{"total":55,"rows":[{"id":"2106.15764","title":"The Threat of Offensive AI to Organizations","created":"2021-06-30","cited_by_count":38,"area":null},{"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":"2202.03277","title":"On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks","created":"2022-02-07","cited_by_count":15,"area":null},{"id":"2202.05470","title":"Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers","created":"2022-02-11","cited_by_count":14,"area":null},{"id":"2108.11673","title":"Why Adversarial Reprogramming Works, When It Fails, and How to Tell the Difference","created":"2021-08-26","cited_by_count":13,"area":null},{"id":"2109.04344","title":"EvilModel 2.0: Bringing Neural Network Models into Malware Attacks","created":"2021-09-09","cited_by_count":13,"area":null},{"id":"2110.03301","title":"EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection","created":"2021-10-07","cited_by_count":13,"area":null},{"id":"2208.12248","title":"Quo Vadis: Hybrid Machine Learning Meta-Model based on Contextual and Behavioral Malware Representations","created":"2022-08-20","cited_by_count":9,"area":null},{"id":"2401.12790","title":"MORPH: Towards Automated Concept Drift Adaptation for Malware Detection","created":"2024-01-23","cited_by_count":8,"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":"2205.12311","title":"Fast & Furious: Modelling Malware Detection as Evolving Data Streams","created":"2022-05-24","cited_by_count":6,"area":null},{"id":"2212.02663","title":"Efficient Malware Analysis Using Metric Embeddings","created":"2022-12-05","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":"2110.01362","title":"Automating Privilege Escalation with Deep Reinforcement Learning","created":"2021-10-04","cited_by_count":5,"area":null},{"id":"2308.09958","title":"A Comparison of Adversarial Learning Techniques for Malware Detection","created":"2023-08-19","cited_by_count":5,"area":null},{"id":"2312.13435","title":"The Adaptive Arms Race: Redefining Robustness in AI Security","created":"2023-12-20","cited_by_count":5,"area":null},{"id":"2412.18442","title":"SoK: On the Offensive Potential of AI","created":"2024-12-24","cited_by_count":5,"area":null},{"id":"2112.01724","title":"Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach","created":"2021-12-03","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":"2210.02840","title":"Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet Detection","created":"2022-10-06","cited_by_count":3,"area":null},{"id":"2305.09674","title":"Quantum Machine Learning for Malware Classification","created":"2023-05-09","cited_by_count":3,"area":null},{"id":"2306.13587","title":"Creating Valid Adversarial Examples of Malware","created":"2023-06-23","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":"2308.16562","title":"The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning","created":"2023-08-31","cited_by_count":3,"area":null},{"id":"2402.19027","title":"How to Train your Antivirus: RL-based Hardening through the Problem-Space","created":"2024-02-29","cited_by_count":3,"area":null},{"id":"2506.12113","title":"Semantic Preprocessing for LLM-based Malware Analysis","created":"2025-06-13","cited_by_count":3,"area":null},{"id":"2102.00898","title":"DRLDO: A novel DRL based De-ObfuscationSystem for Defense against Metamorphic Malware","created":"2021-02-01","cited_by_count":2,"area":null},{"id":"2106.07860","title":"Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery","created":"2021-06-15","cited_by_count":2,"area":null},{"id":"2304.07360","title":"Combining Generators of Adversarial Malware Examples to Increase Evasion Rate","created":"2023-04-14","cited_by_count":2,"area":null},{"id":"2109.11542","title":"ADVERSARIALuscator: An Adversarial-DRL Based Obfuscator and Metamorphic Malware SwarmGenerator","created":"2021-09-23","cited_by_count":1,"area":null},{"id":"2204.06241","title":"Stealing and Evading Malware Classifiers and Antivirus at Low False Positive Conditions","created":"2022-04-13","cited_by_count":1,"area":null},{"id":"2209.09033","title":"A Transferable and Automatic Tuning of Deep Reinforcement Learning for Cost Effective Phishing Detection","created":"2022-09-19","cited_by_count":1,"area":null},{"id":"2402.02600","title":"Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach","created":"2024-02-04","cited_by_count":1,"area":null},{"id":"2404.18541","title":"Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research","created":"2024-04-29","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":"2507.08597","title":"ADAPT: A Pseudo-labeling Approach to Combat Concept Drift in Malware Detection","created":"2025-07-11","cited_by_count":1,"area":null},{"id":"2111.11487","title":"A Comparison of State-of-the-Art Techniques for Generating Adversarial Malware Binaries","created":"2021-11-22","cited_by_count":0,"area":null},{"id":"2209.02453","title":"Instance Attack:An Explanation-based Vulnerability Analysis Framework Against DNNs for Malware Detection","created":"2022-09-06","cited_by_count":0,"area":null},{"id":"2210.15429","title":"Multi-view Representation Learning from Malware to Defend Against Adversarial Variants","created":"2022-10-25","cited_by_count":0,"area":null},{"id":"2306.15559","title":"RansomAI: AI-powered Ransomware for Stealthy Encryption","created":"2023-06-27","cited_by_count":0,"area":null},{"id":"2309.13841","title":"On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors","created":"2023-09-25","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":"2408.02310","title":"On the Robustness of Malware Detectors to Adversarial Samples","created":"2024-08-05","cited_by_count":0,"area":null},{"id":"2502.02290","title":"FRAUD-RLA: A new reinforcement learning adversarial attack against credit card fraud detection","created":"2025-02-04","cited_by_count":0,"area":null},{"id":"2507.03993","title":"MalVol-25: A Diverse, Labelled and Detailed Volatile Memory Dataset for Malware Detection and Response Testing and Validation","created":"2025-07-05","cited_by_count":0,"area":null},{"id":"2508.10038","title":"Certifiably robust malware detectors by design","created":"2025-08-10","cited_by_count":0,"area":null},{"id":"2604.21310","title":"Adversarial Evasion in Non-Stationary Malware Detection: Minimizing Drift Signals through Similarity-Constrained Perturbations","created":"2026-04-23","cited_by_count":0,"area":null},{"id":"2604.22639","title":"Adversarial Malware Generation in Linux ELF Binaries via Semantic-Preserving Transformations","created":"2026-04-24","cited_by_count":0,"area":null},{"id":"2605.18624","title":"Learning to Look Benign: Targeted Evasion of Malware Detectors via API Import Injection","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2605.23623","title":"Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2605.25937","title":"Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation","created":"2026-05-25","cited_by_count":0,"area":null},{"id":"2607.24896","title":"Latent Stability Analysis of Malware Representations Under Feature-Space Perturbations","created":"2026-07-27","cited_by_count":0,"area":null},{"id":"2608.00745","title":"Whetstones: Measuring Coevolution Between Adaptive Malware and Behavioral Defense","created":"2026-08-01","cited_by_count":0,"area":null}],"next_cursor":null}