May 8, 2024, 4:42 a.m. | Chang Liu, Rebecca Saul, Yihao Sun, Edward Raff, Maya Fuchs, Townsend Southard Pantano, James Holt, Kristopher Micinski

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.03991v1 Announce Type: cross
Abstract: Binary code is pervasive, and binary analysis is a key task in reverse engineering, malware classification, and vulnerability discovery. Unfortunately, while there exist large corpuses of malicious binaries, obtaining high-quality corpuses of benign binaries for modern systems has proven challenging (e.g., due to licensing issues). Consequently, machine learning based pipelines for binary analysis utilize either costly commercial corpuses (e.g., VirusTotal) or open-source binaries (e.g., coreutils) available in limited quantities. To address these issues, we present …

arxiv binary construction cs.cr cs.lg dataset machine machine learning type

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