Showing 1–18 of 18 results
/ Date/ Name
Jul 20, 2018TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and TimeFeb 2, 2024TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)Oct 8, 2020Transcending Transcend: Revisiting Malware Classification in the Presence of Concept DriftAug 26, 2025DRMD: Deep Reinforcement Learning for Malware Detection under Concept DriftJun 30, 2025Beyond the TESSERACT:Trustworthy Dataset Curation for Sound Evaluations of Android Malware ClassifiersFeb 11, 2022Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware ClassifiersNov 5, 2019Intriguing Properties of Adversarial ML Attacks in the Problem Space [Extended Version]Jun 24, 2025KnowML: Improving Generalization of ML-NIDS with Attack Knowledge GraphsFeb 12, 2021Realizable Universal Adversarial Perturbations for MalwareFeb 24, 2025Unveiling ECC Vulnerabilities: LSTM Networks for Operation Recognition in Side-Channel AttacksFeb 9, 2026SoK: The Pitfalls of Deep Reinforcement Learning for CybersecurityFeb 29, 2024How to Train your Antivirus: RL-based Hardening through the Problem-SpaceDec 24, 2024On the Effectiveness of Adversarial Training on Malware ClassifiersMay 9, 2024ML-Based Behavioral Malware Detection Is Far From a Solved ProblemFeb 5, 2024Unraveling the Key of Machine Learning-based Android Malware DetectionDec 29, 2022"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and PracticeOct 19, 2020Dos and Don'ts of Machine Learning in Computer SecurityDec 20, 2023The Adaptive Arms Race: Redefining Robustness in AI Security