Paper Citation Record · LEDGER

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers

As of 17 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2607.07739.

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

pith.paper-citation-record.v1
2607.07739 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-10T20:01:35.084471Z

measured 28 of 28 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T03:45:31.522130Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

27 of 27 outbound references displayed

verified exact8
verified fuzzy16
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 667a5cf5-23dd-4549-a9d5-c11dda4c4704 · outbound

This paper cites Revolutionizing Cyber Threat Detection with Large Language Models: A privacy-preserving BERT-based Lightweight Model for IoT/IIoT Devices.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Revolutionizing Cyber Threat Detection with Large Language Models: A privacy-preserving BERT-based Lightweight Model for IoT/IIoT Devices

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-07-10T20:07:33.432153Z

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 737c0cbb-258f-4afd-8836-543fa35d0261 · outbound

This paper cites Large language models for cyber security: A systematic literature review.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Large language models for cyber security: A systematic literature review

Reference 2

Resolution: verified exact; doi, observed 2026-07-10T20:07:33.353805Z

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 d5bba9f1-27a0-433d-a80e-6390a973528b · outbound

This paper cites Modeling realistic adversarial attacks against network intrusion detection systems,.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Modeling realistic adversarial attacks against network intrusion detection systems,

Reference 3

Resolution: verified exact; doi, observed 2026-07-10T20:07:33.348246Z

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 ab344bb7-acfa-4753-af9a-9e0c9cf60040 · outbound

This paper cites Evaluating and improving adversarial robustness of machine learning-based network intrusion detectors,.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Evaluating and improving adversarial robustness of machine learning-based network intrusion detectors,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:07:33.907764Z

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 91dc4c86-f0a8-47bb-8f8d-ba111fd47fa0 · outbound

This paper cites Intriguing properties of adversarial ml attacks in the problem space [extended version],.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Intriguing properties of adversarial ml attacks in the problem space [extended version],

Reference 5

Resolution: verified exact; doi, observed 2026-07-10T20:07:33.341464Z

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 a887d897-73e3-4876-b832-cdad37e5e565 · outbound

This paper cites Explaining and harness- ing adversarial examples,.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Explaining and harness- ing adversarial examples,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:07:33.911764Z

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 72618a4a-f72f-420f-ab61-52b01a4ade7d · outbound

This paper cites Towards deep learning models resistant to adversarial attacks,.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Towards deep learning models resistant to adversarial attacks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:07:33.905636Z

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 b17588c5-41b9-4c2c-86f7-b730a13dca68 · outbound

This paper cites Enhancing robustness against adversarial examples in network intrusion detection systems,.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Enhancing robustness against adversarial examples in network intrusion detection systems,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:07:33.909696Z

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 7147c57a-7249-47ba-8808-c71c46fe9d0b · outbound

This paper cites Adversarial examples in deep learning for multivariate time series regression,.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Adversarial examples in deep learning for multivariate time series regression,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:07:33.903729Z

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 9fca5e54-123b-4119-9bf0-16c291bdf033 · outbound

This paper cites TabLLM: Few-shot Classification of Tabular Data with Large Language Models.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers TabLLM: Few-shot Classification of Tabular Data with Large Language Models

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-07-10T20:07:33.438444Z

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 61902610-0b34-4359-b983-95c57aaa03a6 · outbound

This paper cites Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-07-10T20:07:33.435259Z

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 e85f859f-d3b1-457d-afcf-a4da49de4ad6 · outbound

This paper cites Delving into transferable adversarial examples and black-box attacks,.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Delving into transferable adversarial examples and black-box attacks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:07:33.932906Z

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 2a459d77-6d2e-4825-a600-24e9e4b4570f · outbound

This paper cites Black-box adversarial attacks with limited queries and information,.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Black-box adversarial attacks with limited queries and information,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:07:33.928735Z

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 43098756-c8a7-412e-bfb4-57d543a9d802 · outbound

This paper cites Hopskipjumpattack: A query-efficient decision-based attack,.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Hopskipjumpattack: A query-efficient decision-based attack,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:07:33.930549Z

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 c1f83d4f-ef33-4c50-af28-60d7b5d3386f · outbound

This paper cites Adversarial Examples Are Not Easily Detected.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Adversarial Examples Are Not Easily Detected

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-07-10T20:07:33.347946Z

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 537a1a53-2007-4494-9fb6-2abc24fe389c · outbound

This paper cites A detailed analysis of the kdd cup 99 data set,.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers A detailed analysis of the kdd cup 99 data set,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:07:33.925015Z

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 e282d713-59b2-4849-b2d3-1327649cee77 · outbound

This paper cites Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set).

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:07:33.921376Z

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 49aa5009-56ea-4cca-a34c-935e072f5db0 · outbound

This paper cites Toward generating a new intrusion detection dataset and intrusion traffic characterization,.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Toward generating a new intrusion detection dataset and intrusion traffic characterization,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:07:33.919339Z

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 f1978778-fd28-4601-8109-7c91b23c8c7e · outbound

This paper cites Available: https://api.semanticscholar.org/CorpusID: 4707749.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Available: https://api.semanticscholar.org/CorpusID: 4707749

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:07:33.923089Z

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 1a21dfb2-c73e-4608-9dc0-bd511cd63d5c · outbound

This paper cites Generating network intrusion detection dataset based on real and encrypted synthetic attack traffic,.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Generating network intrusion detection dataset based on real and encrypted synthetic attack traffic,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:07:33.926966Z

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 d8911497-f07c-447d-89c3-a8906619a045 · outbound

This paper cites RT-IoT2022 ,.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers RT-IoT2022 ,

Reference 21

Resolution: verified exact; doi, observed 2026-07-10T20:07:33.344740Z

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 61e2650a-2eaf-420b-ba84-437dc0ed35e1 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers LoRA: Low-Rank Adaptation of Large Language Models

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-07-10T20:07:33.428757Z

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 1b950cbe-fe4e-4ff3-995b-afe455e84a24 · outbound

This paper cites Learning from imbalanced data,.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Learning from imbalanced data,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:07:33.917488Z

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 81372075-166f-4c66-8fa8-c7c038f0cccb · outbound

This paper cites year =.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers year =

Reference 24

Resolution: metadata mismatch; doi, observed 2026-07-10T20:07:33.340539Z

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 ee8695a8-9e87-43a1-8ec2-f9ebc7aa13f8 · outbound

This paper cites Efficient Memory Management for Large Language Model Serving with PagedAttention , booktitle =.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Efficient Memory Management for Large Language Model Serving with PagedAttention , booktitle =

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-07-10T20:07:33.357460Z

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 9eda42a6-89cb-42ed-b187-98ec724c5545 · outbound

This paper cites Feature squeezing: Detecting adversarial examples in deep neural networks,.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Feature squeezing: Detecting adversarial examples in deep neural networks,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:07:33.915339Z

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 523e8993-0a9b-45f7-9f2d-76bf901b4670 · outbound

This paper cites Ciciot2023: A real-time dataset and benchmark for large-scale attacks in iot environment.

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers Ciciot2023: A real-time dataset and benchmark for large-scale attacks in iot environment

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:07:33.913569Z

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

Observation 20493fdc-dfd8-437e-a7d1-37ea32ae6f78 · inbound

Traffic-Aware Randomized Smoothing for LLM-Based Network Intrusion Detection cites this paper.

Traffic-Aware Randomized Smoothing for LLM-Based Network Intrusion Detection Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T03:45:31.522130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.