Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification | Paper Citation Record

Observation 6686ba9f-6ff6-4836-9989-b6d970fcb83f · outbound

This paper cites Dtmic: Deep transfer learning for malware image classification,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Dtmic: Deep transfer learning for malware image classification,

Reference 1

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Observation 09dc3b8a-33d3-49dd-ab40-b75777a17149 · outbound

This paper cites A few-shot meta-learning based siamese neural network using entropy features for ransomware classification,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification A few-shot meta-learning based siamese neural network using entropy features for ransomware classification,

Reference 2

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Observation 7b026197-9406-48a3-b3fe-626571fe442a · outbound

This paper cites From data and model levels: Improve the performance of few-shot malware classification,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification From data and model levels: Improve the performance of few-shot malware classification,

Reference 3

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Observation f09f1edf-f372-4d6e-bc3b-06da7989a674 · outbound

This paper cites Attention-based multidimensional deep learning approach for cross-architecture iomt malware detection and classification in healthcare cyber-physical systems,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Attention-based multidimensional deep learning approach for cross-architecture iomt malware detection and classification in healthcare cyber-physical systems,

Reference 4

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Observation 0faaab12-c21e-487e-a792-8e4128861a5f · outbound

This paper cites Denoising ad- versarial autoencoder for obfuscated traffic detection and recovery,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Denoising ad- versarial autoencoder for obfuscated traffic detection and recovery,

Reference 5

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Observation ee8109c0-ed58-4f12-92ac-6f0f2b1a012c · outbound

This paper cites Ae-dcnn: Autoen- coder enhanced deep convolutional neural network for malware classi- fication,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Ae-dcnn: Autoen- coder enhanced deep convolutional neural network for malware classi- fication,

Reference 6

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Observation 1f46c0e7-b3db-444a-8195-2f351d8ccce2 · outbound

This paper cites Zero-day ransomware attack detection using deep contractive autoencoder and voting based ensemble classifier,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Zero-day ransomware attack detection using deep contractive autoencoder and voting based ensemble classifier,

Reference 7

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Observation bb0ec206-d3e1-46b7-b0e7-8d364b136f2e · outbound

This paper cites Effective and efficient hybrid android malware classification using pseudo-label stacked auto- encoder,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Effective and efficient hybrid android malware classification using pseudo-label stacked auto- encoder,

Reference 8

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Observation f1798a35-f693-4564-8baa-6a7d9ae58112 · outbound

This paper cites Deep neural network based malware detection using two dimensional binary program features,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Deep neural network based malware detection using two dimensional binary program features,

Reference 9

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Observation 9745ff8e-897f-4902-9334-9adfa39c0038 · outbound

This paper cites Malware detection using static analysis in android: a review of feco (features, classification, and obfuscation),.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Malware detection using static analysis in android: a review of feco (features, classification, and obfuscation),

Reference 10

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Observation db694b1b-ab60-46e4-a7c1-c624c831256c · outbound

This paper cites Efficacy of nonlinear manifold learning in malware image pattern analysis,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Efficacy of nonlinear manifold learning in malware image pattern analysis,

Reference 11

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Observation 26a8fed5-3559-4699-b067-2bcfe03f91e5 · outbound

This paper cites An adaptive hybrid pattern for noise-robust texture analysis,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification An adaptive hybrid pattern for noise-robust texture analysis,

Reference 12

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Observation 082787c7-bd06-4c9a-9996-9e628aa2252f · outbound

This paper cites Relation networks for object detection,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Relation networks for object detection,

Reference 13

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Observation 7eaef511-7637-442f-baad-7bca42d60775 · outbound

This paper cites A strings- based similarity analysis approach for characterizing iot malware and in- ferring their underlying relationships,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification A strings- based similarity analysis approach for characterizing iot malware and in- ferring their underlying relationships,

Reference 14

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Observation c93f2808-23f7-4492-90ec-703e11571888 · outbound

This paper cites Hadm: Hybrid analysis for detection of malware,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Hadm: Hybrid analysis for detection of malware,

Reference 15

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Observation 9152580a-3543-4ad5-acc7-5934eb27ab21 · outbound

