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UID:pretalx-pts2026-3EF7YY@cfp.pass-the-salt.org
DTSTART;TZID=CET:20260701T175500
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DESCRIPTION:If you want to understand binary code\, you first need to disas
 semble and decompile it. But to do that\, you must know the target archite
 cture the code was built for. Think of it like using Google Translate: if 
 you feed a Spanish text but tell the tool it’s German\, the English outp
 ut will be complete nonsense. The same is true for machine code: the wrong
  architecture produces garbage.\n\nWhen working with executable file forma
 ts such as ELF\, Mach-O\, or PE\, this information is conveniently stored 
 in file metadata. That is how your favorite disassembler knows what to do.
  With raw binary files\, however\, there is no metadata. Just raw bytes. A
 nd that makes architecture detection much harder.\n\nIn this rump I'll pre
 sent a machine learning approach using classifiers to train a model capabl
 e of identifying architecture from binaries in less than a second\, with a
  model weighting less than 10MB\, that's correct 95% of the time over all 
 28 architecture variants it supports. I'll present our approach and how yo
 u can reproduce it using open source tools such as sklearn.
DTSTAMP:20260714T235159Z
LOCATION:Amphitheater 122
SUMMARY:Architecture Detection: Teaching Machines to Read Raw Binaries - Qu
 entin Kaiser
URL:https://cfp.pass-the-salt.org/pts2026/talk/3EF7YY/
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