论文标题
根据已知指标，图形图形的风险预测网站关系：确定对用户的派生威胁
Graphing Website Relationships for Risk Prediction: Identifying Derived Threats to Users Based on Known Indicators
论文作者
论文摘要
该研究的假设是，基于推荐人链接的关系以及恶意网站的啤酒花数量可能表明对另一个网站的风险。我们选择接收器操作特性（ROC）分析作为比较捕获的网络流量的真实正面和假阳性率以测试我们模型的预测能力的方法。已知的威胁指标被用作指定器，并利用NEO4J图数据库来绘制基于参考链接的其他网站之间的关系。使用引用流量，我们绘制了具有已知关系的网站访问的用户访问，以跟踪用户从非恶意网站发展到已知威胁的速度。结果是通过已知威胁的跃距离分组的，以计算预测率。该模型的结果分别在7.42％至37.50％之间产生了58.59％和63.45％和假正率之间的真实正率。 True and froms的正率表明，基于已知威胁的近距离接近性的绩效提高，而与威胁的参考距离增加导致误报率更高。
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.
