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Lessons Learned on Machine Learning for Computer Security., , , , , , , and . IEEE Secur. Priv., 21 (5): 72-77 (September 2023)TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)., , , , , , , and . CoRR, (2024)INSOMNIA: Towards Concept-Drift Robustness in Network Intrusion Detection., , , , , and . AISec@CCS, page 111-122. ACM, (2021)Is It Overkill? Analyzing Feature-Space Concept Drift in Malware Detectors., , , , , , , , and . SP (Workshops), page 21-28. IEEE, (2023)Dos and Don'ts of Machine Learning in Computer Security., , , , , , , and . CoRR, (2020)TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time., , , , and . USENIX Security Symposium, page 729-746. USENIX Association, (2019)Dos and Don'ts of Machine Learning in Computer Security., , , , , , , and . USENIX Security Symposium, page 3971-3988. USENIX Association, (2022)Enabling Fair ML Evaluations for Security., , , , and . ACM Conference on Computer and Communications Security, page 2264-2266. ACM, (2018)Intriguing Properties of Adversarial ML Attacks in the Problem Space., , , and . CoRR, (2019)Are Machine Learning Models for Malware Detection Ready for Prime Time?, , , and . IEEE Secur. Priv., 21 (2): 53-56 (March 2023)