{"total":48,"rows":[{"id":"2109.13916","title":"Unsolved Problems in ML Safety","created":"2021-09-28","cited_by_count":292,"area":null},{"id":"2206.09707","title":"The Role of Machine Learning in Cybersecurity","created":"2022-06-20","cited_by_count":22,"area":null},{"id":"2506.05074","title":"EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers","created":"2025-06-05","cited_by_count":19,"area":null},{"id":"2111.15031","title":"MOTIF: A Large Malware Reference Dataset with Ground Truth Family Labels","created":"2021-11-29","cited_by_count":17,"area":null},{"id":"2504.07137","title":"Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering","created":"2025-04-07","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":"2405.03991","title":"Assemblage: Automatic Binary Dataset Construction for Machine Learning","created":"2024-05-07","cited_by_count":12,"area":null},{"id":"2108.04081","title":"Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraints","created":"2021-08-09","cited_by_count":11,"area":null},{"id":"2203.12980","title":"MERLIN -- Malware Evasion with Reinforcement LearnINg","created":"2022-03-24","cited_by_count":11,"area":null},{"id":"2111.10085","title":"Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors","created":"2021-11-19","cited_by_count":7,"area":null},{"id":"2502.10556","title":"Recent Advances in Malware Detection: Graph Learning and Explainability","created":"2025-02-14","cited_by_count":7,"area":null},{"id":"2212.02663","title":"Efficient Malware Analysis Using Metric Embeddings","created":"2022-12-05","cited_by_count":6,"area":null},{"id":"2410.22677","title":"Is Function Similarity Over-Engineered? Building a Benchmark","created":"2024-10-30","cited_by_count":6,"area":null},{"id":"2505.18551","title":"LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift Analysis","created":"2025-05-24","cited_by_count":6,"area":null},{"id":"2306.06228","title":"AVScan2Vec: Feature Learning on Antivirus Scan Data for Production-Scale Malware Corpora","created":"2023-06-09","cited_by_count":5,"area":null},{"id":"2210.03702","title":"Class-wise and reduced calibration methods","created":"2022-10-07","cited_by_count":4,"area":null},{"id":"2312.15813","title":"Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits!","created":"2023-12-25","cited_by_count":4,"area":null},{"id":"2410.23601","title":"Stabilizing Linear Passive-Aggressive Online Learning with Weighted Reservoir Sampling","created":"2024-10-31","cited_by_count":4,"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":"2506.12113","title":"Semantic Preprocessing for LLM-based Malware Analysis","created":"2025-06-13","cited_by_count":3,"area":null},{"id":"2209.03188","title":"Avast-CTU Public CAPE Dataset","created":"2022-09-06","cited_by_count":2,"area":null},{"id":"2306.07989","title":"A Survey on Cross-Architectural IoT Malware Threat Hunting","created":"2023-06-09","cited_by_count":2,"area":null},{"id":"2511.17573","title":"Binary BPE: A Family of Cross-Platform Tokenizers for Binary Analysis","created":"2025-11-14","cited_by_count":2,"area":null},{"id":"2606.28929","title":"Cybersecurity is the True Frontier for Generative AI Success or Failure","created":"2026-06-27","cited_by_count":2,"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":"2206.03265","title":"Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detection","created":"2022-06-07","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":"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":"2506.17309","title":"Efficient Malware Detection with Optimized Learning on High-Dimensional Features","created":"2025-06-18","cited_by_count":1,"area":null},{"id":"2507.04372","title":"Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classification","created":"2025-07-06","cited_by_count":1,"area":null},{"id":"2508.09652","title":"Demystifying the Role of Rule-based Detection in AI Systems for Windows Malware Detection","created":"2025-08-13","cited_by_count":1,"area":null},{"id":"2511.22095","title":"Binary-30K: A Heterogeneous Dataset for Deep Learning in Binary Analysis and Malware Detection","created":"2025-11-27","cited_by_count":1,"area":null},{"id":"2511.23198","title":"Clustering Malware at Scale: A First Full-Benchmark Study","created":"2025-11-28","cited_by_count":1,"area":null},{"id":"2604.02490","title":"Automated Malware Family Classification using Weighted Hierarchical Ensembles of Large Language Models","created":"2026-04-02","cited_by_count":1,"area":null},{"id":"2605.00297","title":"Trident: Improving Malware Detection with LLMs and Behavioral Features","created":"2026-04-30","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":"2206.06256","title":"On the impact of dataset size and class imbalance in evaluating machine-learning-based windows malware detection techniques","created":"2022-06-13","cited_by_count":0,"area":null},{"id":"2310.18165","title":"Enhancing Enterprise Network Security: Comparing Machine-Level and Process-Level Analysis for Dynamic Malware Detection","created":"2023-10-27","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":"2508.06734","title":"Evaluating Out-of-Distribution Robustness in Graph-Based Android Malware Classification: A New Principled Benchmark","created":"2025-08-08","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":"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},{"id":"2601.17907","title":"FARM: Few-shot Adaptive Malware Family Classification under Concept Drift","created":"2026-01-25","cited_by_count":0,"area":null},{"id":"2603.26632","title":"Machine Learning Transferability for Malware Detection","created":"2026-03-27","cited_by_count":0,"area":null},{"id":"2605.05807","title":"LCC-LLM: Leveraging Code-Centric Large Language Models for Malware Attribution","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2607.24177","title":"EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability","created":"2026-07-27","cited_by_count":0,"area":null},{"id":"2609.23995","title":"Simpler Methods Work Better for L1 Penalized Logistic Models and Large Datasets","created":"2026-09-21","cited_by_count":0,"area":null},{"id":"2609.24280","title":"Temporal Generalization and Explanation Stability of Control Flow Graph Neural Networks for Malware Detection","created":"2026-09-21","cited_by_count":0,"area":null}],"next_cursor":null}