [49], FOSS [8], Trident [14], HyperVision [22], FARE [19], FlowPic [67], Whisper [6], I 2 RNN [10], TrafficScope [68], few-shot learning method FC-Net [34], FS-IDS [30], CNN-MAML [25], RFS [27], RBRN [33], PBCNN [37] and KNN-Siamese [38].”
Sparse Gaussian–Markov Modeling for Robust and Trustworthy Unknown Cyber Defense
Zha, C., Liu, T., Lin, C., Bai, B., & Zhang, R. (2026) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2026.3668824 · Cited by 1
“Compared to traditional methods, AI-based approaches offer a distinct advantage in identifying unknown threats - those that lie beyond the distribution of in-distribution (ID) data [9], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29].”
FOADA: Toward Robust Open-World Mobile App Fingerprinting
Gong, J., Meng, G.-T., Cai, W., Liang, S., Wang, T., & Chang, E.-C. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3589008 · Cited by 1
“Flowprint is a semi-supervised algorithm for open-world app recognition, clustering network flows based on source and destination IP addresses [11].”
Traffic-MoE: A Sparse Foundation Model for Network Traffic Security Analysis
Zhou, J., Sun, C., Fu, W., Shen, M., Yu, S.-Q., & Xuan, Q. (2026) · arXiv.org · doi:10.48550/arxiv.2601.00357
“Additionally, to verify the effectiveness of the pre-training phase, we include two ML methods ( AppScanner [19], FlowPrint [20]) and one DL method ( FS-Net [10]) for comparison.”
UDFS: Lightweight Representation-Driven Robust Network Traffic Classification
Xian, Y., Zeng, X., Huang, M., Zhou, A., Cui, X.-Y., Liu, P., & Cui, L. (2025) · arXiv.org · doi:10.48550/arxiv.2509.11157
“…method, we select nine representative baseline models covering current mainstream approaches for comprehensive comparison: • Statistical Features : Models using statistical or IP-based features extracted from flows, e.g., AppScanner [15] (statistical features) and FlowPrint [20] (IP features).”
InfluentialLv, H., 谭开忠, Wang, S., Mo, Z., & Ding, Y. (2026) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2026.104046
Lv, H.-F., Tan, K.-Z., Zhao, F., Wang, S., & Ding, Y. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112855
NetGuise: Adversarial Traffic Generation for Early-Stage Classifier Evasion
Wu, B.-H., Divakaran, D. M., & Gurusamy, M. (2026) · doi:10.48550/arxiv.2610.27456
“Modern machine learning (ML)-based traffic classifiers (MTCs) for encrypted traffic have emerged as a dominant paradigm, operating on traffic metadata alone (e.g., packet sizes, directions, and inter-arrival times) [19, 20, 40, 52, 55].”
MAppPrint: Robust and Real-time Mobile App Fingerprinting via Traffic Behavior Correlation Graphs
Duan, T., Zhao, J.-Z., Li, J.-L., Li, R.-H., Zhang, Y., Xu, N., Wang, P.-H., & Hu, H.-B. (2026) · doi:10.48550/arxiv.2610.22148
“For example, FlowPrint [2] and MAppGraph [3] represent concurrent flows as a destination correlation graph and identify apps through a graph matching algorithm or a DGCNN [57].”
InfluentialNetCoFi: A Budget-Aware Coarse-Fine Dual-Branch Model for Encrypted Traffic Classification
Ma, Z.-Y., Yu, Q.-M., Lin, C.-G., Cui, W.-T., Song, Z.-J., & Yang, G.-Z. (2026) · doi:10.48550/arxiv.2610.17978
“Early methods such as AppScanner and FlowPrint use statistical or side-channel fingerprints for application identification [13], [14].”
Replicability: Challenging the Myth of Randomness Patterns in Encrypted Traffic
Shpakov, T., Burkhart, M., & Vanbever, L. (2026) · ACM Internet Measurement Conference (IMC) · doi:10.1145/3777912.3839783
“These features include strong identification information (SII) such as MAC and IP addresses, protocol ports, and certificate data, as well as traffic flow characteristics [4, 38] and side-channel metadata such as packet sizes [27, 30] and timing patterns [12].”
