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Dos and Don'ts of Machine Learning in Computer Security.

, , , , , , , и . USENIX Security Symposium, стр. 3971-3988. USENIX Association, (2022)

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INSOMNIA: Towards Concept-Drift Robustness in Network Intrusion Detection., , , , , и . AISec@CCS, стр. 111-122. ACM, (2021)Is It Overkill? Analyzing Feature-Space Concept Drift in Malware Detectors., , , , , , , , и . SP (Workshops), стр. 21-28. IEEE, (2023)TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time., , , , и . USENIX Security Symposium, стр. 729-746. USENIX Association, (2019)Dos and Don'ts of Machine Learning in Computer Security., , , , , , , и . USENIX Security Symposium, стр. 3971-3988. USENIX Association, (2022)Enabling Fair ML Evaluations for Security., , , , и . ACM Conference on Computer and Communications Security, стр. 2264-2266. ACM, (2018)Transcending TRANSCEND: Revisiting Malware Classification in the Presence of Concept Drift., , , и . SP, стр. 805-823. IEEE, (2022)Investigating Labelless Drift Adaptation for Malware Detection., , , и . AISec@CCS, стр. 123-134. ACM, (2021)Intriguing Properties of Adversarial ML Attacks in the Problem Space., , , и . SP, стр. 1332-1349. IEEE, (2020)Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers., , , , , , , и . SP, стр. 719-736. IEEE, (2023)