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Paper Citation Record · LEDGER

ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection

As of 13 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2608.03250.

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

pith.paper-citation-record.v1
2608.03250 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:33:14.647561Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

14 of 14 outbound references displayed

  • verified exact2
  • verified fuzzy10
  • unresolved2
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 68672b51-5490-4089-b5df-eb38e6ecccbb · outbound

This paper cites SeqMobile: A Sequence Based Efficient Android Malware Detection System Using RNN on Mobile Devices.

ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection SeqMobile: A Sequence Based Efficient Android Malware Detection System Using RNN on Mobile Devices

Reference 1

Resolution
verified exact
local_arxiv, observed 2026-08-05T22:33:14.795476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T22:33:14.589631Z digest=sha256:471eddbcdb72206292527726e2dac0f43de61d6fcf30a1e333ffd775bbc449c8

Observation e334d340-fda0-4776-962a-d65a7eef1280 · outbound

This paper cites Hendricks, Study.com — Take Online Courses.

ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection Hendricks, Study.com — Take Online Courses

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:33:14.967006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T22:33:14.595536Z digest=sha256:e2cf4e05f1e14f7a0055fbd775cb8b4101ed51cbdde31aebe765b1d8765b6bd8

Observation 37d8cd3f-ebf8-4f77-b0c4-2e707fcbf640 · outbound

This paper cites Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection.

ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-05T22:33:14.599998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:33:14.599998Z digest=sha256:3886e512e5b30ff1486a414803664b544738eadff5975b70ecd55dc887433fb8

Observation ec7919f6-1b00-4acd-8482-b25fbb070879 · outbound

This paper cites Palmer, Mobile malware attacks are booming in 2019: These are the most common threats , Jul.

ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection Palmer, Mobile malware attacks are booming in 2019: These are the most common threats , Jul

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:33:14.952498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T22:33:14.604660Z digest=sha256:5c1445acf83b85beb384e36403ee91408f0a8298bb776dc05ba4d0c978c2b723

Observation 097dcdb8-8123-4e9a-b1f9-5fde0503bae8 · outbound

This paper cites an unresolved cited work.

ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection Unresolved cited work

Reference 5

Resolution
unresolved
raw_fallback, observed 2026-08-05T22:33:14.938699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T22:33:14.609378Z digest=sha256:9a02368bb20b8c17f3ce7ea0385a9849637ca3e52fac67fc61ca6c3e31476ea7

Observation 0b851ba3-dda2-4dee-acf2-aa0743a3dc2e · outbound

This paper cites Callaham, The history of Android: The evolution of the biggest mobile OS in the world, May 2021.

ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection Callaham, The history of Android: The evolution of the biggest mobile OS in the world, May 2021

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:33:14.924687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T22:33:14.613837Z digest=sha256:6ab265eafaad121f92c7d3cb60676fe0a8bb4a5309f685fd97a7cd1c6b8ee422

Observation 3789f331-d476-443a-9a08-87394f52c136 · outbound

This paper cites Machine learning and deep learning methods for cybersecurity,.

ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection Machine learning and deep learning methods for cybersecurity,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:33:14.909755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T22:33:14.618148Z digest=sha256:b78ce208dc373f14ac3b8cd4e1618c0b732ef5f5f50b864c2f030f95affd14ad

Observation d19351ba-125d-4f1e-931a-da5c2f3e0b62 · outbound

This paper cites Cyber attacks targeting android cellphones,.

ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection Cyber attacks targeting android cellphones,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:33:14.893537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T22:33:14.623025Z digest=sha256:ca71ea08de2a74745565e936d369fb0a83fa34528f6373a8769760d050642de7

Observation 79446cdd-6979-450d-845d-a5f18c4cc35c · outbound

This paper cites Intelligent mobile malware detection using permission requests and api calls,.

ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection Intelligent mobile malware detection using permission requests and api calls,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:33:14.874864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T22:33:14.627050Z digest=sha256:8bb464d158ca1d381ecc2680527ce3f9cb722416669c543f8f39c2f0881db556

Observation a9271511-02bd-43a1-948d-464411c4f4ea · outbound

This paper cites Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network,.

ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:33:14.858252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T22:33:14.631000Z digest=sha256:d0496aa031f98685e1031832a26962024055f313dea073b91d5c7ce090fdd8f8

Observation 8c5a4ac7-842a-4d62-9a36-a9c5da634013 · outbound

This paper cites Analysis of variance (anova),.

ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection Analysis of variance (anova),

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:33:14.841460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T22:33:14.635078Z digest=sha256:95ba0d48f6c7d45d9982967a0589001f357ff3ace7892b4107170e67b7fff307

Observation a33b5da5-a704-4d73-a82c-f968a6e4b71d · outbound

This paper cites Available: https://www.sciencedirect.com/ science/article/pii/0169743989800954.

ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection Available: https://www.sciencedirect.com/ science/article/pii/0169743989800954

Reference 12

Resolution
verified exact
raw_fallback, observed 2026-08-05T22:33:14.754165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T22:33:14.639039Z digest=sha256:26277b8d970348019dc9580cc28d7c04b398bd31e1149e959045149540d4b2d3

Observation 7b09799f-c978-476c-9658-bac7aa5462d9 · outbound

This paper cites Ambielli, Gini Impurity (With Examples), Oct.

ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection Ambielli, Gini Impurity (With Examples), Oct

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:33:14.825334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T22:33:14.643345Z digest=sha256:59898d2c0122bc09a3af4a999dc75055a4c6995ca5b7943e54339875cfe56a24

Observation 12f9c6d9-613a-4477-9b31-7d52213eb2df · outbound

This paper cites Dynamic android malware cat - egory classification using semi -supervised deep learn - ing,.

ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection Dynamic android malware cat - egory classification using semi -supervised deep learn - ing,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:33:14.810493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T22:33:14.647561Z digest=sha256:00bc8820ca187f41100c0c4493a027526872ade244d6c690b97cdfb8b1a3ebe2

Pith citing papers

No inbound Pith citation observations are available.