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Paper Citation Record · LEDGER

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment

As of 17 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2502.06138.

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

pith.paper-citation-record.v1
2502.06138 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T16:40:48.556794Z

measured 29 of 29 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

29 of 29 outbound references displayed

  • verified exact5
  • verified fuzzy5
  • unresolved11
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch6

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f6ddaca4-4d83-4cb4-afd3-699af0136751 · outbound

This paper cites Buchanan,” SkipGateNet: A Lightweight CNN -LSTM hybrid model with learnable skip connections for efficient botnet attack detec - tion in IoT”, IEEE Access, vol.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Buchanan,” SkipGateNet: A Lightweight CNN -LSTM hybrid model with learnable skip connections for efficient botnet attack detec - tion in IoT”, IEEE Access, vol

Reference 1

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doi, observed 2026-08-08T16:40:48.617851Z

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.

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Observation 8a26796b-5428-46fa-8d0f-fbc7d6e21707 · outbound

This paper cites Szewczyk, and J.J Kang, Malbot - DRL: Malware botnet detection using deep reinforcement learning in IOT Net- works.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Szewczyk, and J.J Kang, Malbot - DRL: Malware botnet detection using deep reinforcement learning in IOT Net- works

Reference 2

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T16:40:48.469799Z digest=sha256:649bc9ccb32847581aa7d779bdd9bf015e0ac7bd7aeccbd23adff8580a14fae2

Observation e88fe157-ba4f-48fa-ad64-4db5f3e6ea8b · outbound

This paper cites Improving IOT security with explainable AI: Quantitative evaluation of explainability for IOT botnet detection.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Improving IOT security with explainable AI: Quantitative evaluation of explainability for IOT botnet detection

Reference 3

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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.

source=pdf_text observed=2026-08-08T16:40:48.473090Z digest=sha256:9e20a152556491b55d916ef0d3a8da79a579ba9e2cf93fe819ed4ce3a54eb6be

Observation c66d6253-70ad-4b04-bb90-0c73d0725aab · outbound

This paper cites A domain embedding model for botnet detection based on Smart Blockchain.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment A domain embedding model for botnet detection based on Smart Blockchain

Reference 4

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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.

source=pdf_text observed=2026-08-08T16:40:48.476135Z digest=sha256:f6b045ad5f9dc6b63635931be20fee5149b9ee3eccef6ae5c97f0b4be3996364

Observation a12f98d0-d43f-4ad7-bb1b-2a123cb3f108 · outbound

This paper cites Big Data Mining and Analytics, vol.7, no.2, pp.500 –511, April 2024, https://doi.org/10.26599/bdma.2023.9020027.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Big Data Mining and Analytics, vol.7, no.2, pp.500 –511, April 2024, https://doi.org/10.26599/bdma.2023.9020027

Reference 5

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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.

source=pdf_text observed=2026-08-08T16:40:48.479326Z digest=sha256:0e2c85f7d734e71b74c1a522f3c443fb54432fc05a424e080fdee19e54244784

Observation 25d6b031-3e91-4084-9364-ee6fa6aa9259 · outbound

This paper cites Bao and E.Dutkiewicz,”Constrained twin variational auto-encoder for intrusion detection in IOT Systems”.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Bao and E.Dutkiewicz,”Constrained twin variational auto-encoder for intrusion detection in IOT Systems”

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T16:40:48.482441Z digest=sha256:02270f23dcbd0d062ba8ec73fc1ab54bab3b1c70442539fb53d6858a28f2f951

Observation 1fcf1e8b-8fc2-4cca-b165-42eb4cb136b5 · outbound

This paper cites an unresolved cited work.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Unresolved cited work

Reference 7

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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.

source=pdf_text observed=2026-08-08T16:40:48.485633Z digest=sha256:80081a90df2b6f9460bf403c7913c84e5fa9a673765e94cb5bd005e0c2b5cf0d

Observation 3eb9918d-bd10-4e2d-8c9a-34ddc42946ae · outbound

This paper cites an unresolved cited work.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Unresolved cited work

Reference 8

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source=pdf_text observed=2026-08-08T16:40:48.488493Z digest=sha256:e2f70223c9f0fa44fc2afb4894b88a92020c2f5be5494395f95574403162da03

Observation ec36f558-2520-45e1-afdf-ea4ee3a96519 · outbound

This paper cites an unresolved cited work.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Unresolved cited work

