Paper Citation Record · LEDGER

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond

As of 16 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2412.03483.

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

pith.paper-citation-record.v1
2412.03483 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:24:46.180202Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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 exact0
verified fuzzy31
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e08c9465-52e7-4614-8c41-c2721c87c185 · outbound

This paper cites A vision of 6g wireless systems: Applications, trends, technologies, and open research problems.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond A vision of 6g wireless systems: Applications, trends, technologies, and open research problems

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.748793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 691616a3-f2b8-422e-89d3-13c7a4da602c · outbound

This paper cites Ai and 6g security: Opportunities and challenges.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Ai and 6g security: Opportunities and challenges

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.732327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 35df9ea9-51cb-4588-b66b-d3ad40910a21 · outbound

This paper cites The road towards 6g: A comprehensive survey.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond The road towards 6g: A comprehensive survey

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.715475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 40031d7b-da58-4717-8cbe-bb32c8e23149 · outbound

This paper cites The roadmap to 6g security and privacy.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond The roadmap to 6g security and privacy

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.699168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8d27a898-7911-49ae-9b4f-d88f4d460832 · outbound

This paper cites Dynamic neural networks: A survey.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Dynamic neural networks: A survey

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.683498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 49f73643-bdd7-41ff-9840-3ac84a91de6c · outbound

This paper cites Jacobs, Michael I.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Jacobs, Michael I

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.667805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fc4151b0-87eb-4078-ba07-82baf4a78196 · outbound

This paper cites Expert gate: Lifelong learning with a network of experts.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Expert gate: Lifelong learning with a network of experts

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.652516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e61b7489-5595-4e10-923a-2bbca25fe264 · outbound

This paper cites A survey on mixture of experts, 2024.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond A survey on mixture of experts, 2024

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T22:24:46.056493Z

Source-reported events for the cited work

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Observation 3b2ea79a-984c-44e3-ad3b-9740da989cd3 · outbound

This paper cites Outrageously large neural networks: The sparsely-gated mixture-of-experts layer.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Outrageously large neural networks: The sparsely-gated mixture-of-experts layer

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.625933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 795a9c11-251a-4c87-9976-d614476bc54a · outbound

This paper cites 5G-NIDD: A Comprehensive Network Intrusion Detection Dataset Generated over 5G Wireless Network.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond 5G-NIDD: A Comprehensive Network Intrusion Detection Dataset Generated over 5G Wireless Network

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T22:24:46.065955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56dd5767-7677-45eb-917c-d3d5f7dd6b98 · outbound

This paper cites Nancy sns ju project - cyberattacks on o-ran 5g testbed dataset, 2024.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Nancy sns ju project - cyberattacks on o-ran 5g testbed dataset, 2024

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.610215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 962ce5a4-4035-489d-9fc3-eba351b6b6c2 · outbound

This paper cites Performance analysis of intrusion detection systems using a feature selection method on the unsw-nb15 dataset.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Performance analysis of intrusion detection systems using a feature selection method on the unsw-nb15 dataset

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.595364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 213b4368-e564-418b-a687-90f093cb7c89 · outbound

This paper cites Fusion of statistical importance for feature selection in deep neural network-based intrusion detection system.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Fusion of statistical importance for feature selection in deep neural network-based intrusion detection system

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.579979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7df4b6cf-b3d1-48d1-8996-022d594b6745 · outbound

This paper cites Real-time fusion multi-tier dnn-based collaborative idps with complementary features for secure uav-enabled 6g networks.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Real-time fusion multi-tier dnn-based collaborative idps with complementary features for secure uav-enabled 6g networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.564311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4b754edc-6337-4b7c-83b5-2a772d16ef2e · outbound

This paper cites Shamim Towhid, and Mohammad Sadegh Khosravani.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Shamim Towhid, and Mohammad Sadegh Khosravani

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.548608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7cfbd806-5c3d-4f14-807f-79c1fccb3509 · outbound

This paper cites Dos/ddos attack dataset of 5g network slicing, 2023.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Dos/ddos attack dataset of 5g network slicing, 2023

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.532710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 749b9ec7-46dd-4c4a-baf2-2f4f3070eaec · outbound

