# Quickest Intruder Detection for Multiple User Active Authentication

arXiv: https://arxiv.org/abs/2006.11921 · HTML: https://mlsanity.com/paper/2006.11921/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [Blockchain and Biometrics: Survey, GDPR Analysis, and Future Directions](https://mlsanity.com/paper/2302.10883/) (2023)
- [SELM: Siamese Extreme Learning Machine with Application to Face Biometrics](https://mlsanity.com/paper/2108.03140/) (2021)

## Abstract (arXiv)

In this paper, we investigate how to detect intruders with low latency for Active Authentication (AA) systems with multiple-users. We extend the Quickest Change Detection (QCD) framework to the multiple-user case and formulate the Multiple-user Quickest Intruder Detection (MQID) algorithm. Furthermore, we extend the algorithm to the data-efficient scenario where intruder detection is carried out with fewer observation samples. We evaluate the effectiveness of the proposed method on two publicly available AA datasets on the face modality.