Ni, T., Sun, Z.-H., Zhou, Y.-H., Du, Y.-F., Zhao, Q.-C., & Wang, C. (2026) · doi:10.1145/3830454.3832587
ANT: A Multi-Granularity Network Traffic Dataset and Benchmark for Agents Behavior Auditing
Li, F., Gao, X.-Y., Liu, Z.-X., Li, T., Fu, C.-P., Wang, Z.-Q., & Xu, K. (2026) · doi:10.48550/arxiv.2610.06514
“Traffic analysis meth-ods already use such features to identify websites and applications and detect cyberattacks (Sirinam et al., 2018; van Ede et al., 2020; Lin et al., 2022; Wang et al., 2024; Fu et al., 2025).”
Ghosts in the Memory: Detecting Unintended Sensitive Data in Android Apps
Song, S., Kim, T., Kim, W., Park, S., Hwang, S., & Kim, H. (2026) · Proceedings of the ACM on Software Engineering · doi:10.1145/3832198
“Network-inspection approaches [6, 32, 40] are similarly limited under encryption.”
Impaler: Improving adaptability of domain fronting detection with adaptable traffic representation
Xie, Y.-B., Li, Y.-Y., Dai, X.-M., Yang, L., Ayinga, & Guan, L. (2026) · Computers & Security · doi:10.1016/j.cose.2026.105183
DCMV-FM: A dual-channel multi-view method for inner VPN tool recognition in nested encrypted tunnel
Zhang, Y., Cheng, G., Luan, X., Wang, X.-Y., & Zhang, Z. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112835
Zhao, H., & Yin, A. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112844
TrafficCross: Cross-View Multimodal Information Fusion for Encrypted Traffic Classification
Zuo, M., Cheng, Y.-N., Sun, G.-Y., Zhang, Z.-X., & Huo, X.-Y. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104884
Navigating the Latent Manifold: Proactive Concept Drift Adaptation for Resilient NIDS
Zha, C., Kang, Z.-F., Liu, T., Shen, D.-K., & Zhang, R.-Y. (2026) · doi:10.48550/arxiv.2609.08623
“Classical machine learning methods, such as clustering, decision trees, and random forests, have been applied to classify malicious traffic [19– 21, 36, 65, 67, 75], while reconstruction mechanisms and transfer learning help detect unknown attacks [37, 54, 62, 73, 77, 82].”
Li, S.-C., Yao, L.-F., Yang, X.-K., Tu, K., Wu, C.-H., Wang, H., Liu, J., Lin, P., Fu, X., & Pan, M. (2026) · doi:10.48550/arxiv.2609.06749
“In practice, the adversary can infer this via various side-channels (van Ede et al., 2020).”
Understanding the Privacy-Preserving Potential of HTTP/2 Against Webpage Fingerprinting
Cebere, B., Kumar, P., Chatel, S., Lueks, W., & Rossow, C. (2026) · doi:10.48550/arxiv.2609.05119
“Previous website fingerprinting works explored the potential of Gaussian Distributions [41], linear models [14], random forests [35], clustering methods [24, 59], and neural networks (NN) [5, 16–18, 34, 36, 57, 60, 62, 66, 73].”
Zhao, H., Shang, Y., & Yang, X. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112542
Keep Experts Diverse: A Task-Aware MoE for Multi-Task Traffic Analysis
Fu, J., Fang, J., Sun, J., Xi, L., Zhuang, S., Geng, L., Liu, Y., & Lv, Z. (2026) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2026/290
“Early studies on network traffic analysis primarily relied on conventional machine learning, where manually crafted statistical features were designed to characterize flow-level behaviors [Shao et al. , 2019; Taylor et al. , 2016; van Ede et al. , 2020].”
TrafficMoE: LLM-based mixture-of-experts framework for multi-task network traffic classification
Chen, H., Gao, D., Zhang, W., Yang, L., & Zhou, Z. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-01202-7
SoftCoS-Net: QoS-proxy-driven fused supervision for ultra-lightweight edge CoS classification
Ma, W.-Y., Lu, J., Tang, C.-W., Feng, K.-H., Wu, X., & Wu, K. (2026) · Journal of King Saud University: Computer and Information Sciences · doi:10.1007/s44443-026-01074-x
Sun, L., Guo, Y.-Y., & Muhammad, G. (2026) · IEEE transactions on consumer electronics · doi:10.1109/tce.2026.3678908
“The baseline models include: Hybrid-Falcon [15], NT-GNN(Network Tra ffi c Graph Neural Network) [27], MLP, CICModel [24], DeepAMD [28], Flow-Print [29], MappGraph(Mobile-App Graph) [8], MateGraph [30], MDADroid [31], CICModel-API call [32], Extra Trees [33], FAGnet(Family-Aware Graph neural network)…”
Towards Analyzing Network Fingerprinting from Attacker's and Defender's Perspective
Nicholson, M., Faustin, F., & Ullah, S. (2026) · 2026 Southeast Symposium on Contemporary Engineering Topics (SSCET) · doi:10.1109/sscet69323.2026.11735379
Normality in Anomaly: Rethinking Traffic Labels
Zha, C., Shen, D., Zhang, R., & Ren, K. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3688655
“…during drift events, but also offers limited improvement in model generalization when drifted data are absent. c) Baselines Selection: We select several representative baselines, including AppScanner [40], Kitsune [43], and FlowPrint [41] as classical closed-set traffic classification methods.”
