计算机科学 ›› 2020, Vol. 47 ›› Issue (7): 257-262.doi: 10.11896/jsjkx.190900107
所属专题： 信息安全 虚拟专题
王萌, 丁志军
WANG Meng, DING Zhi-jun
摘要： 近年来,随着移动互联网的快速发展,越来越多的业务从浏览器端转移到了移动端。但是,寄生在移动互联网上的黑色产业链也达到了泛滥的地步。设备指纹技术应运而生,即利用设备的特征属性为每个设备生成独一无二的标识。其间涌现了很多利用机器学习方法进行设备唯一性认证的策略,其中大部分方法注重于模型的建立,很少对特征选择部分展开深入研究,而特征选择直接关系到最终模型的性能。针对该问题,文中提出了一种新的设备指纹特征选择及模型构建方法(Feature Selection Based on Discrimination and Stability and Weight-based Similarity Calculation,FSDS-WSC),即根据不同设备的特征区分度和相同设备的特征稳定性选出最具价值的一些特征,并将这些特征的重要程度作为特征权重应用到模型建立的后续过程中。在真实场景中的6424台Android设备上,将FSDS-WSC与当今主流的其他特征选择方法进行了对比实验。结果表明,FSDS-WSC相比其他方法有了较大改进,设备唯一性认证的准确率达到了99.53%,证实了FSDS-WSC的优越性。
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