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Paper Citation Record · LEDGER

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN

As of 20 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2607.02981.

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

pith.paper-citation-record.v1
2607.02981 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T05:39:49.867395Z

measured 51 of 51 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

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Reference resolution

51 of 51 outbound references displayed

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Outbound references

Observation 9cbcb9be-c2a6-4fee-a417-200414abdede · outbound

This paper cites Explainable artificial intelligence for intrusion detection in IoT networks: A deep learning based approach,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Explainable artificial intelligence for intrusion detection in IoT networks: A deep learning based approach,

Reference 1

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Observation 1b8c92d5-5128-4da6-a3f2-21548a620fc3 · outbound

This paper cites Deep learning enabled intrusion detec- tion system for industrial IoT environment,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Deep learning enabled intrusion detec- tion system for industrial IoT environment,

Reference 2

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Observation c995b4c4-af57-4151-9eac-44b74ff7427d · outbound

This paper cites Real- time collaborative intrusion detection system in uav networks using deep learning,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Real- time collaborative intrusion detection system in uav networks using deep learning,

Reference 3

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Observation dbd5af79-41f3-423a-b6f3-af500d1c5ca1 · outbound

This paper cites Detecting zero-day attack with federated learning using autonomously extracted anomalies in IoT,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Detecting zero-day attack with federated learning using autonomously extracted anomalies in IoT,

Reference 4

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Observation 3bf1e157-f23b-4f4e-9fd1-3b1a2684660c · outbound

This paper cites Auditable and verifiable federated learning based on blockchain- enabled decentralization,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Auditable and verifiable federated learning based on blockchain- enabled decentralization,

Reference 5

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Observation 89563bb6-f983-4c69-8406-9588fc28d3d8 · outbound

This paper cites Fully decentralized multiagent commu- nication via causal inference,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Fully decentralized multiagent commu- nication via causal inference,

Reference 6

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Observation c5950547-9809-4532-8fa5-627d65c7cb92 · outbound

This paper cites Big-IDS: A decentralized multi agent reinforcement learning approach for distributed intrusion detection in big data networks,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Big-IDS: A decentralized multi agent reinforcement learning approach for distributed intrusion detection in big data networks,

Reference 7

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Observation 5da9efa0-56ae-4131-87de-ec5b91cb7bd2 · outbound

This paper cites Modern netflow network dataset with labeled attacks and detection methods,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Modern netflow network dataset with labeled attacks and detection methods,

Reference 8

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Observation eb1fb3c5-b74a-4eb1-bb23-5722c4c1e06f · outbound

This paper cites Iotforge pro: A security testbed for generating intrusion dataset for industrial iot,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Iotforge pro: A security testbed for generating intrusion dataset for industrial iot,

Reference 9

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Observation 7b4c42d3-d8bf-4f2a-9211-d188534e6f68 · outbound

This paper cites Ma- chine learning-based network intrusion detection optimization for cloud computing environments,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Ma- chine learning-based network intrusion detection optimization for cloud computing environments,

Reference 10

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Observation d5a68dd8-a84a-4223-887c-8f9a5bc2b1f8 · outbound

This paper cites Feature engineering in machine learning-based intrusion detection systems for ot networks,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Feature engineering in machine learning-based intrusion detection systems for ot networks,

Reference 11

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Observation fc6c15ac-1f02-4cb6-b922-69d58f4a5a1f · outbound

This paper cites A taxonomy of machine-learning-based intrusion detection systems for the internet of things: A survey,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN A taxonomy of machine-learning-based intrusion detection systems for the internet of things: A survey,

Reference 12

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Observation b233ebb4-eda1-4594-8d41-ce561ab2bc7e · outbound

This paper cites A robust deep learning-based approach for network traffic classification using CNNs and RNNs,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN A robust deep learning-based approach for network traffic classification using CNNs and RNNs,

Reference 13

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Observation e897efe4-2222-4d06-ab33-ad4bac135b55 · outbound

This paper cites Hybrid-cnn intrusion detection framework for can networks in connected and autonomous vehicles,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Hybrid-cnn intrusion detection framework for can networks in connected and autonomous vehicles,

Reference 14

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Observation 1c5849d5-bbb1-4729-8815-404c71bd8475 · outbound

This paper cites EE-GCN: A graph convolutional network based intrusion detection method for IIoT,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN EE-GCN: A graph convolutional network based intrusion detection method for IIoT,

