{"total":18,"rows":[{"id":"2302.04332","title":"Continuous Learning for Android Malware Detection","created":"2023-02-08","cited_by_count":33,"area":null},{"id":"2206.09707","title":"The Role of Machine Learning in Cybersecurity","created":"2022-06-20","cited_by_count":22,"area":null},{"id":"2502.13055","title":"LAMD: Context-driven Android Malware Detection and Classification with LLMs","created":"2025-02-18","cited_by_count":18,"area":null},{"id":"2205.08944","title":"SoK: The Impact of Unlabelled Data in Cyberthreat Detection","created":"2022-05-18","cited_by_count":15,"area":null},{"id":"2105.11363","title":"Learning Security Classifiers with Verified Global Robustness Properties","created":"2021-05-24","cited_by_count":14,"area":null},{"id":"2305.00550","title":"SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection","created":"2023-04-30","cited_by_count":13,"area":null},{"id":"2307.14657","title":"Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance","created":"2023-07-27","cited_by_count":13,"area":null},{"id":"2402.01359","title":"TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)","created":"2024-02-02","cited_by_count":9,"area":null},{"id":"2203.04686","title":"The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems","created":"2022-03-09","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":"2205.12569","title":"Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine Learning","created":"2022-05-25","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":"2402.02953","title":"Unraveling the Key of Machine Learning-based Android Malware Detection","created":"2024-02-05","cited_by_count":4,"area":null},{"id":"2409.13213","title":"MalMixer: Few-Shot Malware Classification with Retrieval-Augmented Semi-Supervised Learning","created":"2024-09-20","cited_by_count":3,"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":"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":"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":"2605.24903","title":"SEED: Semi-supervised Continual MalwarE Detection for Tackling ConcEpt Drift on a BuDget","created":"2026-05-24","cited_by_count":0,"area":null}],"next_cursor":null}