Reference 9

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source=pdf_text observed=2026-08-08T16:40:48.491164Z digest=sha256:8ba2cdee32571c44df926396c844223bf60c4643d5e68b7e7f28d649b6f8101f

Observation 776afc64-2631-4617-a303-8986e85cc864 · outbound

This paper cites an unresolved cited work.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Unresolved cited work

Reference 10

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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.

source=pdf_text observed=2026-08-08T16:40:48.493929Z digest=sha256:d77420e764ba241b7c05d3dc062fb0cc19ac0d47af76c4e726f933bc728491fe

Observation c6e004ca-3619-4bf3-95c2-b4e8e7a89c1c · outbound

This paper cites Raza, H.Nazari and M.Almutiry.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Raza, H.Nazari and M.Almutiry

Reference 11

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source=pdf_text observed=2026-08-08T16:40:48.496860Z digest=sha256:0e240d88d0ad0bc8713907c38ac2766073b3f0f74957e37a1bfb028924eb3ded

Observation 2bc1b72f-faaa-44a0-a909-526e307ff2be · outbound

This paper cites Nugraha, A.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Nugraha, A

Reference 12

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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.

source=pdf_text observed=2026-08-08T16:40:48.499681Z digest=sha256:599b36fac7b4fd0cc36feeb5619ca66fb1061728b933fa8eb069edb4ad6b6bd6

Observation 60c31b1e-439e-462e-877e-4304ad6da9d4 · outbound

This paper cites Karunakaran,” Deep learning approach to DGA cla ssification for effective cyber security”, J.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Karunakaran,” Deep learning approach to DGA cla ssification for effective cyber security”, J

Reference 13

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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.

source=pdf_text observed=2026-08-08T16:40:48.502443Z digest=sha256:99fff64d9a7322c687b5a19f5badbc9dc9fc1b36f5d9fd521c69204cfb53c622

Observation f22b2e77-37c8-467f-87b5-b3e2a0329af5 · outbound

This paper cites W.Wardhani, D.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment W.Wardhani, D

Reference 14

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metadata mismatch
raw_fallback, observed 2026-08-08T16:40:49.680519Z

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.

source=pdf_text observed=2026-08-08T16:40:48.505126Z digest=sha256:3c2a0b88807dbe8e02be9922c12c3e42b7d65d5c513be174a88aec1aaf446b16

Observation 20d581f2-6c60-4e91-aa9a-64a94a1edb44 · outbound

This paper cites Salama, I.Yaseen, and A.A.Alneil, ”Hybrid metaheuristics with machine learning based botnet detection in cloud assisted internet of things environment”.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Salama, I.Yaseen, and A.A.Alneil, ”Hybrid metaheuristics with machine learning based botnet detection in cloud assisted internet of things environment”

Reference 15

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source=pdf_text observed=2026-08-08T16:40:48.507956Z digest=sha256:22141834962bf053bca02db2e0ba7a7b180105004a1d68631c67ac11250ff6ad

Observation 3c57aa99-a885-43e6-b097-3c490f13d52a · outbound

This paper cites IEEE Access, vol.9, pp.141154 –141166,2021 https://doi.org/10.1109/access.2021.3119575.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment IEEE Access, vol.9, pp.141154 –141166,2021 https://doi.org/10.1109/access.2021.3119575

Reference 16

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source=pdf_text observed=2026-08-08T16:40:48.510781Z digest=sha256:4ae2f50c633127d8e96b95ce6f43a6c23bfe08e6f38dcd230e0efc2d6a4b11ea

Observation 07f31556-084f-4402-951d-652797530468 · outbound

This paper cites Wu,” Network intrusion detection combined hybrid sampling with deep hierarchical network.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Wu,” Network intrusion detection combined hybrid sampling with deep hierarchical network

Reference 17

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source=pdf_text observed=2026-08-08T16:40:48.513515Z digest=sha256:02fbe2823c6977e0d9f431fc834afa31a60f79b0e978d7a420079a0c1a831a21

Observation 86d486c1-0a83-408a-9abd-16acdc41b3fa · outbound

This paper cites Habaebi,M.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Habaebi,M

Reference 18

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source=pdf_text observed=2026-08-08T16:40:48.516403Z digest=sha256:8a527894605ae3db43ab3f71529926c0f7668a8c15c081b7623270d955b32a1c

Observation 95aa7278-0964-4c5a-8f37-0565f534a291 · outbound

This paper cites Jabbar,A.S.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Jabbar,A.S

