This is not at all my own idea and this is, basically, the only thing that academia researches as of today: almost every single academic paper published in the last years talking about binary diffing (or, as academia calls it "Binary Code Similarity Analysis") is based on "machine learning" techniques.
Some popular academic examples: DeepBinDiff or BindiffNN. Don't worry if you don't know them. Nobody uses them. At all.
It's very sad, but it's always a damn waste of time reading academic research about binary diffing or, as it's called at the academia, about binary code similarity analysis. It's either all fairytales that cannot be proved or, plainly, false and/or wrong.
An example? One paper that I have re-read today says that #BinDiff and #Diaphora are mono-architecture and totally discard these tools for the paper. LOL.
One question regarding #bindiffing: Have you ever used a tool called #DeepBinDiff? I am not talking about "BinDiff" but rather about "DeepBinDiff".
Joxean Koret (@matalaz)