DefCon presenters explore programmer de-anonymization, stylistic fingerprints

Just like cooking, drawing, or writing, when it comes to coding, every programmer has a personal preference in how the algorithm is laid out, how certain pieces of code are strung together, and so on, ultimately creating a “signature” of sorts. Now researchers have found that by using machine learning, it can be used to help identify pieces of code even if they were written anonymously.This is according to Rachel Greenstadt, an associate professor of computer science at Drexel University, who together with Aylin Caliskan who was her former PhD student and is now an assistant professor at George Washington University, who presented their findings at the DefCon hacking experience.How the AI works is by being fed examples of a programmer’s work where it studies the coding structure. From there, it will then be able to train itself to be capable of spotting that programmer’s work in the future. Based on the testing that they did using Google’s Code Jam, their AI appeared to be relat

DefCon presenters explore programmer de-anonymization, stylistic fingerprints

One of the nicer things about higher education: Gaining awareness of the signature styles of authors, painters, musicians even before we are told their names. Well, signature styles are not ...

Wed 15 Aug 18 from TechXplore

Machine Learning Could Be Used To Identify Anonymous Code

Just like cooking, drawing, or writing, when it comes to coding, every programmer has a personal preference in how the algorithm is laid out, how certain pieces of code are strung together, ...

Sun 12 Aug 18 from Ubergizmo

Machine learning can 'fingerprint' programmers

Programmers tend to have their own distinct styles, but it's not really feasible to pore over many lines of code looking for telltale cues about a program's author. Now, that ...

Sun 12 Aug 18 from Engadget

Machine Learning Can Identify the Authors of Anonymous Code

Researchers have repeatedly shown that writing samples, even those in artificial languages, contain a unique fingerprint that's hard to hide.

Fri 10 Aug 18 from Wired Security

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