Timing as a side-channel vulnerability: neural network analysis of generalized PIN prediction

Abstract

This study explores a potential side-channel vulnerability in Personal Identification Number (PIN) entry systems (PEDs), by analyzing keystroke timing patterns with machine learning techniques. PIN-based authentication, ubiquitous in securing physical and digital access, struggles to balance usability and security, with prior research focusing on direct threats like visual observation and proposing countermeasures such as extended PINs, or haptic feedback. This research hypothesizes that human timing behavior during 4-digit PIN entry may reveal detectable patterns exploitable by machine learning to predict PINs, posing a novel risk distinct from traditional attack vectors. Using a supervised learning model trained on latency data from a virtual keypad, the study assesses whether these patterns generalize across users, testing a 10,000-class classification problem with out-of-sample k-fold cross-validation. Results show limited pattern detection—a top-1 accuracy of 0.115% and a top-10 of 1.198%, exceeding random guessing but insufficient for practical targeted attacks— with a bias toward top-row digits (e.g., 73, 77), possibly reflecting psychological preferences for numbers like 3 and 7. While generalization remains limited by participant variability, and dataset constraints, the findings suggest a context-specific vulnerability more relevant to large-scale, non-targeted scenarios than individual breaches. This work underscores the need to consider subtle behavioral leaks in PED design, advocating for continued innovation to address emerging machine learning-driven threats.