Composite Behavioral Modeling for Identity Theft Detection in Online Social Networks
Composite Behavioral Modeling for Identity Theft Detection in Online Social Networks
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DOI：
10.1109/tcss.2021.3092007
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发表时间：
2018-01
影响因子：
5
通讯作者：
Cheng Wang;Hang Zhu;Bo Yang
中科院分区：
文献类型：
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作者：
Cheng Wang;Hang Zhu;Bo Yang
在这项工作中，我们的目标是建立一个桥梁，从粗糙的行为数据，一个有效的，快速响应，和强大的行为模型，在线身份盗窃检测。我们专注于在线社交网络（OSN）中的这个问题，其中用户通常具有复合行为记录，由多维低质量数据组成，例如，离线签到和在线用户生成内容（UGC）。作为一个有见地的结果，我们验证了有一个互补的效果之间的不同维度的记录建模用户的行为模式。为了深入利用这种互补效应，我们提出了一个联合（而不是融合）模型来捕获用户的复合行为的在线和离线特征。我们通过将所提出的联合模型与两个真实世界数据集上的典型模型及其融合模型进行比较来评估：Foursquare和Yelp。实验结果表明，我们的模型优于现有的，在Foursquare和Yelp的接收器工作特征曲线（AUC）值下的面积分别为0.956和0.947。特别是，召回率（真阳性率）可以达到65.3%，在Foursquare和Yelp的72.2%，相应的干扰率（假阳性率）低于1%。值得一提的是，这些性能可以通过只检查一个复合行为来实现，这保证了我们方法的低响应延迟。这项研究将为网络安全界提供新的见解，了解是否以及如何通过对用户的复合行为模式进行建模来改进实时在线身份认证。
In this work, we aim at building a bridge from coarse behavioral data to an effective, quick-response, and robust behavioral model for online identity theft detection. We concentrate on this issue in online social networks (OSNs) where users usually have composite behavioral records, consisting of multidimensional low-quality data, e.g., offline check-ins and online user-generated content (UGC). As an insightful result, we validate that there is a complementary effect among different dimensions of records for modeling users’ behavioral patterns. To deeply exploit such a complementary effect, we propose a joint (instead of fused) model to capture both online and offline features of a user’s composite behavior. We evaluate the proposed joint model by comparing it with typical models and their fused model on two real-world datasets: Foursquare and Yelp. The experimental results show that our model outperforms the existing ones, with the area under the receiver operating characteristic curve (AUC) values 0.956 in Foursquare and 0.947 in Yelp, respectively. Particularly, the recall (true positive rate) can reach up to 65.3% in Foursquare and 72.2% in Yelp with the corresponding disturbance rate (false-positive rate) below 1%. It is worth mentioning that these performances can be achieved by examining only one composite behavior, which guarantees the low response latency of our method. This study would give the cybersecurity community new insights into whether and how real-time online identity authentication can be improved via modeling users’ composite behavioral patterns.