InfluentialLi, Z., Si, C.-X., Zhou, Z., Cheng, Z.-Y., Sun, P.-S., & Ren, Y.-M. (2026) · Comput. Networks · doi:10.1016/j.comnet.2026.112522
Luo, X., Yan, G., Yin, L.-H., Hou, G.-P., Guo, J., Song, Y., Li, X., Wang, W.-T., & Zhu, T.-Q. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104611
GBFF: A Graph‐Based Feature Fusion Framework for Malware Detection in Encrypted Network Flows
Pathak, I., Indu, S., & Walia, G. S. (2026) · Concurrency and Computation · doi:10.1002/cpe.70853
Graph–Time IoT IDS: Requirement‐Aligned Impact Evaluation
Dubey, K., Batra, I., Bajpai, A., Malik, A., Alshara, M., Jan, T., Ahmad, N., & Khalil, R. (2026) · Engineering Reports · doi:10.1002/eng2.70975
“Even for encrypted payload data, semi-supervised fingerprinting results suggest destination-temporal features remain useful [ 23].”
Traffic-CBM: A Structurally Interpretable Multimodal Framework for Encrypted Traffic Classification
Jin, H., Chen, W.-S., Liang, S., Jia, H.-Z., Zhao, M., Jin, S., Lai, S.-N., & Yue, Y. (2026) · arXiv.org · doi:10.48550/arxiv.2606.29909
“Early studies mainly relied on observable side-channel evidence, such as statistical patterns and packet-level sequences, for encrypted traffic classification and related encrypted traffic analysis tasks [2, 12, 38, 40].”
HiLo-MAE: Efficient Pre-trained Traffic Representation for Encrypted Network Sensing
Jiang, X., Zheng, Z., Zhang, L., Liu, H., & Wang, X.-R. (2026) · Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks · doi:10.1109/secon68281.2026.11579095
EdgeFlow: Statistical Feature Modulation for Efficient Encrypted Traffic Classification
Zhou, X.-W., Li, M., Lu, Y.-F., Huang, C., & Sun, Y.-L. (2026) · IEEE International Joint Conference on Neural Network · doi:10.1109/ijcnn69854.2026.11728407
“[17] pioneered Bayesian classification using flow statistics, while subsequent works like AppScanner [7] and FlowPrint [8] utilized packet size distributions and temporal correlations for fingerprinting.”
InfluentialWang, Y., Sun, D., Wan, W., Zhao, J., Du, G., & Long, C. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112477
TrafficGraphMAE: Self-Supervised Masked Graph Autoencoders for Encrypted Traffic Classification
Meng, Z.-X., Liu, M.-Y., Shi, J.-Z., Xiong, G., Li, Z., & Gou, G.-P. (2026) · IEEE International Joint Conference on Neural Network · doi:10.1109/ijcnn69854.2026.11729768
Complementarity-Guided Neural Architecture Search for Encrypted Traffic Classification
Hu, Z.-G., Lu, Y.-D., Ma, X., Guo, L.-F., Liu, G.-Q., & Yang, K.-K. (2026) · IEEE International Joint Conference on Neural Network · doi:10.1109/ijcnn69854.2026.11729507
A Contrastive Dual-Task Framework for Few-Shot Traffic Classification in IoT Networks
Lu, Z., Chen, M., Cui, S., Zhao, B., & Zheng, Y. (2026) · Sensors · doi:10.3390/s26113471 · Cited by 1
“Additionally, FlowPrint [19] constructs mobile application fingerprints by discovering temporal correlations among destination-related features, thereby facilitating mobile application classification.”
