计算机科学
认证(法律)
密码
架空(工程)
背景(考古学)
工作流程
编码器
情态动词
人工智能
机器学习
特征(语言学)
代表(政治)
人机交互
计算机安全
数据库
操作系统
政治学
哲学
政治
古生物学
生物
化学
高分子化学
法学
语言学
作者
Muyan Yao,Zuodong Jin,Ruipeng Gao,Peng Qi,Dan Tao
出处
期刊:Transactions on Emerging Telecommunications Technologies
日期:2023-12-01
摘要
Abstract Widely accepted explicit authentication protocols are vulnerable to a series of attacks, for example, shoulder surfing and smudge attacks, leaving users with the constant burden of periodic password changes. As such, we propose a novel framework for continuous authentication on smartphones. This approach is guided by pattern unlocking, which is widely used and will not cause learning cost. After collecting multi‐modal data that describe both behavioral and contextual information, we employ a multi‐branch context‐aware attention network as the representation learner to perform feature extraction, then an auto encoder is then used for authentication. To overcome challenges, including cold‐start and few‐shot training, which is less discussed in other works, we incorporate transfer learning with a coarse‐to‐fine pre‐training workflow. Additionally, we deploy a hierarchical approach to offload model tuning overhead from smartphones. Extensive experiments on more than 68 000 real‐world recordings validate the effectiveness of the proposed method, with an EER (equal error rate) of 2.472% under mixed contexts, which consistently outperforms state‐of‐the‐art approaches under both static and mixed contexts.
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