| 摘 要: 随着移动互联网的高速发展，智能手机已经成为人们社交、支付、出行、娱乐等活动中不可或缺的工具。而一旦手机被盗用，用户的各类应用账户都可能被无限制地访问，继而导致身份冒用、隐私泄露、财产损失等严重后果。本文提出一种在移动端采集用户行为特征，并通过神经网络建模的识别方法，通过收集手机的倾斜角度、移动速度、加速度，以及用户点击、滑动屏幕的力度、速度、触点形状、面积等数据，验证设备是否被他人盗用。这种身份验证技术具有难以窃取和伪造、验证流程用户无感知、隐私性好等优点。 |
| 关键词: 行为生物识别；身份验证；移动互联网；卷积神经网络 |
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中图分类号: TP311.52
文献标识码: A
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| Research on Recognition of Mobile User Behavior Features |
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YANG Fan
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(China Unionpay Co ., Ltd ., Shanghai 201201, China )
yangfan@unionpay.com
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| Abstract: With the rapid development of mobile Internet, smart phones have become an indispensable device in social activities, payments, travelling, entertainment and other activities. Once the mobile phone was stolen, the user's various application accounts might be accessed without restrictions, leading to serious consequences such as identity fraud, privacy leakage, and property loss. This paper proposes a recognition method that collects user behavior features on mobile terminals and uses neural network modeling. Data, such as tilt angle, movement speed, acceleration speed, as well as force, speed, shape and area of touchpoints, are collected to verify whether the device is stolen by others. This kind of identity verification technology has the advantages of being stealing and forgery resistant, undisturbed and privacy-friendly. |
| Keywords: behavioral biometrics; identity verification; mobile Internet; convolutional neural network |
