论文标题
使用分配数据的网络安全建模
Network Security Modelling with Distributional Data
论文作者
论文摘要
我们使用机器学习方法研究了僵尸网络命令和控制（C2）主机的检测。为此，我们使用NetFlow数据（用于监视IP流量的行业标准）以及使用两组功能的ML模型：基于NetFlow变量的常规NetFlow变量和分销功能。除了使用NetFlow功能的静态摘要外，我们还将其IP级分布的分位数作为预测模型中的输入特征来预测IP是否属于已知的僵尸网络族。这些模型用于开发入侵检测系统，以预测因恶意攻击而识别的交通轨迹。通过将预测与已发表的恶意IP地址和深度数据包检查的现有派遣者进行匹配来验证结果。我们提出的新型分销特征的使用以及使建模复杂输入特征空间的技术相结合，从而通过训练有素的模型进行了高度准确的预测。
We investigate the detection of botnet command and control (C2) hosts in massive IP traffic using machine learning methods. To this end, we use NetFlow data -- the industry standard for monitoring of IP traffic -- and ML models using two sets of features: conventional NetFlow variables and distributional features based on NetFlow variables. In addition to using static summaries of NetFlow features, we use quantiles of their IP-level distributions as input features in predictive models to predict whether an IP belongs to known botnet families. These models are used to develop intrusion detection systems to predict traffic traces identified with malicious attacks. The results are validated by matching predictions to existing denylists of published malicious IP addresses and deep packet inspection. The usage of our proposed novel distributional features, combined with techniques that enable modelling complex input feature spaces result in highly accurate predictions by our trained models.
