Benchmarking keystroke authentication algorithms
Benchmarking keystroke authentication algorithms
复制标题
击键认证算法的基准测试
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Adam Sherwin
中科院分区：
文献类型：
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作者：
Jiaju Huang;Daqing Hou;S. Schuckers;Timothy Law;Adam Sherwin
自由文本击键动力学是一种行为生物识别，具有强大的潜力，可以提供不引人注目的用户身份验证。这种行为生物识别技术很重要，因为它们可以用作其他一站式身份验证方法（例如用户ID和密码）的附加保护层。不幸的是，由于缺乏大型，共享的自由文本数据集，仍缺乏击键动力学算法的评估和比较。在这项研究中，我们基于内核密度估计（KDE）提出了一种新颖的击键动力学算法，并使用三个出版数据集，将其与其他两种最先进的算法进行对比，即Gunetti＆Picardi和Buffalo的SVM算法，即以及我们自己的新数据集，它比以前的数据集大。我们在必要时修改算法以使其具有可比的设置，包括配置文件和测试样本量。 Gunetti＆Picardi和我们自己的KDE算法的性能都比Buffalo的SVM算法要好得多。尽管简单得多，但新开发的KDE算法的性能与Gunetti＆Picardi的算法相似，在三个约束数据集上的算法，但在我们新的无约束数据集中最好的表现。与我们的新数据集相比，这三种算法在以一种或另一种方式受到约束的三个之前的数据集上的性能要好得多，这确实是不受限制的。这突出了我们不受约束的数据集在代表击键动力学的真实情况方面的重要性。最后，新的KDE算法在我们的新数据集中的性能降低。
Free-text keystroke dynamics is a behavioral biometric that has the strong potential to offer unobtrusive and continuous user authentication. Such behavioral biometrics are important as they may serve as an additional layer of protection over other one-stop authentication methods such as the user ID and passwords. Unfortunately, evaluation and comparison of keystroke dynamics algorithms are still lacking due to the absence of large, shared free-text datasets. In this research, we present a novel keystroke dynamics algorithm, based on kernel density estimation (KDE), and contrast it with two other state-of-the-art algorithms, namely Gunetti & Picardi's and Buffalo's SVM algorithms, using three published datasets, as well as our own new, unconstrained dataset that is an order of magnitude larger than the previous ones. We modify the algorithms when necessary such that they have comparable settings, including profile and test sample sizes. Both Gunetti & Picardi's and our own KDE algorithms have performed much better than Buffalo's SVM algorithm. Although much simpler, the newly developed KDE algorithm is shown to perform similarly as Gunetti & Picardi's algorithm on the three constrained datasets, but the best on our new unconstrained dataset. All three algorithms perform significantly better on the three prior datasets, which are constrained in one way or another, than our new dataset, which is truly unconstrained. This highlights the importance of our unconstrained dataset in representing the real-world scenarios for keystroke dynamics. Lastly, the new KDE algorithm degrades the least in performance on our new dataset.