计算机科学 ›› 2018, Vol. 45 ›› Issue (2): 181-188.doi: 10.11896/j.issn.1002-137X.2018.02.032
陈冬祥,丁志军,闫春钢,王咪咪
CHEN Dong-xiang, DING Zhi-jun, YAN Chun-gang and WANG Mi-mi
摘要： 在电子交易中,用户通过PC端的浏览器进行交易。由于钓鱼网站等盗号方式的威胁,传统的账号密码认证方式存在着失效的风险。现有的用户网页浏览行为认证方法主要针对用户的某一方面行为进行认证。若对大量用户仅进行单方面行为的认证,则难以区分特征相似用户,会造成认证失效。基于用户浏览网页的序列行为、超链接使用行为和操作浏览器行为的多因素浏览行为特征,采用机器学习方法构建了一种认证方法。实验结果表明,在一定的误报率情况下,该方法的侦测率达到了90%以上。
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