# Robust Keystroke Biometric Anomaly Detection

arXiv: https://arxiv.org/abs/1606.09075 · HTML: https://mlsanity.com/paper/1606.09075/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2016.

## Built on by (most cited first, 3 of 3)

- [TypeNet: Deep Learning Keystroke Biometrics](https://mlsanity.com/paper/2101.05570/) (2021)
- [SetMargin Loss applied to Deep Keystroke Biometrics with Circle Packing Interpretation](https://mlsanity.com/paper/2109.00938/) (2021)
- [BeCAPTCHA-Type: Biometric Keystroke Data Generation for Improved Bot Detection](https://mlsanity.com/paper/2207.13394/) (2022)

## Abstract (arXiv)

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.