Research by Rachel Greenstadt, PhD, an associate professor of computer science in the College of Computing & Informatics (CCI), was featured in an August 10 Wired article. Greenstadt, along with Aylin Caliskan, Greenstadt's former PhD student and now an assistant professor at George Washington University, developed a program that can identify anonymous computer coders by analyzing characteristics of their coding style. Greenstadt and Caliskan presented their findings during a session at DEF CON 26, held in Las Vegas from August 9-12, 2018, where it gained attention from the cybersecurity community.
Caliskan and Greenstadt say their work could be used to tell whether a programming student plagiarized, or whether a developer violated a noncompete clause in their employment contract. Security researchers could also potentially use it to help determine who might have created a specific type of malware.
Read the full article at this link: https://www.wired.com/story/machine-learning-identify-anonymous-code/.