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arXiv 2305.09482cs.CRcs.LG
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你的身份就是你的行为——基于机器学习和触摸动力学的连续用户认证

Your Identity is Your Behavior -- Continuous User Authentication based on Machine Learning and Touch Dynamics

Minnesota State University, Mankato(明尼苏达州立大学曼卡托分校)

Brendan Pelto, Mounika Vanamala, Rushit Dave

AI总结:

本研究基于移动触摸动力学,使用Neural Network、XGBoost和SVC三种算法进行连续用户认证,发现Neural Network表现最佳,准确率达80%至95%,有效提升了移动设备安全性。

摘要:

本文旨在研究使用三种不同算法:Neural Network、Extreme Gradient Boosting和Support Vector Machine,基于移动触摸动力学的连续认证应用。当今移动设备在全球日益普及,目前智能手机订阅量已超过60亿。移动触摸动力学是指用户与移动设备交互的独特模式,包括触摸压力、滑动速度和触摸持续时间等因素。连续认证是指在用户使用设备期间持续验证其身份的过程,而不仅仅是在初始登录时。本研究使用了从40名受试者收集的触摸动力学数据集,数据采集使用LG V30+设备。参与者每人玩了四款移动游戏:PUBG、Diep.io、Slither和Minecraft,每款游戏各10分钟。三种算法在提取的数据集上进行了训练和测试,其性能基于准确率、精确率、假阴性率和假阳性率等指标进行评估。研究结果表明,三种算法均能根据用户个人的触摸动力学有效对用户进行分类,准确率在80%到95%之间。Neural Network算法表现最佳,获得了最高的准确率和精确率得分,紧随其后的是XGBoost和SVC。数据表明,使用移动触摸动力学的连续认证具有潜力,可作为增强安全性并降低个人设备被未经授权访问风险的有效方法。本研究还指出,为给定数据集和用例选择正确算法的重要性,因为不同算法的性能水平可能会因具体任务而异。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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