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VCCFinder: Finding Potential Vulnerabilities in Open-Source Projects to Assist Code Audits.

, , , , , , , and . ACM Conference on Computer and Communications Security, page 426-437. ACM, (2015)

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Dos and Don'ts of Machine Learning in Computer Security., , , , , , , and . CoRR, (2020)Dos and Don'ts of Machine Learning in Computer Security., , , , , , , and . USENIX Security Symposium, page 3971-3988. USENIX Association, (2022)Adversarial Preprocessing: Understanding and Preventing Image-Scaling Attacks in Machine Learning., , , , and . USENIX Security Symposium, page 1363-1380. USENIX Association, (2020)Privacy-Enhanced Fraud Detection with Bloom Filters., , , , and . SecureComm (1), volume 254 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, page 396-415. Springer, (2018)Efficient and Explainable Detection of Mobile Malware with Machine Learning.. Braunschweig University of Technology, Germany, (2019)Against All Odds: Winning the Defense Challenge in an Evasion Competition with Diversification., , , , and . CoRR, (2020)Evaluating Explanation Methods for Deep Learning in Security., , , and . EuroS&P, page 158-174. IEEE, (2020)Don't Paint It Black: White-Box Explanations for Deep Learning in Computer Security., , , and . CoRR, (2019)TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)., , , , , , , and . CoRR, (2024)Drift Forensics of Malware Classifiers., , , , , and . AISec@CCS, page 197-207. ACM, (2023)