A Framework for Behavioral Biometric Authentication Using Deep Metric Learning on Mobile Devices
A Framework for Behavioral Biometric Authentication Using Deep Metric Learning on Mobile Devices
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DOI：
10.1109/tmc.2021.3072608
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发表时间：
2020-05
影响因子：
7.9
通讯作者：
Cong Wang;Y. Xiao;Xing Gao;Li Li-Li;Jun Wang
中科院分区：
文献类型：
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作者：
Cong Wang;Y. Xiao;Xing Gao;Li Li-Li;Jun Wang
使用行为生物特征的移动的身份验证已经成为一个活跃的研究领域。现有的研究依赖于构建机器学习分类器来识别个人的独特模式。然而，这些分类器没有足够的能力来学习判别特征。当在移动的设备上实现时，它们面临着来自行为动态、数据隐私和侧信道泄漏的新挑战。为了解决这些挑战，我们提出了一个新的框架，将培训电池供电的移动的设备，所以私人数据永远不会离开设备和培训可以灵活地安排，以适应在运行时的行为模式。我们将分类问题重新表述为深度度量学习，以提高区分能力，并设计一种有效的对策，通过在感知信号中嵌入噪声特征来阻止侧信道泄漏，而不会牺牲太多的可用性。实验表明，在三个公共数据集上的认证准确率超过95%，从数据较少的多类分类中获得了15%的收益，并且对暴力攻击和侧信道攻击的鲁棒性分别为99%和90%。我们展示了使用移动的CPU进行训练的可行性，其中训练100个epoch需要不到10分钟，并且可以通过特征转移提高3-5倍。最后，我们分析内存，能源和计算开销。我们的研究结果表明，训练消耗的能量低于观看视频，略高于玩游戏。
Mobile authentication using behavioral biometrics has been an active area of research. Existing research relies on building machine learning classifiers to recognize an individual’s unique patterns. However, these classifiers are not powerful enough to learn the discriminative features. When implemented on the mobile devices, they face new challenges from the behavioral dynamics, data privacy and side-channel leaks. To address these challenges, we present a new framework to incorporate training on battery-powered mobile devices, so private data never leaves the device and training can be flexibly scheduled to adapt the behavioral patterns at runtime. We re-formulate the classification problem into deep metric learning to improve the discriminative power and design an effective countermeasure to thwart side-channel leaks by embedding a noise signature in the sensing signals without sacrificing too much usability. The experiments demonstrate authentication accuracy over 95 percent on three public datasets, a sheer 15 percent gain from multi-class classification with less data and robustness against brute-force and side-channel attacks with 99 and 90 percent success, respectively. We show the feasibility of training with mobile CPUs, where training 100 epochs takes less than 10 mins and can be boosted 3-5 times with feature transfer. Finally, we profile memory, energy and computational overhead. Our results indicate that training consumes lower energy than watching videos and slightly higher energy than playing games.