# Continuous Authentication Using One-class Classifiers and their Fusion

arXiv: https://arxiv.org/abs/1710.11075 · HTML: https://mlsanity.com/paper/1710.11075/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2017.

## Built on by (most cited first, 6 of 6)

- [Defending Touch-based Continuous Authentication Systems from Active Adversaries Using Generative Adversarial Networks](https://mlsanity.com/paper/2106.07867/) (2021)
- [LLM-Assisted Cheating Detection in Korean Language via Keystrokes](https://mlsanity.com/paper/2507.22956/) (2025)
- [iCTGAN--An Attack Mitigation Technique for Random-vector Attack on Accelerometer-based Gait Authentication Systems](https://mlsanity.com/paper/2210.00615/) (2022)
- [GANTouch: An Attack-Resilient Framework for Touch-based Continuous Authentication System](https://mlsanity.com/paper/2210.01594/) (2022)
- [Smartphone Impostor Detection with Behavioral Data Privacy and Minimalist Hardware Support](https://mlsanity.com/paper/2103.06453/) (2021)
- [Dictionary Attack on IMU-based Gait Authentication](https://mlsanity.com/paper/2309.11766/) (2023)

## Abstract (arXiv)

While developing continuous authentication systems (CAS), we generally assume that samples from both genuine and impostor classes are readily available. However, the assumption may not be true in certain circumstances. Therefore, we explore the possibility of implementing CAS using only genuine samples. Specifically, we investigate the usefulness of four one-class classifiers OCC (elliptic envelope, isolation forest, local outliers factor, and one-class support vector machines) and their fusion. The performance of these classifiers was evaluated on four distinct behavioral biometric datasets, and compared with eight multi-class classifiers (MCC). The results demonstrate that if we have sufficient training data from the genuine user the OCC, and their fusion can closely match the performance of the majority of MCC. Our findings encourage the research community to use OCC in order to build CAS as they do not require knowledge of impostor class during the enrollment process.