MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks | Paper Citation Record

Observation 9836f7a9-475c-4684-a723-a4f06c565936 · outbound

This paper cites 2022 global mobile threat report.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks 2022 global mobile threat report

Reference 1

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Observation d6594779-8933-4ad5-984a-595a89b83048 · outbound

This paper cites Shishkova.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Shishkova

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.179011Z

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Observation 718b961e-cf09-4596-93a8-051f55a8b211 · outbound

This paper cites Sedmdroid: An enhanced stacking ensemble framework for android malware detection.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Sedmdroid: An enhanced stacking ensemble framework for android malware detection

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.193776Z

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Observation b3bdb36a-4136-4e2a-82fe-b710bc9dc9ca · outbound

This paper cites Sdac: A slow-aging solution for android malware detection using semantic distance based api clustering.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Sdac: A slow-aging solution for android malware detection using semantic distance based api clustering

Reference 4

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Observation 9951a307-cad1-482d-b851-72f523ec4b9d · outbound

This paper cites Cyber code intelligence for android malware detection.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Cyber code intelligence for android malware detection

Reference 5

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Observation 7c0fa286-fd53-4933-bd8e-33e3c49b2c6d · outbound

This paper cites A hybrid deep network framework for android malware detection.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks A hybrid deep network framework for android malware detection

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.197476Z

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Observation 51b46a53-c862-42ae-a478-8edaf342687d · outbound

This paper cites Comprehensive android malware detection based on federated learning architecture.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Comprehensive android malware detection based on federated learning architecture

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.201405Z

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Observation e3a1a4b3-2d19-4174-94a7-fde2399dda1e · outbound

This paper cites Intrigu- ing properties of adversarial ml attacks in the problem space.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Intrigu- ing properties of adversarial ml attacks in the problem space

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.186520Z

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Observation abe76f74-cc0d-41d5-8d11-76614dcb421a · outbound

This paper cites Black-box adversarial example attack towards fcg based android malware detection under incomplete feature information.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Black-box adversarial example attack towards fcg based android malware detection under incomplete feature information

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.121905Z

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Observation dc2cba4c-bbb2-4452-a56d-c9f21143aeef · outbound

This paper cites Yes, machine learning can be more secure! a case study on android malware detection.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Yes, machine learning can be more secure! a case study on android malware detection

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.007708Z

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Observation 7c4e6c8d-5da7-41d1-af53-3f2562c86c54 · outbound

This paper cites Android hiv: A study of repackaging malware for evad- ing machine-learning detection.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Android hiv: A study of repackaging malware for evad- ing machine-learning detection

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.030597Z

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Observation 8cde0047-5bb6-425a-b35b-cdf8956a65d1 · outbound

This paper cites Adversarial deep ensemble: Evasion attacks and defenses for malware detection.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Adversarial deep ensemble: Evasion attacks and defenses for malware detection

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.100679Z

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Observation 2479102b-16ff-4b4d-9ffc-36e92be087ea · outbound

This paper cites Backdoor attack on machine learning based android malware detectors.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Backdoor attack on machine learning based android malware detectors

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:56.995292Z

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Observation 14b6ebe8-a957-442d-ada2-df406f104a67 · outbound

This paper cites {Explanation- Guided} backdoor poisoning attacks against malware classifiers.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks {Explanation- Guided} backdoor poisoning attacks against malware classifiers

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.147142Z

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Observation d91559c5-506d-4765-a9ef-afc900fdabd6 · outbound

This paper cites When does machine learning {FAIL}? generalized transferability for evasion and poisoning attacks.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks When does machine learning {FAIL}? generalized transferability for evasion and poisoning attacks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.058919Z

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Observation a150027e-47a6-4663-8bc3-2c8c9162b2d5 · outbound

This paper cites Why do adversarial attacks transfer? explaining transferability of evasion and poisoning attacks.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Why do adversarial attacks transfer? explaining transferability of evasion and poisoning attacks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:56.979233Z

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Observation 695ae454-13e8-4aec-b744-cdff63feb82d · outbound

This paper cites A framework for enhancing deep neural networks against adversarial malware.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks A framework for enhancing deep neural networks against adversarial malware

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.118156Z

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Observation cff505c3-f692-45aa-bcc3-10b3c33c3472 · outbound

This paper cites Adversarial elf malware detection method using model interpre- tation.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Adversarial elf malware detection method using model interpre- tation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.175655Z

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Observation 17556a92-3393-4157-9f6c-103bf0e371dc · outbound

This paper cites Pad: Towards principled adversarial malware detection against evasion attacks.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Pad: Towards principled adversarial malware detection against evasion attacks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:56.999301Z

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Observation 8530ce66-a946-4cfc-bfc8-17a5d5a6bcd3 · outbound

