Paper Citation Record · LEDGER

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery

As of 17 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2505.21462.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.21462 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:34:13.082015Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

verified exact1
verified fuzzy33
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation defea18c-2b17-4f34-8c9c-d1c21817e280 · outbound

This paper cites Flowlens: Enabling efficient flow classification for ml-based network security applications,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Flowlens: Enabling efficient flow classification for ml-based network security applications,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:19.651006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b7a99793-83e9-43fa-910f-ee526cb976f5 · outbound

This paper cites Encrypted tls traffic classification on cloud platforms,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Encrypted tls traffic classification on cloud platforms,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:19.525028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 05342b59-b4ca-4029-807b-6c1c6b750a60 · outbound

This paper cites A two-phase approach to fast and accurate classification of encrypted traffic,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery A two-phase approach to fast and accurate classification of encrypted traffic,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:19.397628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 23ad3e0e-7dbb-4263-857e-80dc38cb1f5e · outbound

This paper cites Packet-level open-world app fingerprinting on wireless traffic,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Packet-level open-world app fingerprinting on wireless traffic,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:19.250557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f59fe91d-2f98-4fef-881a-d9855b330237 · outbound

This paper cites Frequency domain feature based robust malicious traffic detection,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Frequency domain feature based robust malicious traffic detection,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:19.113218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3699083e-af56-46c8-bdca-450c75456a89 · outbound

This paper cites Traffic identification of tor and web-mix,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Traffic identification of tor and web-mix,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:18.943228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4d45fd0a-6c48-49b4-b397-fba45cfabc69 · outbound

This paper cites Legal issues surrounding monitoring during network research,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Legal issues surrounding monitoring during network research,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:18.783025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d19157a7-6277-45da-aee5-4dcee97f4879 · outbound

This paper cites A survey of payload-based traffic classification approaches,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery A survey of payload-based traffic classification approaches,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:18.594755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3a811e5e-6876-41de-b306-63592a630caa · outbound

This paper cites Enhancing tor’s perfor- mance using real-time traffic classification,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Enhancing tor’s perfor- mance using real-time traffic classification,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:18.408647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation da4abff0-04c4-49e7-afa9-971ddb96b6b8 · outbound

This paper cites Tor traffic analysis and detection via machine learning techniques,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Tor traffic analysis and detection via machine learning techniques,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:18.285487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b02e6fae-b012-44c3-af2b-681c708515d6 · outbound

This paper cites Anonymity services tor, i2p, jondonym: Classifying in the dark (web),.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Anonymity services tor, i2p, jondonym: Classifying in the dark (web),

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:18.103551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e6b66324-2d8d-426c-ad3d-f6d047685e79 · outbound

This paper cites Seeing traffic paths: Encrypted traffic classification with path signature features,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Seeing traffic paths: Encrypted traffic classification with path signature features,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:17.959960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 75e33a31-9362-4edb-af21-d2171b0e4f54 · outbound

This paper cites Website fingerprinting at internet scale,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Website fingerprinting at internet scale,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:17.771886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation db18766d-d619-4f43-ae28-f75344a63969 · outbound

This paper cites Online website fingerprinting: Evaluating website fingerprinting attacks on tor in the real world,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Online website fingerprinting: Evaluating website fingerprinting attacks on tor in the real world,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:17.600509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9319595a-4660-440d-86d8-885b13480ad6 · outbound

This paper cites Fs-net: A flow sequence network for encrypted traffic classification,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Fs-net: A flow sequence network for encrypted traffic classification,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:17.454469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2da6f0f1-e371-4032-921e-415acf38fd6c · outbound

This paper cites Flow sequence-based anonymity network traffic identification with residual graph convolutional networks,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Flow sequence-based anonymity network traffic identification with residual graph convolutional networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:17.298488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 24a0c45b-8f7d-4bd4-adf0-bbd772b2fba6 · outbound

This paper cites Yet another traffic classifier: A masked autoencoder based traffic transformer with multi-level flow representation,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Yet another traffic classifier: A masked autoencoder based traffic transformer with multi-level flow representation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:17.124382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1bd8a728-7d90-4ea8-92ce-793776c79d84 · outbound

This paper cites Robust multi-tab website fingerprinting attacks in the wild,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Robust multi-tab website fingerprinting attacks in the wild,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:16.975148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b2a7664b-bf87-4959-b4d1-892b2b84db6c · outbound