Reference 15

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Observation 88db8667-d181-433f-978d-b1b666ff7a31 · outbound

This paper cites A comprehensive survey on graph neural networks,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN A comprehensive survey on graph neural networks,

Reference 16

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Observation 6d0de8c2-45ea-4dfd-8239-34b1b4e71f93 · outbound

This paper cites Network Intrusion Detection with Edge-Directed Graph Multi-Head Attention Networks.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Network Intrusion Detection with Edge-Directed Graph Multi-Head Attention Networks

Reference 17

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Observation b98a73f3-22c8-4731-982f-c112b9fbb05e · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN KAN: Kolmogorov-Arnold Networks

Reference 18

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Observation 2f6f313e-2405-4356-bf7b-cd31d0a1f80a · outbound

This paper cites Selective kernel networks,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Selective kernel networks,

Reference 19

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source=pdf_text observed=2026-07-12T05:39:49.867395Z digest=sha256:25009d07d4166e23f72e11f1f9e2b00e00c6cd73c3e00e19cf8db6e4fb2e9bc3

Observation 333bdd0a-387b-4715-8f3c-64e28cb93387 · outbound

This paper cites Activation Space Selectable Kolmogorov-Arnold Networks.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Activation Space Selectable Kolmogorov-Arnold Networks

Reference 20

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Observation 5cfc821c-5ed2-4e21-bbdf-fac10a39ecec · outbound

This paper cites A com- pendium on network and host based intrusion detection systems,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN A com- pendium on network and host based intrusion detection systems,

Reference 21

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Observation 606c31a6-dc64-450b-8bf9-3e2b3b2d0e76 · outbound

This paper cites A novel wrapped feature selection framework for developing power system intrusion detection based on machine learning methods,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN A novel wrapped feature selection framework for developing power system intrusion detection based on machine learning methods,

Reference 22

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Observation 47c558f8-5952-4251-8354-38c0a82ff7c8 · outbound

This paper cites An effective intrusion detection approach using SVM with na ¨ıve bayes feature embedding,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN An effective intrusion detection approach using SVM with na ¨ıve bayes feature embedding,

Reference 23

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Observation bdd69216-0980-4e37-8f29-20fd2439ab2f · outbound

This paper cites A naive bayesian network intrusion detection algorithm based on principal component analysis,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN A naive bayesian network intrusion detection algorithm based on principal component analysis,

Reference 24

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Observation 7afa8e7d-0617-447d-a771-f9e5404df78d · outbound

This paper cites A survey of random forest based methods for intrusion detection systems,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN A survey of random forest based methods for intrusion detection systems,

Reference 25

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Observation e79b80da-6edc-4f50-853d-6d4f617d73d4 · outbound

This paper cites Per- formance evaluation of learning models for intrusion detection system using feature selection,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Per- formance evaluation of learning models for intrusion detection system using feature selection,

Reference 26

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Observation 16d25576-440e-4e91-aff3-f8e7419810a5 · outbound

This paper cites A comprehensive intrusion detection framework using boosting algorithms,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN A comprehensive intrusion detection framework using boosting algorithms,

Reference 27

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Observation 8c15b048-627b-45ff-8c08-f304521b1345 · outbound

This paper cites Machine learning and deep learning,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Machine learning and deep learning,

Reference 28

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Observation f009a02f-c938-439a-bac0-1d4ca21f9600 · outbound

This paper cites CapsRule: Explainable deep learning for classifying network attacks,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN CapsRule: Explainable deep learning for classifying network attacks,

Reference 29

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Observation 1f877d4d-cba3-443c-bf49-2659c5f179eb · outbound

This paper cites CNN-based network intrusion detection against denial-of-service attacks,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN CNN-based network intrusion detection against denial-of-service attacks,

Reference 30

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Observation bb544b93-816d-43bf-8d96-906e0c35dae8 · outbound

This paper cites Network Intrusion Detection based on LSTM and Feature Embedding.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Network Intrusion Detection based on LSTM and Feature Embedding

Reference 31

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Observation eb069fbc-398c-4039-9348-936de2df90a5 · outbound

This paper cites Dygra-edge: A dynamic gradient-regulated attention network for real- time intrusion detection on industrial iot-edge nodes,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Dygra-edge: A dynamic gradient-regulated attention network for real- time intrusion detection on industrial iot-edge nodes,

Reference 32

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Observation 8bd3bb0f-d467-4bc6-9149-9b1b873e5b76 · outbound