This paper cites Obfuscated malware detection using deep generative model based on global/local features,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Obfuscated malware detection using deep generative model based on global/local features,

Reference 16

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Observation aae3a953-faf3-4647-b19d-c490aa407d5a · outbound

This paper cites Siamese neural network based few-shot learning for anomaly detection in industrial cyber-physical systems,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Siamese neural network based few-shot learning for anomaly detection in industrial cyber-physical systems,

Reference 17

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Observation 33738441-4759-4d2e-8683-1816f94dc362 · outbound

This paper cites Dynamic prototype network based on sample adaptation for few-shot malware detection,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Dynamic prototype network based on sample adaptation for few-shot malware detection,

Reference 18

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Observation e753ab33-3cb0-4205-b77c-f95613794d8c · outbound

This paper cites Android malware obfuscation variants detection method based on multi-granularity opcode features,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Android malware obfuscation variants detection method based on multi-granularity opcode features,

Reference 19

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Observation 4dc30dae-f72c-4f37-ac04-972b3271ae7e · outbound

This paper cites A malicious android malware detection system based on implicit relationship mining,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification A malicious android malware detection system based on implicit relationship mining,

Reference 20

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This paper cites Learning to classify: A flow- based relation network for encrypted traffic classification,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Learning to classify: A flow- based relation network for encrypted traffic classification,

Reference 21

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Observation 318d3ba7-6f13-4496-9b4f-4f53d8858302 · outbound

This paper cites Maldae: Detecting and explaining malware based on correlation and fusion of static and dynamic characteristics,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Maldae: Detecting and explaining malware based on correlation and fusion of static and dynamic characteristics,

Reference 22

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Observation b9028fdf-6e55-44d1-a280-230feb4348a9 · outbound

This paper cites Behavior-based detection and classification of malicious software utilizing structural characteristics of group sequence graphs,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Behavior-based detection and classification of malicious software utilizing structural characteristics of group sequence graphs,

Reference 23

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Observation 848980af-b041-40b5-aed1-2f61a6b58621 · outbound

This paper cites A graph-based model for malicious software detection exploiting domination relations between system-call groups,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification A graph-based model for malicious software detection exploiting domination relations between system-call groups,

Reference 24

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Observation fd59338b-e12b-4bad-a76f-2a73e1f0f261 · outbound

This paper cites Malinsight: A systematic profiling based malware detection framework,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Malinsight: A systematic profiling based malware detection framework,

Reference 25

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Observation f375c5d8-934a-431a-bf92-93b1ce37c035 · outbound

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Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification A hybrid machine learning approach for malicious behaviour detection and recognition in cloud computing,

Reference 26

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Observation c1de57c9-cbfa-4ad8-9681-1d9f7f05e5db · outbound

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Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Countering cyber threats for industrial applications: An automated approach for malware evasion detection and analysis,

Reference 27

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Observation 26ab0ace-e5bb-4410-b106-086e4da0b05c · outbound

This paper cites Ransomware early detection by the analysis of file sharing traffic,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Ransomware early detection by the analysis of file sharing traffic,

Reference 28

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Observation 3e8723cd-96ab-488e-816b-aa0153cef11e · outbound

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Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification A mobile malware detection method using behavior features in network traffic,

Reference 29

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Observation 16bf13dc-8599-4490-8fea-cd600f8c82d8 · outbound

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Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification A hybrid approach of mobile malware detection in android,

Reference 30

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Observation 42bed9dd-5c8c-4205-a6e8-98734851b072 · outbound

This paper cites A comparison of static, dynamic, and hybrid analysis for malware detection,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification A comparison of static, dynamic, and hybrid analysis for malware detection,

Reference 31

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Observation 0833eda6-c291-462e-9107-084002cc73b0 · outbound

This paper cites Ai-hydra: Advanced hybrid approach using random forest and deep learning for malware classification,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Ai-hydra: Advanced hybrid approach using random forest and deep learning for malware classification,

Reference 32

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Observation 2a6a86f9-e561-4f81-b158-bcba24f36893 · outbound

This paper cites An investigation of byte n-gram features for malware classification,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification An investigation of byte n-gram features for malware classification,