This paper cites snwf: Website fingerprint- ing attack by ensembling the snapshot of deep learning,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery snwf: Website fingerprint- ing attack by ensembling the snapshot of deep learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:16.661150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ce042995-0f46-46aa-b16f-5dc1d9ac6473 · outbound

This paper cites Flowprint: Semi-supervised mobile-app fingerprinting on encrypted network traffic,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Flowprint: Semi-supervised mobile-app fingerprinting on encrypted network traffic,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:16.512516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d5472846-894c-420b-a038-5bb69bececf7 · outbound

This paper cites Detecting Unknown Encrypted Malicious Traffic in Real Time via Flow Interaction Graph Analysis.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Detecting Unknown Encrypted Malicious Traffic in Real Time via Flow Interaction Graph Analysis

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:34:13.620069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 84d3bcce-dc09-4fad-89d5-b68580edcff7 · outbound

This paper cites Adawfpa: Adaptive online website fingerprinting attack for tor anonymous network: A stream-wise paradigm,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Adawfpa: Adaptive online website fingerprinting attack for tor anonymous network: A stream-wise paradigm,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:16.361847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 15c6eaaf-c342-4913-861f-9a0098cefd97 · outbound

This paper cites Autonomous unknown- application filtering and labeling for dl-based traffic classifier update,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Autonomous unknown- application filtering and labeling for dl-based traffic classifier update,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:16.192156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f8488242-9b56-4c78-aad8-080a4d11469c · outbound

This paper cites On realistically attacking tor with website fingerprinting,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery On realistically attacking tor with website fingerprinting,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:16.006895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 530d31a9-1ee3-4247-8c8e-885a785841d5 · outbound

This paper cites Triplet fingerprinting: More practical and portable website fingerprinting with n-shot learning,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Triplet fingerprinting: More practical and portable website fingerprinting with n-shot learning,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T13:34:12.402194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03544fe5-02df-44af-aca7-c37cd467d9b8 · outbound

This paper cites Gandalf: Gan for data-limited fingerprinting,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Gandalf: Gan for data-limited fingerprinting,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:15.814002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6d9bbb0b-d2d5-42ee-82dd-57307751a0fb · outbound

This paper cites Few-shot website fingerprinting attack with cluster adaptation,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Few-shot website fingerprinting attack with cluster adaptation,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:15.562568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 29fcaa04-87e4-459d-b885-0b39c2c31ee5 · outbound

This paper cites Attribute-based zero- shot learning for encrypted traffic classification,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Attribute-based zero- shot learning for encrypted traffic classification,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:15.165169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 416fc4c7-45dd-498a-ae84-f0b58364cfe7 · outbound

This paper cites Available: https://api.semanticscholar.org/CorpusID: 258297158.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Available: https://api.semanticscholar.org/CorpusID: 258297158

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:15.314192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b3a75f7a-0b96-4e5b-ae0e-bc03b49ac6b2 · outbound

This paper cites Digging into anonymous traffic: A deep analysis of the tor anonymizing network,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Digging into anonymous traffic: A deep analysis of the tor anonymizing network,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:14.409628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d8a9d906-8fd8-487e-842d-88d491d47f8b · outbound

This paper cites Conditional variational auto-encoder and extreme value theory aided two-stage learning ap- proach for intelligent fine-grained known/unknown intrusion detection,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Conditional variational auto-encoder and extreme value theory aided two-stage learning ap- proach for intelligent fine-grained known/unknown intrusion detection,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:14.110927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d0416327-9250-4ee8-8208-291063b37f0f · outbound

This paper cites Characterization of tor traffic using time based features,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Characterization of tor traffic using time based features,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:14.700046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 32329b34-1dc1-422f-a133-34e87191fb70 · outbound

This paper cites In- cremental open set intrusion recognition using extreme value machine,.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery In- cremental open set intrusion recognition using extreme value machine,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:13.863577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1ec65f7d-56a0-4aa8-91ee-91175cc0a868 · outbound

This paper cites Available: https://api.semanticscholar.org/CorpusID: 249717931.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Available: https://api.semanticscholar.org/CorpusID: 249717931

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:14.968627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8c200774-6ade-40a2-b942-af47f042b433 · outbound

This paper cites Available: https://api.semanticscholar.org/CorpusID: 260002990.

M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery Available: https://api.semanticscholar.org/CorpusID: 260002990

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:34:16.799512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.