Reference 20

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verified exact
doi, observed 2026-08-08T16:40:48.598656Z

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.

source=pdf_text observed=2026-08-08T16:40:48.522663Z digest=sha256:b5a848257670485b9fc7450882456f2710dbd19a0537fe3f848c81e8ce97accb

Observation 4d303bff-f8cf-4666-b84f-a6c61a3b1bb4 · outbound

This paper cites Sriram,P.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Sriram,P

Reference 21

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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.

source=pdf_text observed=2026-08-08T16:40:48.525988Z digest=sha256:ab71af0b2f973877f95ba5b7779885ac6ac4619bab2be127d275bda43efb14fe

Observation 7ed235a4-2eb6-4798-9a6f-0e4a8584527e · outbound

This paper cites Moustafa and J.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Moustafa and J

Reference 22

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source=pdf_text observed=2026-08-08T16:40:48.529464Z digest=sha256:7095c6acd82ad1a3ef0acdfb2bb52f29ccaebf075d35342cb20085d1a69570e3

Observation 60457431-c1ca-4047-a5b4-174600f22a43 · outbound

This paper cites Zeeshan, Q.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Zeeshan, Q

Reference 23

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source=pdf_text observed=2026-08-08T16:40:48.532773Z digest=sha256:d5b4cff41a65c4e2e52b3f5247897358c60df008708dba3260d1be404efccdc7

Observation 9422b8c0-b918-44d7-9636-bd59616c56ec · outbound

This paper cites Ahmad, Q.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Ahmad, Q

Reference 24

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

source=pdf_text observed=2026-08-08T16:40:48.536184Z digest=sha256:92cbfb2c60d8b1f7ab4fcba98e891004796727be85dad50086624e666a8401ba

Observation e7a92dd2-116d-4cf4-ad38-385c86d7e302 · outbound

This paper cites an unresolved cited work.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Unresolved cited work

Reference 25

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

source=pdf_text observed=2026-08-08T16:40:48.539875Z digest=sha256:793e9ad1cf4616b92b3fcf95b43bf17c520e5423892ff8a4610b3358101de6ad

Observation 8f1893e0-cc11-47d2-9089-44fba1bcd657 · outbound

This paper cites Lopez-Martin, B.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Lopez-Martin, B

Reference 26

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

source=pdf_text observed=2026-08-08T16:40:48.543093Z digest=sha256:de838f1f7c3cb77769f6badf9c16508abc54e22bbb06c303afe94777d62d8cdb

Observation 6e56bd18-2d32-40e8-b57a-2467866cb0b0 · outbound

This paper cites an unresolved cited work.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Unresolved cited work

Reference 27

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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.

source=pdf_text observed=2026-08-08T16:40:48.546799Z digest=sha256:8c2106ee27801db8469522245349c3d26c2af7474b37500dfd6cb20ff61445cb

Observation b5e665d9-6b35-4845-b677-8e1f92621631 · outbound

This paper cites Halbouni, T.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Halbouni, T

Reference 28

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raw_fallback, observed 2026-08-08T16:40:50.746489Z

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.

source=pdf_text observed=2026-08-08T16:40:48.550248Z digest=sha256:831f2a4ef8de252dd3958a9e124395f4db9827fc8a776d0f4079a490a648e589

Observation b9386073-e1b0-4121-9b40-a15e181971c1 · outbound

This paper cites Enhancing IoT Security: A Machine Learning Approach to Intrusion Detection System Evaluation,.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment Enhancing IoT Security: A Machine Learning Approach to Intrusion Detection System Evaluation,

Reference 29

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raw_fallback, observed 2026-08-08T16:40:48.751245Z

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.

source=pdf_text observed=2026-08-08T16:40:48.553536Z digest=sha256:d893acf0632d630207485a62737da003c7bda4c902e66dbdde9093958a467fdd

Observation 2fddc07c-30c8-42bc-8ee0-109a9af97dbc · outbound

This paper cites IoT Guardian: An Intelligent Framework for Multi -Class Intrusion Detection with Machine Learning.

Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in IoT Environment IoT Guardian: An Intelligent Framework for Multi -Class Intrusion Detection with Machine Learning

Reference 30

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raw_fallback, observed 2026-08-08T16:40:50.735614Z

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.

source=pdf_text observed=2026-08-08T16:40:48.556794Z digest=sha256:be57096e08c77d19879e1afaffb0de75eb2cfc4ec947f70e64b1f227fcac5b2f

Pith citing papers

No inbound Pith citation observations are available.