This paper cites Boosting fast ad- versarial training with learnable adversarial initialization.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Boosting fast ad- versarial training with learnable adversarial initialization

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.143330Z

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Observation cdfc41b9-0016-480e-9b0a-852c6c976efa · outbound

This paper cites Interpolated joint space adversarial training for robust and generalizable defenses.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Interpolated joint space adversarial training for robust and generalizable defenses

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.125798Z

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Observation 257c2973-7f7e-4680-a6a2-e5ca492f223b · outbound

This paper cites Robust android malware detection against adversarial example attacks.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Robust android malware detection against adversarial example attacks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.062380Z

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Observation ad7af6db-8d7e-4b75-8bdd-ee921f46579b · outbound

This paper cites A self- supervised approach for adversarial robustness.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks A self- supervised approach for adversarial robustness

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.108717Z

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Observation 7958870b-698d-4c6c-afa8-5563118d0be4 · outbound

This paper cites Adversarial purification with score-based generative models.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Adversarial purification with score-based generative models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.019616Z

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Observation 0d8472f8-4223-4f9e-a0c7-37a57360ccdb · outbound

This paper cites Evaluating the adversarial robustness of adaptive test- time defenses.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Evaluating the adversarial robustness of adaptive test- time defenses

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.055136Z

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Observation 10b70eda-51bb-4953-a40c-07d7f3084222 · outbound

This paper cites Privacy preserving defense for black box classifiers against on-line adversarial attacks.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Privacy preserving defense for black box classifiers against on-line adversarial attacks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.162014Z

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Observation 84843d2b-8b85-4ad5-9d2c-65ead62a4044 · outbound

This paper cites Diffusion models for adversarial purification.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Diffusion models for adversarial purification

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.003659Z

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Observation b38f3d38-0393-4657-85a9-9c4da6e0c3a2 · outbound

This paper cites Ofei: A semi-black-box android adversarial sample attack framework against dlaas.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Ofei: A semi-black-box android adversarial sample attack framework against dlaas

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.104940Z

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Observation 9a544fef-3853-4870-b00e-647ab6f28c5a · outbound

This paper cites Drebin: Effective and explainable detection of android malware in your pocket.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Drebin: Effective and explainable detection of android malware in your pocket

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.015809Z

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Observation 14340e20-70b3-477d-b326-f3a175d7f2fc · outbound

This paper cites Androzoo: Collecting millions of android apps for the research community.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Androzoo: Collecting millions of android apps for the research community

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.086894Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e2639fd2-d7a6-4d79-ad24-812de135c9e5 · outbound

This paper cites Practical evasion of a learning-based classifier: A case study.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Practical evasion of a learning-based classifier: A case study

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.038874Z

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Observation 52b32d74-7610-40d3-92d8-b0b0b729827e · outbound

This paper cites Avpass: Leaking and bypassing antivirus detection model automatically.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Avpass: Leaking and bypassing antivirus detection model automatically

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.094600Z

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Observation cf57e97a-7313-48a6-9304-5568fcbe430a · outbound

This paper cites Towards deep learning models resistant to adversarial attacks.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Towards deep learning models resistant to adversarial attacks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.066597Z

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Observation 5fb53b19-0108-4129-81d3-82765b1bb972 · outbound

This paper cites Interpreting adversarially trained convo- lutional neural networks.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Interpreting adversarially trained convo- lutional neural networks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.154706Z

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Observation bf26d63e-94bf-403a-aa10-6d4e98b75083 · outbound

This paper cites Ad- versarial deep learning for robust detection of binary encoded malware.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Ad- versarial deep learning for robust detection of binary encoded malware

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.090839Z

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Observation bb61c1d7-8c1c-4a1d-b921-6eba97e6bd37 · outbound

This paper cites Generating adversarial malware examples for black-box attacks based on gan.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Generating adversarial malware examples for black-box attacks based on gan

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.113091Z

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Observation f9f67366-fd9a-4dfb-8645-1ba9da40fa2f · outbound

This paper cites Query efficient decision based sparse attacks against black-box deep learning models.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Query efficient decision based sparse attacks against black-box deep learning models

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.047244Z

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Observation f36f4b5a-1bc4-4bfd-b1cb-16d91e49c023 · outbound

This paper cites Reliable evaluation of adversarial ro- bustness with an ensemble of diverse parameter-free attacks.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Reliable evaluation of adversarial ro- bustness with an ensemble of diverse parameter-free attacks

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.011851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 380470ed-5530-4435-a16e-b5b1799c4073 · outbound

This paper cites PBP: post- training backdoor purification for malware classifiers.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks PBP: post- training backdoor purification for malware classifiers

