Continuous Authentication Using One-class Classifiers and their Fusion
This work addresses the challenge of continuous authentication for security applications where impostor data is unavailable, offering a practical solution but is incremental in nature.
The paper tackles the problem of building continuous authentication systems when only genuine user data is available, by exploring one-class classifiers and their fusion, and finds that with sufficient genuine training data, they can closely match the performance of most multi-class classifiers on four behavioral biometric datasets.
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.