Robust Keystroke Biometric Anomaly Detection

TL;DR

This paper presents a robust keystroke anomaly detection method evaluated in a large-scale competition, emphasizing preprocessing and normalization over the specific detection algorithms, achieving state-of-the-art results.

Contribution

It introduces a keystroke alignment preprocessing algorithm and evaluates fifteen top anomaly detection systems, highlighting the importance of preprocessing and normalization techniques.

Findings

Manhattan distance achieved 5.32% EER

Preprocessing and score normalization significantly improved performance

All top systems outperformed previous submissions

Abstract

The Keystroke Biometrics Ongoing Competition (KBOC) presented an anomaly detection challenge with a public keystroke dataset containing a large number of subjects and real-world aspects. Over 300 subjects typed case-insensitive repetitions of their first and last name, and as a result, keystroke sequences could vary in length and order depending on the usage of modifier keys. To deal with this, a keystroke alignment preprocessing algorithm was developed to establish a semantic correspondence between keystrokes in mismatched sequences. The method is robust in the sense that query keystroke sequences need only approximately match a target sequence, and alignment is agnostic to the particular anomaly detector used. This paper describes the fifteen best-performing anomaly detection systems submitted to the KBOC, which ranged from auto-encoding neural networks to ensemble methods. Manhattan distance achieved the lowest equal error rate of 5.32%, while all fifteen systems performed better than any other submission. Performance gains are shown to be due in large part not to the particular anomaly detector, but to preprocessing and score normalization techniques.