Reference 33

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Observation ec12b954-9571-4f52-a514-d3f0f9a51130 · outbound

This paper cites Ensemble model ransomware classification: A static analysis-based approach,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Ensemble model ransomware classification: A static analysis-based approach,

Reference 34

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Observation f9a5eb0d-fde1-4177-b160-9fbdda2340a5 · outbound

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Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Android malware classification method: Dalvik bytecode frequency analysis,

Reference 35

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Observation 197a2ff6-7713-4fd0-8599-eb8afe7dcd6a · outbound

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Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Malware images: visualization and automatic classification,

Reference 36

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Observation f3ecb0a6-21b4-4723-a9e2-7cb1dfd71245 · outbound

This paper cites Visdroid: Android malware classification based on local and global image features, bag of visual words and machine learning techniques,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Visdroid: Android malware classification based on local and global image features, bag of visual words and machine learning techniques,

Reference 37

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Observation cd81bde7-1798-45a3-ae60-d4a938350fa6 · outbound

This paper cites Hybrid malware detection based on bi-lstm and spp-net for smart iot,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Hybrid malware detection based on bi-lstm and spp-net for smart iot,

Reference 38

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Observation 876eed5d-9e94-4931-970a-d6651d40fdaa · outbound

This paper cites Survey for detection and anal- ysis of android malware (s) through artificial intelligence techniques,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Survey for detection and anal- ysis of android malware (s) through artificial intelligence techniques,

Reference 39

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Observation 907ded98-46d6-400d-83bf-b5aa5ded6935 · outbound

This paper cites A cloud- based platform for the emulation of complex cybersecurity scenarios,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification A cloud- based platform for the emulation of complex cybersecurity scenarios,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.742535Z

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Observation 2039a39c-6606-4901-b685-92632aa96ac0 · outbound

This paper cites Using virtual environments for the assessment of cybersecurity issues in iot scenarios,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Using virtual environments for the assessment of cybersecurity issues in iot scenarios,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.731830Z

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Observation 4118075b-2d13-444e-9e5e-e7d63c1df721 · outbound

This paper cites Malware detection through low-level features and stacked denoising autoencoders.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Malware detection through low-level features and stacked denoising autoencoders

Reference 42

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Observation 7724f389-e0c9-4442-bbb2-2261deca2ccb · outbound

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Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network,

Reference 43

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Observation 980cf61e-93b1-4ae5-a3de-d5c7b86c2ad2 · outbound

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Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.704051Z

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Observation acdaf92f-5c6a-40d5-a3ab-0e1e28a8f479 · outbound

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Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Mpsautodetect: A malicious powershell script detection model based on stacked denoising auto-encoder,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.695105Z

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Observation c6c1c956-4ca8-4278-abc9-42657866bd2f · outbound

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Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Bm3d and deep image prior based denoising for the defense against adversarial attacks on malware detection net- works,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.685178Z

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Observation f2ea9ae8-fbc8-4688-a25c-8d009fea0288 · outbound

This paper cites Advanced obfuscation techniques for java bytecode,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Advanced obfuscation techniques for java bytecode,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.675611Z

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Observation f11dd2b9-8aa3-405f-a2b6-3776c059188a · outbound

This paper cites Evaluation of android anti-malware techniques against dalvik bytecode obfuscation,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Evaluation of android anti-malware techniques against dalvik bytecode obfuscation,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.665912Z

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Observation b1c1ad58-630a-4613-9483-8ba83053ea0a · outbound

This paper cites A study of detecting computer viruses in real-infected files in the n-gram representation with machine learning methods,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification A study of detecting computer viruses in real-infected files in the n-gram representation with machine learning methods,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.656933Z

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Observation f321c198-d924-45dd-b737-dc0a82137c4c · outbound

This paper cites A comparative study on op- timization, obfuscation, and deobfuscation tools in android.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification A comparative study on op- timization, obfuscation, and deobfuscation tools in android

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.645937Z

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Observation cf6b5b61-ecd3-498a-801c-20bb249f9661 · outbound