This paper cites 5g-siid: an intelligent hybrid ddos intrusion detector for 5g iot networks.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond 5g-siid: an intelligent hybrid ddos intrusion detector for 5g iot networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.517879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0189fd7f-7d62-464e-94c5-c95d07fad634 · outbound

This paper cites Early network intrusion detection enabled by attention mechanisms and rnns.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Early network intrusion detection enabled by attention mechanisms and rnns

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.502374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 760d8b50-4230-49a1-ab8f-19894ff8fe94 · outbound

This paper cites A cognitive security framework for detecting intrusions in iot and 5g utilizing deep learning.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond A cognitive security framework for detecting intrusions in iot and 5g utilizing deep learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.486667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3988d64a-2a66-4d72-bcf6-d62fab682ac0 · outbound

This paper cites Ddosnet: A deep-learning model for detecting network attacks.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Ddosnet: A deep-learning model for detecting network attacks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.471199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 64d267dc-d601-40ec-96ba-c78456c791a8 · outbound

This paper cites Ghorbani.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Ghorbani

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.455968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 54a48ffd-89e5-4b6a-a322-47ed117d0914 · outbound

This paper cites A deep learning- based malware traffic classifier for 5g networks employing protocol- agnostic and pcap-to-embeddings techniques.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond A deep learning- based malware traffic classifier for 5g networks employing protocol- agnostic and pcap-to-embeddings techniques

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.440509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 241d63b0-2529-44fc-929f-9a7d05897eea · outbound

This paper cites Gan augmentation to deal with imbalance in imaging-based intrusion detection.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Gan augmentation to deal with imbalance in imaging-based intrusion detection

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.424892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f958c412-d462-4ddc-ba16-268261eecd11 · outbound

This paper cites Real-time network packet classification exploiting computer vision ar- chitectures.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Real-time network packet classification exploiting computer vision ar- chitectures

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.409215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5884cfee-409d-45f4-868b-206709d8312b · outbound

This paper cites Wu, and Zhanbo Li.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Wu, and Zhanbo Li

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.392523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 43125294-c5af-4768-bd6d-163cd91f448c · outbound

This paper cites Manticore: An unsupervised intrusion detection system based on contrastive learning in 5g networks.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Manticore: An unsupervised intrusion detection system based on contrastive learning in 5g networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.376361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f8e81bc9-c9dd-40d2-b492-6c724c177364 · outbound

This paper cites Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T22:24:46.143624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4357fa9-6015-4e70-b034-e5e4c3e6f48a · outbound

This paper cites Real-time clustering based on deep embeddings for threat detection in 6g networks.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Real-time clustering based on deep embeddings for threat detection in 6g networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.360218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 851d6026-8d7c-43ac-9ca4-dd08d073448b · outbound

This paper cites Unsupervised deep learning approach for network intrusion detection combining convolutional au- toencoder and one-class svm.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Unsupervised deep learning approach for network intrusion detection combining convolutional au- toencoder and one-class svm

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.344499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a9d1f7f0-880a-428b-b695-981ed8cf32bc · outbound

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

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set)

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.328975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1e121a25-c181-4b26-8e6e-949663b73611 · outbound

This paper cites A detailed analysis of cicids2017 dataset for designing intrusion detection systems.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond A detailed analysis of cicids2017 dataset for designing intrusion detection systems

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.312658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bbc4ee20-e727-4112-8a2c-a9840d5e4415 · outbound

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

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Toward generating a new intrusion detection dataset and intrusion traffic characterization

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T22:24:46.166610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6c09a88-a8b1-4098-bd59-a0df7164b7a1 · outbound

This paper cites Rajalakshmi, Dinh Duc Nha Nguyen, and Yong Xiang.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond Rajalakshmi, Dinh Duc Nha Nguyen, and Yong Xiang

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.285866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 618b4cc3-136f-4904-8444-624ae41c8418 · outbound

This paper cites PyTorch: an imper- ative style, high-performance deep learning library.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond PyTorch: an imper- ative style, high-performance deep learning library

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.269648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6f106f26-1457-4045-8fd0-ca0756ec49af · outbound

This paper cites GAIN: Missing data imputation using generative adversarial nets.

Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond GAIN: Missing data imputation using generative adversarial nets

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:24:46.253316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.