Continuous authentication using one-class classifiers and their fusion
Continuous authentication using one-class classifiers and their fusion
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DOI：
10.1109/isba.2018.8311467
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
R. Kumar;P. P. Kundu-P.;V. Phoha
中科院分区：
文献类型：
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作者：
R. Kumar;P. P. Kundu-P.;V. Phoha
在开发连续认证系统（CAS）时，我们通常假设真实和冒名顶替类的样本都是现成的。然而，在某些情况下，这种假设可能不成立。因此，我们探讨了仅使用真实样本实施CAS的可能性。具体来说，我们研究了四个一类分类器OCC（椭圆包络，隔离森林，局部离群值因子，一类支持向量机）和它们的融合的有用性。这些分类器的性能进行了评估四个不同的行为生物特征数据集，并与8个多类分类器（MCC）。结果表明，如果我们有足够的训练数据，从真正的用户OCC，和他们的融合可以密切匹配的性能大多数MCC。我们的研究结果鼓励研究界使用OCC来构建CAS，因为它不需要在注册过程中了解冒名顶替者类。
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 it does not require knowledge of impostor class during the enrollment process.