{"total":52,"rows":[{"id":"2106.09947","title":"Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples","created":"2021-06-18","cited_by_count":23,"area":null},{"id":"2206.09707","title":"The Role of Machine Learning in Cybersecurity","created":"2022-06-20","cited_by_count":22,"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":"2202.10276","title":"Poisoning Attacks and Defenses on Artificial Intelligence: A Survey","created":"2022-02-21","cited_by_count":16,"area":"safety"},{"id":"2208.13058","title":"Adversarial Robustness for Tabular Data through Cost and Utility Awareness","created":"2022-08-27","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":"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":"2305.00550","title":"SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection","created":"2023-04-30","cited_by_count":13,"area":null},{"id":"2309.01866","title":"Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting","created":"2023-09-05","cited_by_count":10,"area":null},{"id":"2303.09767","title":"It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness","created":"2023-03-17","cited_by_count":9,"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":"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":"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":"2210.13660","title":"Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning","created":"2022-10-24","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":"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":"2109.09654","title":"Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?","created":"2021-09-20","cited_by_count":5,"area":null},{"id":"2206.08004","title":"When a RF Beats a CNN and GRU, Together -- A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic Classification","created":"2022-06-16","cited_by_count":5,"area":null},{"id":"2212.05380","title":"Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors","created":"2022-12-11","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":"2409.13723","title":"Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges","created":"2024-09-09","cited_by_count":5,"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":"2310.03166","title":"Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors","created":"2023-10-04","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":"2106.12328","title":"Learning Explainable Representations of Malware Behavior","created":"2021-06-23","cited_by_count":2,"area":null},{"id":"2107.02896","title":"Efficient Detection of Botnet Traffic by features selection and Decision Trees","created":"2021-06-30","cited_by_count":2,"area":null},{"id":"2207.05548","title":"Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware","created":"2022-07-12","cited_by_count":2,"area":null},{"id":"2210.11592","title":"New data poison attacks on machine learning classifiers for mobile exfiltration","created":"2022-10-20","cited_by_count":2,"area":"safety"},{"id":"2501.13782","title":"Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems","created":"2025-01-23","cited_by_count":2,"area":null},{"id":"2202.13922","title":"MaMaDroid2.0 -- The Holes of Control Flow Graphs","created":"2022-02-28","cited_by_count":1,"area":null},{"id":"2206.10144","title":"Open-Source Framework for Encrypted Internet and Malicious Traffic Classification","created":"2022-06-21","cited_by_count":1,"area":null},{"id":"2306.07118","title":"On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities","created":"2023-06-12","cited_by_count":1,"area":null},{"id":"2308.04964","title":"ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine Learning","created":"2023-08-09","cited_by_count":1,"area":null},{"id":"2406.13547","title":"ModSec-Learn: Boosting ModSecurity with Machine Learning","created":"2024-06-19","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":"2102.13376","title":"Collective Intelligence: Decentralized Learning for Android Malware Detection in IoT with Blockchain","created":"2021-02-26","cited_by_count":0,"area":null},{"id":"2111.05108","title":"\"How Does It Detect A Malicious App?\" Explaining the Predictions of AI-based Android Malware Detector","created":"2021-11-06","cited_by_count":0,"area":null},{"id":"2203.02121","title":"Adversarial Patterns: Building Robust Android Malware Classifiers","created":"2022-03-04","cited_by_count":0,"area":null},{"id":"2207.00740","title":"PhilaeX: Explaining the Failure and Success of AI Models in Malware Detection","created":"2022-07-02","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":"2403.11206","title":"CBR -- Boosting Adaptive Classification By Retrieval of Encrypted Network Traffic with Out-of-distribution","created":"2024-03-17","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":"2410.15028","title":"A Novel Reinforcement Learning Model for Post-Incident Malware Investigations","created":"2024-10-19","cited_by_count":0,"area":"deeprl"},{"id":"2506.22750","title":"Enhancing Android Malware Detection with Retrieval-Augmented Generation","created":"2025-06-28","cited_by_count":0,"area":"rag"},{"id":"2508.10038","title":"Certifiably robust malware detectors by design","created":"2025-08-10","cited_by_count":0,"area":null},{"id":"2510.12310","title":"DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detection","created":"2025-10-14","cited_by_count":0,"area":null},{"id":"2512.04338","title":"One Detector Fits All: Robust and Adaptive Detection of Malicious Packages from PyPI to Enterprises","created":"2025-12-03","cited_by_count":0,"area":null},{"id":"2608.28499","title":"REPLICANT: Learning Policies for Evading and Hardening Malware Detectors","created":"2026-08-28","cited_by_count":0,"area":null}],"next_cursor":null}