Abstract: Using Stylometry to Attribute Programmers and Writers
In this talk, I will discuss my lab’s work in the emerging field of adversarial stylometry and machine learning. Machine learning algorithms are increasingly being used in security and privacy domains, in areas that go beyond intrusion or spam detection. For example, in digital forensics, questions often arise about the authors of documents: their identity, demographic background, and whether they can be linked to other documents. The field of stylometry uses linguistic features and machine learning techniques to answer these questions. We have applied stylometry to difficult domains such as underground hacker forums, open source projects (code), and tweets. I will discuss our Doppelgänger Finder algorithm, which enables us to group Sybil accounts on underground forums and detect blogs from Twitter feeds and reddit comments. In addition, I will discuss our work attributing unknown source code and binaries.
Rachel Greenstadt an Associate Professor of Computer Science at Drexel University, where she researches the privacy and security properties of intelligent systems and the economics of electronic privacy and information security. Her work is at “layer 8” of the network—analyzing the content. She runs the Privacy, Security, and Automation Laboratory (PSAL). PSAL is a research group focused on designing more trustworthy intelligent systems—systems that act not only autonomously, but also with integrity, so that they can be trusted with important data and decisions. The lab takes a highly interdisciplinary approach to this research, incorporating ideas from artificial intelligence, psychology, economics, data privacy, and system security. However, a common thread of this work has been studying information flow, trustworthiness, and control. Recently, much of our work has focused on using machine learning to better understand textual communication. This work has often been featured in the press.
She also advises the Drexel Women in Computing Society.
Dr Greenstadt holds a SB from MIT in Computer Science and Engineering, a MEng from MIT in Electrical Engineering and Computer Science, and a PhD from Harvard University, School of Engineering and Applied Science in Computer Science.