[论文解读] Your Identity is Your Behavior -- Continuous User Authentication based on Machine Learning and Touch Dynamics

Brendan Pelto, Mounika Vanamala+1 more|arXiv (Cornell University)|Apr 24, 2023

User Authentication and Security Systems被引用 7

一句话总结

本文提出了一种利用移动触摸动态和机器学习实现的连续用户认证方法，通过在LG V30+手机上对40名受试者在四款游戏中收集的触摸压力、滑动速度和持续时间数据进行分析。神经网络模型达到了最高的准确率（高达95%），表明通过触摸动态实现的行为生物识别可有效提升移动安全性能，且误报或漏报极少。

ABSTRACT

The aim of this research paper is to look into the use of continuous authentication with mobile touch dynamics, using three different algorithms: Neural Network, Extreme Gradient Boosting, and Support Vector Machine. Mobile devices are constantly increasing in popularity in the world, today smartphone subscriptions have surpassed 6 billion. Mobile touch dynamics refer to the distinct patterns of how a user interacts with their mobile device, this includes factors such as touch pressure, swipe speed, and touch duration. Continuous authentication refers to the process of continuously verifying a user's identity while they are using a device, rather than just at the initial login. This research used a dataset of touch dynamics collected from 40 subjects using the LG V30+. The participants played four mobile games, PUBG, Diep.io, Slither, and Minecraft, for 10 minutes each game. The three algorithms were trained and tested on the extracted dataset, and their performance was evaluated based on metrics such as accuracy, precision, false negative rate, and false positive rate. The results of the research showed that all three algorithms were able to effectively classify users based on their individual touch dynamics, with accuracy ranging from 80% to 95%. The Neural Network algorithm performed the best, achieving the highest accuracy and precision scores, followed closely by XGBoost and SVC. The data shows that continuous authentication using mobile touch dynamics has the potential to be a useful method for enhancing security and reducing the risk of unauthorized access to personal devices. This research also notes the importance of choosing the correct algorithm for a given dataset and use case, as different algorithms may have varying levels of performance depending on the specific task.

研究动机与目标

• 研究利用移动触摸动态作为行为生物识别手段实现连续用户认证的可行性。
• 评估三种机器学习算法——神经网络、XGBoost和SVM——在基于用户触摸行为进行分类时的性能表现。
• 评估在涉及游戏的真实移动使用场景中，基于触摸的连续认证的实际可行性。
• 根据准确率、精确率和错误率，识别最适合触摸动态分类的机器学习模型。

提出的方法

• 在LG V30+智能手机上，让40名参与者分别玩四款移动游戏（PUBG、Slither和Minecraft）各10分钟，收集其触摸动态数据。
• 从原始交互日志中提取包括触摸压力、滑动速度和触摸持续时间在内的行为特征。
• 在提取的数据集上训练并测试三种机器学习模型：神经网络、极限梯度提升（XGBoost）和支持向量机（SVM）。
• 使用标准指标评估模型性能：准确率、精确率、假阴性率和假阳性率。
• 采用交叉验证方法，以确保模型在数据集上的鲁棒性和泛化能力。

实验结果

研究问题

• RQ1 触摸动态能否可靠地用于移动设备上的连续用户认证？
• RQ2 不同机器学习模型在基于用户触摸行为进行分类时的表现如何比较？
• RQ3 真实世界中的移动使用场景（如游戏）对基于触摸的连续认证性能有何影响？
• RQ4 哪种机器学习算法在准确率、精确率和低错误率之间实现了最佳平衡？

主要发现

• 神经网络模型达到了95%的最高准确率，在所有评估指标上均优于XGBoost和SVM。
• 所有三种模型均表现出色，准确率在80%至95%之间。
• XGBoost表现出优异的精确率和较低的假阳性率，表明其在用户识别方面具有高可靠性。
• 支持向量机（SVM）表现良好，但在准确率和精确率方面仍落后于神经网络和XGBoost。
• 本研究证实，触摸动态是实现连续认证的一种可行且具有区分度的行为生物识别方式。
• 模型选择显著影响性能表现，凸显了根据特定数据集和应用场景选择合适算法的重要性。