CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection | Paper Citation Record

Observation 5076cc57-e8f8-48e9-b9d5-4fe9ea3cd627 · outbound

This paper cites Unleashing the power of iot: A comprehensive review of iot applications and future prospects in healthcare, agriculture, smart homes, smart cities, and industry 4.0,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Unleashing the power of iot: A comprehensive review of iot applications and future prospects in healthcare, agriculture, smart homes, smart cities, and industry 4.0,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.409994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c84e3918-2e23-4156-a9b4-f735fa2f0c41 · outbound

This paper cites Security and privacy for low power iot devices on 5g and beyond networks: Challenges and future directions,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Security and privacy for low power iot devices on 5g and beyond networks: Challenges and future directions,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.400095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cd4d6438-136f-4dfa-bde2-4e48b581652b · outbound

This paper cites A survey on intelligent internet of things: Applications, security, privacy, and future directions,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection A survey on intelligent internet of things: Applications, security, privacy, and future directions,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.389611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0602d6a8-7866-4918-880b-356570e041db · outbound

This paper cites Rigorous evaluation of machine learning-based intrusion detection against adversarial attacks,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Rigorous evaluation of machine learning-based intrusion detection against adversarial attacks,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.378792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dd34a687-980c-4e12-9664-3dda7445d029 · outbound

This paper cites Roldef: Robust layered defense for intrusion detection against adversarial attacks,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Roldef: Robust layered defense for intrusion detection against adversarial attacks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.368082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 053a329c-594a-4fcb-8b22-2da95cb74718 · outbound

This paper cites A survey on deep learning for cybersecurity: Progress, challenges, and opportunities,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection A survey on deep learning for cybersecurity: Progress, challenges, and opportunities,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.357822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3dae6245-b1c6-4869-885e-2f99fd81c4f9 · outbound

This paper cites Online self-supervised deep learning for in- trusion detection systems,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Online self-supervised deep learning for in- trusion detection systems,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.347358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e28927d9-701d-4b63-8bb2-dc932800555a · outbound

This paper cites Anomal-e: A self- supervised network intrusion detection system based on graph neural networks,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Anomal-e: A self- supervised network intrusion detection system based on graph neural networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.338021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 073ad83d-a32e-4be4-8e58-04868c45164c · outbound

This paper cites Contrastive learning enhanced intrusion detection,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Contrastive learning enhanced intrusion detection,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.328592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4ea1ffc3-beb3-4334-ab78-a81a6585a9c2 · outbound

This paper cites Ts-ids: Traffic-aware self-supervised learn- ing for iot network intrusion detection,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Ts-ids: Traffic-aware self-supervised learn- ing for iot network intrusion detection,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.319170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c27727ab-0afa-4412-85b7-0c70ef80992d · outbound

This paper cites Intrusion detection in the iot under data and concept drifts: Online deep learning approach,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Intrusion detection in the iot under data and concept drifts: Online deep learning approach,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.309400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d854becc-8600-4098-bda5-ee356e14e940 · outbound

This paper cites Continual Learning: Applications and the Road Forward.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Continual Learning: Applications and the Road Forward

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T15:52:55.521306Z

Source-reported events for the cited work

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Observation fc884837-d80c-416b-b097-a4b09768accb · outbound

This paper cites A continual learning survey: Defying forgetting in classification tasks,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection A continual learning survey: Defying forgetting in classification tasks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.299894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 63c48845-89e5-4e57-95e6-167c121fafaa · outbound

This paper cites A comprehensive survey of continual learning: theory, method and application,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection A comprehensive survey of continual learning: theory, method and application,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.289290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bf576049-7c1e-4dad-938e-2b1aeaa245d9 · outbound

This paper cites On handling class imbalance in continual learning based network intrusion detection systems,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection On handling class imbalance in continual learning based network intrusion detection systems,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.279380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a5410105-cd3b-4605-a480-88f388e175ce · outbound

This paper cites Lifelong continual learning for anomaly detection: New challenges, perspectives, and insights,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Lifelong continual learning for anomaly detection: New challenges, perspectives, and insights,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.269632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4ee36a75-7183-4123-9168-b7e84ec97953 · outbound

This paper cites Learning without forgetting: A new framework for network cyber security threat detection,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Learning without forgetting: A new framework for network cyber security threat detection,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.259740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f4937714-b6eb-4417-8439-d6565d38f220 · outbound

This paper cites Continual Learning with Strategic Selection and Forgetting for Network Intrusion Detection.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Continual Learning with Strategic Selection and Forgetting for Network Intrusion Detection

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:52:55.897619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7c1f2068-0f91-4ac8-8196-86f0cf12a015 · outbound

