{"total":41,"rows":[{"id":"2205.11275","title":"RL with KL penalties is better viewed as Bayesian inference","created":"2022-05-23","cited_by_count":135,"area":null},{"id":"2105.00203","title":"Adversarial Example Detection for DNN Models: A Review and Experimental Comparison","created":"2021-05-01","cited_by_count":52,"area":null},{"id":"2103.05292","title":"Deep Learning for Android Malware Defenses: a Systematic Literature Review","created":"2021-03-09","cited_by_count":18,"area":null},{"id":"2102.04661","title":"Security and Privacy for Artificial Intelligence: Opportunities and Challenges","created":"2021-02-09","cited_by_count":16,"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":"2306.12941","title":"Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models","created":"2023-06-22","cited_by_count":12,"area":null},{"id":"2101.03042","title":"Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspective","created":"2021-01-08","cited_by_count":9,"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":"2111.10085","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":"2104.02189","title":"Robust Classification Under $\\ell_0$ Attack for the Gaussian Mixture Model","created":"2021-04-05","cited_by_count":5,"area":null},{"id":"2107.01185","title":"Artificial Neural Network for Cybersecurity: A Comprehensive Review","created":"2021-06-20","cited_by_count":4,"area":null},{"id":"2212.00727","title":"Adversarial Artifact Detection in EEG-Based Brain-Computer Interfaces","created":"2022-11-28","cited_by_count":4,"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":"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":"2106.04435","title":"Enhancing Robustness of Neural Networks through Fourier Stabilization","created":"2021-06-08","cited_by_count":2,"area":null},{"id":"2110.11987","title":"Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations","created":"2021-10-22","cited_by_count":2,"area":null},{"id":"2211.14706","title":"Neural Network Verification as Piecewise Linear Optimization: Formulations for the Composition of Staircase Functions","created":"2022-11-27","cited_by_count":2,"area":null},{"id":"2305.19593","title":"Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks using a Malware Dataset: A Comparative Analysis","created":"2023-05-31","cited_by_count":2,"area":null},{"id":"2405.10924","title":"Boosting Few-Pixel Robustness Verification via Covering Verification Designs","created":"2024-05-17","cited_by_count":2,"area":null},{"id":"2103.10787","title":"LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attack","created":"2021-03-19","cited_by_count":1,"area":null},{"id":"2201.09369","title":"Efficient and Robust Classification for Sparse Attacks","created":"2022-01-23","cited_by_count":1,"area":null},{"id":"2205.03850","title":"SeqNet: An Efficient Neural Network for Automatic Malware Detection","created":"2022-05-08","cited_by_count":1,"area":null},{"id":"2208.06092","title":"On deceiving malware classification with section injection","created":"2022-08-12","cited_by_count":1,"area":null},{"id":"2306.00934","title":"Interpreting GNN-based IDS Detections Using Provenance Graph Structural Features","created":"2023-06-01","cited_by_count":1,"area":null},{"id":"2412.12217","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","created":"2024-12-16","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":"2509.25927","title":"The Impact of Scaling Training Data on Adversarial Robustness","created":"2025-09-30","cited_by_count":1,"area":null},{"id":"2602.06395","title":"Empirical Analysis of Adversarial Robustness and Explainability Drift in Cybersecurity Classifiers","created":"2026-02-06","cited_by_count":1,"area":null},{"id":"2102.02923","title":"PredCoin: Defense against Query-based Hard-label Attack","created":"2021-02-04","cited_by_count":0,"area":null},{"id":"2106.09872","title":"Analyzing Adversarial Robustness of Deep Neural Networks in Pixel Space: a Semantic Perspective","created":"2021-06-18","cited_by_count":0,"area":null},{"id":"2107.03806","title":"Output Randomization: A Novel Defense for both White-box and Black-box Adversarial Models","created":"2021-07-08","cited_by_count":0,"area":null},{"id":"2109.01558","title":"Learning Neural Models for Natural Language Processing in the Face of Distributional Shift","created":"2021-09-03","cited_by_count":0,"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":"2203.04855","title":"Binary Classification Under $\\ell_0$ Attacks for General Noise Distribution","created":"2022-03-09","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":"2402.03576","title":"Generalization Properties of Adversarial Training for $\\ell_0$-Bounded Adversarial Attacks","created":"2024-02-05","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":"2607.01679","title":"Beyond Gradient-Based Attacks: Adversarial Robustness and Explainability Stability in Cybersecurity Classifiers","created":"2026-07-02","cited_by_count":0,"area":null}],"next_cursor":null}