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Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks

, , and . (2018)cite arxiv:1811.00830Comment: Major revision submitted to ACM Computing Surveys.

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Cybersecurity and AI: The PRALab Research Experience., , , , , , , and . Ital-IA, volume 3486 of CEUR Workshop Proceedings, page 426-431. CEUR-WS.org, (2023)On the effectiveness of system API-related information for Android ransomware detection., , , , , and . Comput. Secur., (2019)Enhancing android malware detection explainability through function call graph APIs., , , and . J. Inf. Secur. Appl., (February 2024)PowerDecode: A PowerShell Script Decoder Dedicated to Malware Analysis., , , and . ITASEC, volume 2940 of CEUR Workshop Proceedings, page 219-232. CEUR-WS.org, (2021)Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?, , , , , , and . CoRR, (2020)On the Feasibility of Adversarial Sample Creation Using the Android System API., , , and . Inf., 11 (9): 433 (2020)Evasion attacks against machine learning at test time, , , , , , , and . Joint European Conference on Machine Learning and Knowledge Discovery in Databases, page 387--402. Springer, (2013)Adversarial Detection of Flash Malware: Limitations and Open Issues., , , , and . Comput. Secur., (2020)Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection., , , , , , , , and . IEEE Trans. Dependable Secur. Comput., 16 (4): 711-724 (2019)An Evasion Resilient Approach to the Detection of Malicious PDF Files., , , and . ICISSP (Revised Selected Papers), volume 576 of Communications in Computer and Information Science, page 68-85. Springer, (2015)