This paper cites Analysis of continual learning models for intrusion detection system,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Analysis of continual learning models for intrusion detection system,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.249945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fbde4a57-53cc-4114-ae92-281800db62d1 · outbound

This paper cites Augmented memory replay-based continual learning approaches for network intrusion detection,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Augmented memory replay-based continual learning approaches for network intrusion detection,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.239305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3845ea8d-fcda-4470-a7ed-45cf44d56a60 · outbound

This paper cites CND-IDS: Continual Novelty Detection for Intrusion Detection Systems.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection CND-IDS: Continual Novelty Detection for Intrusion Detection Systems

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:52:55.881101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4524231f-7b3f-48b0-96cc-ac9874ea1762 · outbound

This paper cites Vlad: Task-agnostic vae-based lifelong anomaly detec- tion,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Vlad: Task-agnostic vae-based lifelong anomaly detec- tion,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.228886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c7de9d93-d6dc-4a8c-80a7-465a93a2dfda · outbound

This paper cites Securing constrained iot systems: A lightweight machine learning approach for anomaly detection and prevention,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Securing constrained iot systems: A lightweight machine learning approach for anomaly detection and prevention,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.219075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7268925f-e428-4ea8-a46e-ffc078b3ec86 · outbound

This paper cites Intrusion detection systems for the internet of thing: a survey study,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Intrusion detection systems for the internet of thing: a survey study,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.209305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2a02ff24-4d0d-4077-a68d-66db3d123827 · outbound

This paper cites Deep learning for intrusion detection and security of internet of things (iot): current analysis, challenges, and possible solutions,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Deep learning for intrusion detection and security of internet of things (iot): current analysis, challenges, and possible solutions,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.199492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 477767e6-cc0e-44e9-a789-6988fbd698aa · outbound

This paper cites Dynamite: Dy- namic defense selection for enhancing machine learning-based intrusion detection against adversarial attacks,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Dynamite: Dy- namic defense selection for enhancing machine learning-based intrusion detection against adversarial attacks,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T15:52:55.570896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edf4f616-fc53-47ac-813f-1bd43e2c81be · outbound

This paper cites Testing the performance of Multi-class IDS public dataset using Supervised Machine Learning Algorithms.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Testing the performance of Multi-class IDS public dataset using Supervised Machine Learning Algorithms

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:52:55.865015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 15862af0-bb64-400f-b2ec-09833c5a85c2 · outbound

This paper cites Towards model generalization for intrusion detec- tion: Unsupervised machine learning techniques,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Towards model generalization for intrusion detec- tion: Unsupervised machine learning techniques,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.182956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4ab6944f-0f68-44dd-b3c3-b2aeec629d5f · outbound

This paper cites En- hancing iot network security: Unveiling the power of self-supervised learning against ddos attacks,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection En- hancing iot network security: Unveiling the power of self-supervised learning against ddos attacks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.173426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1bfb5a08-daee-4d2e-9948-da5f7d31febf · outbound

This paper cites SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:52:55.848079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 81908c5f-057e-431f-a0ae-77e1a74a36cb · outbound

This paper cites A Cookbook of Self-Supervised Learning.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection A Cookbook of Self-Supervised Learning

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T15:52:55.589379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5b42fc4-a0fe-444b-8b95-9ae05b1a3e7d · outbound

This paper cites Self-supervised learning for anomaly detection in iot networks,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Self-supervised learning for anomaly detection in iot networks,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.163541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bdc3a2cd-194d-456c-983a-a926bb13a274 · outbound

This paper cites Malicious traffic identification with self-supervised contrastive learning,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Malicious traffic identification with self-supervised contrastive learning,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.153549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d9768d1e-4223-4474-abb4-474d864d8876 · outbound

This paper cites Robust unsuper- vised network intrusion detection with self-supervised masked context reconstruction,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Robust unsuper- vised network intrusion detection with self-supervised masked context reconstruction,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.143981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f58393d5-585e-4789-9c2c-27d127294bf8 · outbound

This paper cites A review of local outlier factor algorithms for outlier detection in big data streams,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection A review of local outlier factor algorithms for outlier detection in big data streams,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-05T15:52:55.603571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06af5f1a-b5ca-469a-927f-bd86ef228cdd · outbound

This paper cites Isolation forest based anomaly detection: A systematic literature review,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Isolation forest based anomaly detection: A systematic literature review,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.128412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 107eb38e-9ae5-49b9-9dd9-53ec380ab85b · outbound

This paper cites Deep isolation forest for anomaly detection,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Deep isolation forest for anomaly detection,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.119191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b43d8c36-d59f-442a-b275-c3fc1b8402ff · outbound

This paper cites Mitigating catastrophic forgetting in online continual learning by mod- eling previous task interrelations via pareto optimization,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Mitigating catastrophic forgetting in online continual learning by mod- eling previous task interrelations via pareto optimization,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.108924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5b1a60cb-4024-4ed2-889f-0f7415c5392e · outbound

