EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability | Paper Citation Record

Observation 0703d204-0482-49dc-9234-89099b678b96 · outbound

This paper cites EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models,

Reference 1

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Observation 4d75c38f-d554-489c-b140-c8d66915a541 · outbound

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

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Deep neural network based malware detection using two dimensional binary program features,

Reference 2

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Observation b7d10bde-2f6a-4bbe-9017-059d823ee83c · outbound

This paper cites Novel feature extraction, selection and fusion for effective malware family classification,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Novel feature extraction, selection and fusion for effective malware family classification,

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Observation dd77f27e-2f1f-4551-a391-3aaa028627f8 · outbound

This paper cites Malware detection by eating a whole EXE,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Malware detection by eating a whole EXE,

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Observation f25e293a-b4a4-4eb8-bd33-5c8a891e7b0e · outbound

This paper cites Deep convolutional malware classifiers can learn from raw executables and labels only,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Deep convolutional malware classifiers can learn from raw executables and labels only,

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Observation 6f8e5109-9976-444e-98d5-4ee3829a287f · outbound

This paper cites Activation analysis of a byte-based deep neural network for malware classification,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Activation analysis of a byte-based deep neural network for malware classification,

Reference 6

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Observation e38a0939-db34-4906-92a2-cb379e6b2d44 · outbound

This paper cites Auditing static machine learning anti-malware tools against metamorphic attacks,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Auditing static machine learning anti-malware tools against metamorphic attacks,

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Observation 3a547b83-8167-484c-a996-65837886fe62 · outbound

This paper cites Malware images: visualization and automatic classification,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Malware images: visualization and automatic classification,

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Observation e982fc11-d7fb-4081-a142-d239786c67ea · outbound

This paper cites Using convolutional neural networks for classification of malware represented as images,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Using convolutional neural networks for classification of malware represented as images,

Reference 9

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Observation 3becbb5f-2de1-4e58-8187-857bf3e23497 · outbound

This paper cites RS-Del: Edit distance robustness certificates for sequence classifiers via randomized deletion,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability RS-Del: Edit distance robustness certificates for sequence classifiers via randomized deletion,

Reference 10

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Observation a22639a3-5a01-4bea-a0ce-8eae4f64d322 · outbound

This paper cites Towards a practical defense against adversarial attacks on deep learning-based malware detectors via ran- domized smoothing,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Towards a practical defense against adversarial attacks on deep learning-based malware detectors via ran- domized smoothing,

Reference 11

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Observation 938603c2-5afd-419f-ae5e-31741d79f828 · outbound

This paper cites Certified robustness of static deep learning-based malware detec- tors against patch and append attacks,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Certified robustness of static deep learning-based malware detec- tors against patch and append attacks,

Reference 12

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Observation ca3c23d5-9642-4eab-a337-6fd8713e5bbe · outbound

This paper cites Drsm: De- randomized smoothing on malware classifier providing certified robust- ness,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Drsm: De- randomized smoothing on malware classifier providing certified robust- ness,

Reference 13

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Observation e3723a2f-880e-4f27-8509-96f38acf8fff · outbound

This paper cites Adversarial robustness of deep learning-based malware detectors via (de)randomized smoothing,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Adversarial robustness of deep learning-based malware detectors via (de)randomized smoothing,

Reference 14

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Observation 63e869bd-ed37-435d-9d72-89a9fd9b42aa · outbound

This paper cites Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing

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Observation c89f9052-57f3-44cc-9184-61389e3c746c · outbound

This paper cites Certified adversarial robustness via randomized smoothing,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Certified adversarial robustness via randomized smoothing,

Reference 16

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Observation 0a8b8d74-48ab-4904-9e7c-afe88bda473b · outbound

This paper cites (de)randomized smoothing for certifiable defense against patch attacks,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability (de)randomized smoothing for certifiable defense against patch attacks,

Reference 17

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Observation 7efa643e-2642-4492-b8fc-5a949b366580 · outbound

This paper cites {TESSERACT}: Eliminating experimental bias in malware classifi- cation across space and time,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability {TESSERACT}: Eliminating experimental bias in malware classifi- cation across space and time,

Reference 18

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Observation 3b1e63cd-cd69-4da3-ae6e-480f0d948343 · outbound

This paper cites Adversarial malware binaries: Evading deep learning for malware detection in executables,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Adversarial malware binaries: Evading deep learning for malware detection in executables,

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Observation 1c66b820-056a-44ef-b0ca-ebee737bcf5b · outbound

This paper cites Malware makeover: Breaking ml-based static analysis by modifying executable bytes,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Malware makeover: Breaking ml-based static analysis by modifying executable bytes,

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Observation ce9dfaea-e810-42f9-ac55-843da7c0e3fc · outbound

This paper cites Adversarial exemples: A survey and experimental evaluation of practical attacks on machine learning for windows malware detection,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Adversarial exemples: A survey and experimental evaluation of practical attacks on machine learning for windows malware detection,

Reference 21

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Observation 95a1ebe8-6c36-48de-a390-b932f9aa90ba · outbound

This paper cites Functionality-preserving black-box optimization of adversarial win- dows malware,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Functionality-preserving black-box optimization of adversarial win- dows malware,

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This paper cites Adversarial training for{Raw-Binary}malware classifiers,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Adversarial training for{Raw-Binary}malware classifiers,

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Observation 657e609a-2f5f-4d69-bfaa-46b23d8e5d0e · outbound