This paper cites Efficient and privacy-preserving network intrusion detection based on federated learning in sdn-enabled iiot network,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Efficient and privacy-preserving network intrusion detection based on federated learning in sdn-enabled iiot network,

Reference 33

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Observation c90688ff-482f-4cc4-93f6-87c48236099d · outbound

This paper cites Adversarial attacks on neural networks for graph data,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Adversarial attacks on neural networks for graph data,

Reference 34

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Observation bbb799c0-b62d-41fd-90b5-bacbf8477490 · outbound

This paper cites Reconstructed graph neural network with knowledge distillation for lightweight anomaly detection,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Reconstructed graph neural network with knowledge distillation for lightweight anomaly detection,

Reference 35

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Observation 70cf6048-b7d5-43d0-8b2b-95cac249f231 · outbound

This paper cites Automating Botnet Detection with Graph Neural Networks.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Automating Botnet Detection with Graph Neural Networks

Reference 36

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Observation 2692ac0b-15c6-42b3-83a9-d9a107802406 · outbound

This paper cites Towards network anomaly detection using graph embedding,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Towards network anomaly detection using graph embedding,

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Observation a48c1e84-95e5-423a-86a2-6d75f2d1d7a2 · outbound

This paper cites E- GraphSAGE: A graph neural network based intrusion detection system for IoT,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN E- GraphSAGE: A graph neural network based intrusion detection system for IoT,

Reference 38

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Observation f3a75345-c265-4ae4-a458-9df8a31e967b · outbound

This paper cites Graph attention networks,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Graph attention networks,

Reference 39

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Observation 88d353be-6b60-44ff-ad48-df7e8f94dc88 · outbound

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

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Anomal-E: A self- supervised network intrusion detection system based on graph neural networks,

Reference 40

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Observation dea63cc9-afa0-4a0d-9e2f-9ab9ee5cf111 · outbound

This paper cites Deep graph infomax,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Deep graph infomax,

Reference 41

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Observation 8376b2aa-54e5-412c-92b3-eb003acb3e7d · outbound

This paper cites Hadga: Hierarchical attention-based dynamic gnn algorithm for iot botnet detection,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Hadga: Hierarchical attention-based dynamic gnn algorithm for iot botnet detection,

Reference 42

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Observation 43437f38-adf6-4f74-9e55-245832be51dd · outbound

This paper cites TS-IDS: Traffic-aware self-supervised learning for IoT network intrusion detection,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN TS-IDS: Traffic-aware self-supervised learning for IoT network intrusion detection,

Reference 43

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Observation 2c3a504d-8461-4d26-b84f-eb90aeade34c · outbound

This paper cites Applying self- supervised learning to network intrusion detection for network flows with graph neural network,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Applying self- supervised learning to network intrusion detection for network flows with graph neural network,

Reference 44

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Observation d6b8d6b1-6e5d-4966-8a47-15b7ce05f68e · outbound

This paper cites Inductive representation learning on large graphs,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Inductive representation learning on large graphs,

Reference 45

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Observation 2ac26395-91f9-49cb-ae32-5839bdac17a2 · outbound

This paper cites Large selective kernel network for remote sensing object detection,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Large selective kernel network for remote sensing object detection,

Reference 46

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Observation 050bc488-d7c2-44e5-95ff-2ca7dbf8c240 · outbound

This paper cites Channel prior convolutional attention for medical image segmentation,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Channel prior convolutional attention for medical image segmentation,

Reference 47

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Observation 1f52f460-7344-4899-816c-538aa6171fbb · outbound

This paper cites NF-NIDS: Normalizing flows for network intrusion detection systems,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN NF-NIDS: Normalizing flows for network intrusion detection systems,

Reference 48

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Observation cf6c278e-d43d-4393-9ca1-033030093f61 · outbound

This paper cites Netflow datasets for machine learning-based network intrusion detection sys- tems,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Netflow datasets for machine learning-based network intrusion detection sys- tems,

Reference 49

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Observation 5b5525a6-a62e-4b45-864c-a3f5b93b306b · outbound

This paper cites Towards a standard feature set for network intrusion detection system datasets,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Towards a standard feature set for network intrusion detection system datasets,

Reference 50

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Observation f786f782-185d-4ade-a2e8-28f3ac767bbc · outbound

This paper cites Scene: Reasoning about traffic scenes using heterogeneous graph neural networks,.

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN Scene: Reasoning about traffic scenes using heterogeneous graph neural networks,

Reference 51

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