This paper cites Entropy based local binary pattern (elbp) feature extraction technique of multimodal biometrics as defence mechanism for cloud storage,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Entropy based local binary pattern (elbp) feature extraction technique of multimodal biometrics as defence mechanism for cloud storage,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.636188Z

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Observation 77d7c4d5-c900-478f-9c20-74cf589ba49a · outbound

This paper cites Classification of malware by using structural entropy on convolutional neural networks,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Classification of malware by using structural entropy on convolutional neural networks,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.627182Z

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Observation 58292d3d-6dc0-49fb-881c-8979125187e6 · outbound

This paper cites An hmm and structural entropy based detector for android malware: An empirical study,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification An hmm and structural entropy based detector for android malware: An empirical study,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.618007Z

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Observation 4487abce-7caa-4fb4-a97b-ff6115016912 · outbound

This paper cites Trans- forming malware behavioural dataset for deep denoising autoencoders,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Trans- forming malware behavioural dataset for deep denoising autoencoders,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.608514Z

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Observation dc9fac97-5007-4c14-a1cc-b2aedd82c02d · outbound

This paper cites Malware detection in mo- bile environments based on autoencoders and api-images,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Malware detection in mo- bile environments based on autoencoders and api-images,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.598018Z

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Observation 29e7f65e-11e8-4ea0-b619-e57571364a86 · outbound

This paper cites Music removal by convolutional denoising autoencoder in speech recognition,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Music removal by convolutional denoising autoencoder in speech recognition,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.588081Z

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Observation 13f00588-3cdc-4dec-a5fe-a33032d81e45 · outbound

This paper cites Face recognition via deep stacked denoising sparse autoencoders (dsdsa),.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Face recognition via deep stacked denoising sparse autoencoders (dsdsa),

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.578550Z

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Observation 4d381c18-4dad-4ace-bdb0-dddc646518c5 · outbound

This paper cites Imcfn: Image-based malware classification using fine-tuned convolu- tional neural network architecture,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Imcfn: Image-based malware classification using fine-tuned convolu- tional neural network architecture,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.569504Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 82a7c744-dc05-44b1-a544-81a18e062a3b · outbound

This paper cites Mcft-cnn: Malware classification with fine-tune convolution neural networks using traditional and transfer learning in internet of things,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Mcft-cnn: Malware classification with fine-tune convolution neural networks using traditional and transfer learning in internet of things,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.559527Z

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Observation 5c2919b8-b4d4-4dc4-88da-1539b474ee5b · outbound

This paper cites Malware image classification using one-shot learning with siamese networks,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Malware image classification using one-shot learning with siamese networks,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.548680Z

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Observation 746a2fba-28af-4180-92e2-4e7e52d3eb35 · outbound

This paper cites Calculating distances between windows malware using siamese neural network embeddings,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Calculating distances between windows malware using siamese neural network embeddings,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.539779Z

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Observation d406c6a5-f529-4778-84b0-d987a2102cc2 · outbound

This paper cites Learning to compare: Relation network for few-shot learning,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Learning to compare: Relation network for few-shot learning,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T15:41:11.452479Z

Source-reported events for the cited work

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Observation 46ca5b7d-c4b0-46cf-9ac5-c81e194c4de3 · outbound

This paper cites Reinforced similarity learning: Siamese relation networks for robust object tracking,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Reinforced similarity learning: Siamese relation networks for robust object tracking,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.522690Z

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Observation 87e726d4-bd67-44f4-8711-aeaa549caca9 · outbound

This paper cites Learning to filter: Siamese relation network for robust tracking,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Learning to filter: Siamese relation network for robust tracking,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.512987Z

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Observation a11d5f5d-c366-4a7c-8ccf-19b80ff04a13 · outbound

This paper cites Siam-ids: Handling class imbalance problem in intrusion detection systems using siamese neural network,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Siam-ids: Handling class imbalance problem in intrusion detection systems using siamese neural network,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.503627Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5981dceb-56ea-4c27-9e86-1a3ee1d1e712 · outbound

This paper cites Prototypical networks for few-shot learning,.

Relation-aware based Siamese Denoising Autoencoder for Malware Few-shot Classification Prototypical networks for few-shot learning,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:41:11.493864Z

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