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.080424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 07c6199a-e1ca-4ee4-ae5b-e0ed91e787b3 · outbound

This paper cites A multi- modal deep learning method for android malware detection using various features.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks A multi- modal deep learning method for android malware detection using various features

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:56.983239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 89c88c05-39cc-455f-8935-923fd3efdd50 · outbound

This paper cites Adversarial examples for malware detection.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Adversarial examples for malware detection

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:56.991183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7b0f2341-e925-43f5-8848-0acbb5f989db · outbound

This paper cites On the (Statistical) Detection of Adversarial Examples.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks On the (Statistical) Detection of Adversarial Examples

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-05-24T05:33:56.490247Z

Source-reported events for the cited work

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Observation 0533b126-6805-4dd1-996b-c2360f56ee71 · outbound

This paper cites Towards robust detection of adversarial examples.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Towards robust detection of adversarial examples

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:56.936269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e5d20283-d297-4201-b860-431fd165eff9 · outbound

This paper cites Enhancing robustness of deep neural networks against adversarial malware samples: Principles, framework, and application to aics’2019 challenge.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Enhancing robustness of deep neural networks against adversarial malware samples: Principles, framework, and application to aics’2019 challenge

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:56.929074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5d58374f-7722-4c13-b9ad-f0b1e51212e2 · outbound

This paper cites Evading adversarial example detection defenses with orthogonal projected gradient descent.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Evading adversarial example detection defenses with orthogonal projected gradient descent

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.136175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation eedd7bc0-77ad-496a-a466-52c010c0d5b7 · outbound

This paper cites Semantics-preserving node injection attacks against gnn-based acfg malware classifiers.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Semantics-preserving node injection attacks against gnn-based acfg malware classifiers

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.042874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d4a39e8e-7352-4a27-a107-8f054dde6aa6 · outbound

This paper cites Dl-fhmc: Deep learning- based fine-grained hierarchical learning approach for robust mal- ware classification.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Dl-fhmc: Deep learning- based fine-grained hierarchical learning approach for robust mal- ware classification

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:56.932615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a31e1d9a-92c3-4981-a591-110e2e6439b5 · outbound

This paper cites Mamadroid: Detecting android mal- ware by building markov chains of behavioral models (extended version).

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Mamadroid: Detecting android mal- ware by building markov chains of behavioral models (extended version)

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.070400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a92a68a3-4e92-44e0-915f-6750972a9b4c · outbound

This paper cites Efficient query-based attack against ml-based android malware detection under zero knowl- edge setting.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Efficient query-based attack against ml-based android malware detection under zero knowl- edge setting

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.074390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b7c5543f-055c-4fd6-8961-c74022730798 · outbound

This paper cites Mab-malware: A reinforcement learning framework for blackbox generation of adversarial malware.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Mab-malware: A reinforcement learning framework for blackbox generation of adversarial malware

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.151112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 725f2eea-7e1a-49bf-bf02-256c27f2a8b8 · outbound

This paper cites Evadedroid: A practical evasion attack on machine learning for black-box android malware detec- tion.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Evadedroid: A practical evasion attack on machine learning for black-box android malware detec- tion

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.171789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation db6f77e7-f652-4eba-8193-3aefd4e8e53b · outbound

This paper cites Structural attack against graph based android mal- ware detection.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Structural attack against graph based android mal- ware detection

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.129441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b6d2db2e-8097-446c-a896-eb4e18e91967 · outbound

This paper cites Dl-droid: Deep learning based android malware detection using real devices.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Dl-droid: Deep learning based android malware detection using real devices

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.051306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8b2960f5-0c25-4a2c-adc1-f24b742dc683 · outbound

This paper cites Familial clustering for weakly-labeled android malware using hybrid representation learning.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Familial clustering for weakly-labeled android malware using hybrid representation learning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.167651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation caef9022-c330-4c8d-823e-65c7eb0f1982 · outbound

This paper cites Boosting adversarial attacks with momentum.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Boosting adversarial attacks with momentum

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.023901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f2c75dc3-954c-4e0a-8087-1e888ff833e7 · outbound

This paper cites Feature-space bayesian adversarial learning improved malware detector robustness.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Feature-space bayesian adversarial learning improved malware detector robustness

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:56.940025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 443cf7ae-dddf-4144-8d95-7acf940e028c · outbound

This paper cites Malware analysis by combining multiple detectors and observation windows.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Malware analysis by combining multiple detectors and observation windows

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:57.158438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cf33eb13-97de-4c27-976b-f50adb384948 · outbound

This paper cites His major research interests include moving target defense, Android malware detection, and reverse engineering.

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks His major research interests include moving target defense, Android malware detection, and reverse engineering

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T05:33:56.972732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers