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arXiv 2003.00971cs.CRcs.DBcs.NI
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风险预测的网站关系图：基于已知指标识别对用户的衍生威胁

Graphing Website Relationships for Risk Prediction: Identifying Derived Threats to Users Based on Known Indicators

Philip H. Kulp, Nikki E. Robinson

备注：
10 pages, 3 figures, 3 tables
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摘要：

该研究的假设是，基于恶意链接和恶意网站跳数的关系可能表明另一个网站的风险。我们选择接受者操作特征（ROC）分析作为比较捕获的Web流量的真阳性和假阳性率的方法，以测试我们模型的预测能力。已知的威胁指标被用作指示器，Neo4j图形数据库被用来根据引用链接映射其他网站之间的关系。使用引用流量，我们映射了具有已知关系的网站之间的用户访问，以跟踪用户从非恶意网站发展到已知威胁的速度。结果按照与已知威胁的跳距进行分组，以计算预测率。模型的结果产生的真阳性率在58.59%和63.45%之间，假阳性率在7.42%和37.50%之间。真阳性率和假阳性率表明，基于与已知威胁的更近距离的性能得到了改善，而与威胁的参考距离增加导致了更高的假阳性率。

英文摘要：

The hypothesis for the study was that the relationship based on referrer links and the number of hops to a malicious site could indicate the risk to another website. We chose Receiver Operating Characteristics (ROC) analysis as the method of comparing true positive and false positive rates for captured web traffic to test the predictive capabilities of our model. Known threat indicators were used as designators, and the Neo4j graph database was leveraged to map the relationships between other websites based on referring links. Using the referring traffic, we mapped user visits across websites with a known relationship to track the rate at which users progressed from a non-malicious website to a known threat. The results were grouped by the hop distance from the known threat to calculate the predictive rate. The results of the model produced true positive rates between 58.59% and 63.45% and false positive rates between 7.42% to 37.50%, respectively. The true and false positive rates suggest an improved performance based on the closer proximity from the known threat, while an increased referring distance from the threat resulted in higher rates of false positives.

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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