Chinmay Sahu

Chinmay Sahu, Mahesh Banavar, Stephanie Schuckers

In this paper, we propose a new technique to uniquely classify and identify multiple users accessing a single application using keystroke dynamics. This problem is usually encountered when multiple users have legitimate access to shared computers and accounts, where, at times, one user can inadvertently be logged in on another user's account. Since the login processes are usually bypassed at this stage, we rely on keystroke dynamics in order to tell users apart. Our algorithm uses the quantile transform and techniques from localization to classify and identify users. Specifically, we use an algorithm known as ordinal Unfolding based Localization (UNLOC), which uses only ordinal data obtained from comparing distance proxies, by "locating" users in a reduced PCA/Kernel-PCA/t-SNE space based on their typing patterns. Our results are validated with the help of benchmark keystroke datasets and show that our algorithm outperforms other methods.

Chinmay Sahu, Blaine Ayotte, Mahesh K. Banavar

In this innovative practice work-in-progress paper, we compare two different methods to teach machine learning concepts to undergraduate students in Electrical Engineering. While machine learning is now being offered as a senior-level elective in several curricula, this does not mean all students are exposed to it. Exposure to the concepts and practical applications of machine learning will assist in the creation of a workforce ready to tackle problems related to machine learning, currently a hot topic in industry. Preliminary assessments indicate that this approach promotes student learning. While students prefer the proposed side-by-side teaching approach, numerical comparisons show that the workshop approach may be more effective for student learning, indicating that further work in this area is required.