This paper cites Wild patterns: Ten years after the rise of adversarial machine learning,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Wild patterns: Ten years after the rise of adversarial machine learning,

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Observation 7ddded6e-6dc9-4746-88b9-1f5cbfa682af · outbound

This paper cites Attackbench: Evaluating gradient-based attacks for adversarial examples,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Attackbench: Evaluating gradient-based attacks for adversarial examples,

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Observation 8d75e0db-2b07-4e1e-8200-c8f3e0912260 · outbound

This paper cites Robustbench: a standardized adversarial robustness benchmark,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Robustbench: a standardized adversarial robustness benchmark,

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Observation 9ec22f84-d34c-4ef7-a075-1737cdc89b2c · outbound

This paper cites Intriguing properties of adversarial ml attacks in the problem space,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Intriguing properties of adversarial ml attacks in the problem space,

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Observation b2ea6e32-def6-49ad-8a66-6034e3793168 · outbound

This paper cites Robust intelligent malware detection using deep learning,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Robust intelligent malware detection using deep learning,

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Observation c9899227-e3d2-490a-9a1b-723b2d7816d4 · outbound

This paper cites Optimized approaches to malware detection: A study of machine learning and deep learning techniques,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Optimized approaches to malware detection: A study of machine learning and deep learning techniques,

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Observation 8afc5b03-5920-4e5b-86db-2892be95a49f · outbound

This paper cites Evaluating realistic adversarial attacks against machine learning models for windows pe malware detection,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Evaluating realistic adversarial attacks against machine learning models for windows pe malware detection,

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Observation 2c02504b-38e5-4b48-8758-bb3a9af11223 · outbound

This paper cites A comparison of adversarial malware generators,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability A comparison of adversarial malware generators,

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Observation 0e4443c0-64d5-4a76-983d-4b62ad3137d6 · outbound

This paper cites The robust malware detection challenge and greedy random accelerated multi-bit search,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability The robust malware detection challenge and greedy random accelerated multi-bit search,

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Observation e24569ce-fcf5-4884-84e3-fe097859ddaa · outbound

This paper cites Fast minimum-norm adversarial attacks through adaptive norm constraints,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Fast minimum-norm adversarial attacks through adaptive norm constraints,

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Observation ae529dd3-a850-45f0-9be8-5fb7aa2b8da9 · outbound

This paper cites Quo vadis: hybrid machine learning meta-model based on contextual and behavioral malware representations,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Quo vadis: hybrid machine learning meta-model based on contextual and behavioral malware representations,

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Observation ea3ea3a9-1d68-4056-aef2-768e88ab103e · outbound

This paper cites Greedy function approximation: a gradient boosting machine,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Greedy function approximation: a gradient boosting machine,

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Observation 856f7768-d114-4dc1-9ba4-ef91cfa3a4db · outbound

This paper cites Lightgbm: A highly efficient gradient boosting decision tree,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Lightgbm: A highly efficient gradient boosting decision tree,

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Observation 04adb692-8862-42c4-ac8d-d9fcca3bc93d · outbound

This paper cites Deep residual learning for image recognition,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Deep residual learning for image recognition,

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Observation 9ae1f8f8-ab18-417a-b2ed-c3827f24e7a9 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Pytorch: An imperative style, high-performance deep learning library,

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Observation 4d293ae2-1dec-4812-83e3-18d219c1d4c4 · outbound

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

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Why do adversarial attacks transfer? explaining transferability of evasion and poisoning attacks,

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Observation 05772be9-4337-4cfb-aa07-55421aef50aa · outbound

This paper cites The rise of machine learning for detection and classification of malware: Research developments, trends and challenges,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability The rise of machine learning for detection and classification of malware: Research developments, trends and challenges,

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Observation ba49f6b1-360d-4eea-b4fe-ebb63d9d6a9d · outbound

This paper cites Sorel-20m: A large scale benchmark dataset for malicious pe detection,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Sorel-20m: A large scale benchmark dataset for malicious pe detection,

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Observation 7c3e30ba-49c8-4b0a-a20c-3545789750b9 · outbound

This paper cites Ember2024-a benchmark dataset for holistic evaluation of malware classifiers,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Ember2024-a benchmark dataset for holistic evaluation of malware classifiers,

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Observation bb3b448f-0748-44a2-920b-53bc11c2cbc9 · outbound

This paper cites Beyond raw bytes: Towards large language models,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Beyond raw bytes: Towards large language models,

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Observation 8d9f75d9-bfdc-488c-9a51-faa381461411 · outbound

This paper cites Guided malware sample analysis based on graph neural networks,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Guided malware sample analysis based on graph neural networks,

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Observation 318d8e34-4546-46d0-a6bb-7cab94e0c1ac · outbound

This paper cites Training robust ml-based raw-binary malware detectors in hours, not months,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Training robust ml-based raw-binary malware detectors in hours, not months,

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Observation 2f3665b1-6c74-4788-800c-f11c8607ba91 · outbound

This paper cites Updating windows mal- ware detectors: Balancing robustness and regression against adversarial exemples,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Updating windows mal- ware detectors: Balancing robustness and regression against adversarial exemples,

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Observation dd1e21e7-e3b5-4b7a-9d53-48f126a22726 · outbound

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

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Mab- malware: A reinforcement learning framework for blackbox generation of adversarial malware,

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Observation 72b6ffa1-20fd-47c8-9466-7735748c9051 · outbound

This paper cites Creating valid ad- versarial examples of malware,.

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability Creating valid ad- versarial examples of malware,

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