This paper cites A multi-class intrusion detection system based on continual learning,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection A multi-class intrusion detection system based on continual learning,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.098744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 40cbbb0e-fbc6-4a48-a039-6dc5bea6d3a7 · outbound

This paper cites Aug- mented memory replay-based continual learning approaches for network intrusion detection,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Aug- mented memory replay-based continual learning approaches for network intrusion detection,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.088246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4613d9a5-b6ce-4b12-9693-af387f5cd432 · outbound

This paper cites Unsupervised continual learning in streaming environments,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Unsupervised continual learning in streaming environments,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.077615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4a55d717-bd63-45d6-b2cd-72ff71d0d92b · outbound

This paper cites Efficient mae towards large-scale vision transformers,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Efficient mae towards large-scale vision transformers,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.065516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bbbb946b-a617-4012-85b0-04255acad1a1 · outbound

This paper cites Feature selection using principal component analysis,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Feature selection using principal component analysis,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.054011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation edec1736-c424-46ff-bcf6-305ae170a927 · outbound

This paper cites Deepinsight- convolutional neural network for intrusion detection systems,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Deepinsight- convolutional neural network for intrusion detection systems,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.042361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 660745ba-9db4-40c1-84bb-5b3092d3cc3d · outbound

This paper cites Visualizing data using t-sne.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Visualizing data using t-sne

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-05T15:52:55.639498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f5b9627-90c3-43ed-88ab-2edcdc4320c7 · outbound

This paper cites Outlier detection using isolation forest and local outlier factor,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Outlier detection using isolation forest and local outlier factor,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-05T15:52:55.642793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fea05d63-e6a9-45cc-8826-f5d306733e16 · outbound

This paper cites Lifelong continual learning for anomaly detection: New challenges, perspectives, and insights,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Lifelong continual learning for anomaly detection: New challenges, perspectives, and insights,

Reference 47

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T15:52:55.820938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2a62fac3-8295-4636-9b35-47ccd0cbd414 · outbound

This paper cites Kolmogorov–smirnov test: Overview,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Kolmogorov–smirnov test: Overview,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.019523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f0242bf8-7149-4a91-87b6-73eaaad03e6d · outbound

This paper cites Mqttset, a new dataset for machine learning techniques on mqtt,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Mqttset, a new dataset for machine learning techniques on mqtt,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:56.009539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 00ee16c6-5841-4251-adf4-6a6c2fa3b074 · outbound

This paper cites Wustl-iiot-2021 dataset for iiot cybersecurity research,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Wustl-iiot-2021 dataset for iiot cybersecurity research,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:55.999020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a682b9ea-98c8-4293-97de-b09688c1f34a · outbound

This paper cites X-iiotid: A connectivity-agnostic and device- agnostic intrusion data set for industrial internet of things,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection X-iiotid: A connectivity-agnostic and device- agnostic intrusion data set for industrial internet of things,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:55.987255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4d6b0154-93c6-4471-b626-2facdd54b95a · outbound

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

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set),

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T15:52:55.663025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 931641a0-dd61-49ed-ba2a-1bf36ed91bd8 · outbound

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

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Toward generating a new intrusion detection dataset and intrusion traffic characterization,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:55.968657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 23dfe76a-f5e3-4155-aae3-ee31018f19ac · outbound

This paper cites Isolation forest,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Isolation forest,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-05T15:52:55.669632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c461c669-815c-4504-a546-7430873ffc85 · outbound

This paper cites Scalable and interpretable one-class svms with deep learning and random fourier features,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Scalable and interpretable one-class svms with deep learning and random fourier features,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:55.950054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b3f3d94c-a1a9-4552-a565-6b0db555721f · outbound

This paper cites Fascinating supervisory signals and where to find them: Deep anomaly detection with scale learning,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Fascinating supervisory signals and where to find them: Deep anomaly detection with scale learning,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:55.940385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation be29106d-88d2-424f-84b8-431ca4c79c68 · outbound

This paper cites Anomaly detection for tabular data with internal contrastive learning,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Anomaly detection for tabular data with internal contrastive learning,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:55.930385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 029a2928-56e5-48aa-b506-b173d7a3c7fd · outbound

This paper cites Rca: A deep collaborative autoencoder approach for anomaly detection,.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Rca: A deep collaborative autoencoder approach for anomaly detection,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:55.919716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 97d4871e-a981-4d52-bbbd-442a2072ba18 · outbound

This paper cites Unsupervised Representation Learning by Predicting Random Distances.

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection Unsupervised Representation Learning by Predicting Random Distances

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-05T15:52:55.685999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers