{"total":30,"rows":[{"id":"2112.12310","title":"Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art","created":"2021-12-23","cited_by_count":18,"area":null},{"id":"2302.01757","title":"RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion","created":"2023-01-31","cited_by_count":17,"area":null},{"id":"2202.06043","title":"RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style Transformation","created":"2022-02-12","cited_by_count":15,"area":null},{"id":"2103.06297","title":"TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack","created":"2021-03-10","cited_by_count":12,"area":null},{"id":"2204.10192","title":"Residue-Based Natural Language Adversarial Attack Detection","created":"2022-04-17","cited_by_count":8,"area":null},{"id":"2302.10739","title":"MalProtect: Stateful Defense Against Adversarial Query Attacks in ML-based Malware Detection","created":"2023-02-21","cited_by_count":8,"area":null},{"id":"2105.11593","title":"OFEI: A Semi-black-box Android Adversarial Sample Attack Framework Against DLaaS","created":"2021-05-25","cited_by_count":7,"area":null},{"id":"2111.08529","title":"Improving the robustness and accuracy of biomedical language models through adversarial training","created":"2021-11-16","cited_by_count":7,"area":null},{"id":"2106.15820","title":"Explanation-Guided Diagnosis of Machine Learning Evasion Attacks","created":"2021-06-30","cited_by_count":6,"area":null},{"id":"2102.06747","title":"Realizable Universal Adversarial Perturbations for Malware","created":"2021-02-12","cited_by_count":5,"area":null},{"id":"2302.11328","title":"PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks","created":"2023-02-22","cited_by_count":5,"area":null},{"id":"2302.00094","title":"The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models","created":"2023-01-31","cited_by_count":4,"area":null},{"id":"2108.12473","title":"Mal2GCN: A Robust Malware Detection Approach Using Deep Graph Convolutional Networks With Non-Negative Weights","created":"2021-08-27","cited_by_count":3,"area":null},{"id":"2202.07568","title":"StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection","created":"2022-02-15","cited_by_count":3,"area":null},{"id":"2308.01840","title":"URET: Universal Robustness Evaluation Toolkit (for Evasion)","created":"2023-08-03","cited_by_count":3,"area":null},{"id":"2310.03285","title":"Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations","created":"2023-10-05","cited_by_count":3,"area":null},{"id":"2312.06423","title":"MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks","created":"2023-12-11","cited_by_count":3,"area":null},{"id":"2205.15128","title":"Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detection","created":"2022-05-30","cited_by_count":2,"area":null},{"id":"2208.06092","title":"On deceiving malware classification with section injection","created":"2022-08-12","cited_by_count":1,"area":null},{"id":"2312.09636","title":"A Malware Classification Survey on Adversarial Attacks and Defences","created":"2023-12-15","cited_by_count":1,"area":null},{"id":"2401.11126","title":"CARE: Ensemble Adversarial Robustness Evaluation Against Adaptive Attackers for Security Applications","created":"2024-01-20","cited_by_count":1,"area":null},{"id":"2409.19594","title":"MASKDROID: Robust Android Malware Detection with Masked Graph Representations","created":"2024-09-29","cited_by_count":1,"area":null},{"id":"2412.18218","title":"On the Effectiveness of Adversarial Training on Malware Classifiers","created":"2024-12-24","cited_by_count":1,"area":null},{"id":"2505.10903","title":"On the Security Risks of ML-based Malware Detection Systems: A Survey","created":"2025-05-16","cited_by_count":1,"area":null},{"id":"2203.02121","title":"Adversarial Patterns: Building Robust Android Malware Classifiers","created":"2022-03-04","cited_by_count":0,"area":null},{"id":"2302.00537","title":"Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection","created":"2023-02-01","cited_by_count":0,"area":null},{"id":"2405.12266","title":"EGAN: Evolutional GAN for Ransomware Evasion","created":"2024-05-20","cited_by_count":0,"area":null},{"id":"2408.16025","title":"Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations","created":"2024-08-27","cited_by_count":0,"area":null},{"id":"2505.09342","title":"Evaluating the robustness of adversarial defenses in malware detection systems","created":"2025-05-14","cited_by_count":0,"area":null},{"id":"2511.12827","title":"Efficient Adversarial Malware Defense via Trust-Based Raw Override and Confidence-Adaptive Bit-Depth Reduction","created":"2025-11-16","cited_by_count":0,"area":null}],"next_cursor":null}