TraceCodec: A Compiler-Backed Neural Codec for Stateful Multi-Flow Network Traffic Traces
Ding, J.-H., Zhang, X.-C., Xie, X.-H., & Liu, S.-N. (2026) · arXiv.org · doi:10.48550/arxiv.2605.29941
“…span byte-level and segment-level encoders for classification and protocol understanding (Lotfollahi et al., 2020; Wang et al., 2017; Liu et al., 2019; Xiao et al., 2022) and structured packet, flow, and graph views (Holland et al., 2021; Meng et al., 2022; van Ede et al., 2020; Huoh et al., 2023).”
NanoTG: Automated Fine-Grained Mobile Traffic Generation with LLM GUI Agents
Wang, W.-T., Zhu, Y.-J., Li, B.-Y., Li, H., & Liu, Q. (2026) · 2026 IFIP Networking Conference (IFIP Networking) · doi:10.23919/ifipnetworking70592.2026.11578977
“Acts/App App-level (coarse) [6] 2015 5 — [7] 2016 950 — [8] 2018 12 — [9] 2018 512 — [10] 2019 40 — [11] 2020 142 — [12] 2020 35 — [13] 2021 101 — [14] 2021 60 — [15] 2024 60 — [16] 2024 350 —”
PP-OpenNet: Privacy-Preserved Open Set Classification for Network Traffic
Zhang, J., Wu, L., Peng, X., Yang, Q., Liu, R., & Xu, H. (2026) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom59046.2026.11571190
“Approaches like FlowPrint [5] fingerprint header fields (e.g., IP address), which is effi-cient but intrusive and brittle against encryption.”
MLC: A Hierarchical Correlation-Based Application Traffic Identification Method
Yang, Q.-Z., Lu, B., Yang, G., Li, D., Yang, Y., & Wang, X.-Y. (2026) · International Conference on Computer Supported Cooperative Work in Design · doi:10.1109/cscwd68734.2026.11582462
FlowMatch: Few-Shot Encrypted Traffic Classification with Consistency and Confidence
Guan, X., Li, N., Wang, D., Qiao, W., Cui, Z.-L., Jiang, B., Lu, Z.-G., & Liu, B.-X. (2026) · International Conference on Computer Supported Cooperative Work in Design · doi:10.1109/cscwd68734.2026.11581588
DWTN: Need-Driven Presence Detection for Multiplexed Encrypted Traffic
Liu, L., Wei, Z.-L., Xie, W., Wen, L., Chen, Y., Wang, P., Chen, R., & Chen, S. (2026) · 2026 ICEENG International Conference for Innovations in Intelligent Computing and Cybersecurity (IICC) · doi:10.1109/iicc69623.2026.11582017
“In practice, such identification typically leverages what remains observable at the network vantage point—e.g., packet sizes, directions, and limited protocol artifacts not encrypted end-to-end [10], [13], [15], [16], [18], [19], [21].”
CoETR 2 : Complementary Packet-Based Modeling for Encrypted Traffic
Cui, X., Cui, L., Hao, Z.-Y., & Yun, X.-C. (2026) · IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp55912.2026.11462452
Metadata
- Authors
- van Ede, T., Bortolameotti, R., Continella, A., Ren, J., Dubois, D. J., Lindorfer, M., Choffnes, D., van Steen, M., & Peter, A.
- Affiliations
- University of Twente (NL)
- University of California, Santa Barbara (US)
- Northeastern University (US)
- TU Wien (AT)
- ORCID
- Venue
- Proceedings 2020 Network and Distributed System Security Symposium
- Publisher
- Internet Society
- Published
- 2020
- OpenAlex
- W3007562398
- Cited by
- 339
- References
- 51
- Citations by year
- 2026: 66 · 2025: 92 · 2024: 72 · 2023: 49 · 2022: 38 · 2021: 18 · 2020: 4
- FWCI
- 10.41
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Network and Distributed System Security Symposium (publishedVersion)
- University of Twente Research Information (publishedVersion, other-oa)
- University of Twente Research Information (submittedVersion, other-oa)
- Funders
- National Science Foundation (1909020)
- Österreichische Nationalstiftung für Forschung, Technologie und Entwicklung
- Comcast Innovation Fund
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- Bundesministerium für Verkehr, Innovation und Technologie
- Christian Doppler Forschungsgesellschaft
- Bundesministerium für Digitalisierung und Wirtschaftsstandort
- SBA Research
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Information Systems › Mobile and Web Applications
- Physical Sciences › Computer Science › Software › Advanced Malware Detection Techniques
- Keywords
- encrypted traffic, semi-supervised learning, temporal correlation, Android, iOS, network traffic analysis
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