Cyber Threat Indicators Association Prediction Based on Weighted Fusion of Semantic and Topological Information

Yansong Wang, Bo Lang, Nan Xiao, Yikai Chen
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Abstract

Nowadays, Cyber Threat Intelligence (CTI) has become increasingly important for detecting and defending against cyber threats. Researchers often construct CTI heterogeneous graphs to describe threat indicators and their associations. However, most existing link prediction methods of normal heterogeneous graphs show poor performance on CTI graphs, as they mainly focus on the topological features and ignore the attributes of the threat indicators. To address this limitation, this paper proposes Ctiap, a Cyber Threat Indicators Association Prediction model based on weighted fusion of the semantic and topological information. The model firstly aims at the semantic characteristics of threat indicators and the topology of CTI graph. We collected more than 20,000 samples through open web platforms to construct a real-word heterogeneous graph dataset of threat indicators. The experimental results show that the accuracy of our model reaches 93.08%, which is better than the state-of-the-art baseline methods.
基于语义和拓扑信息加权融合的网络威胁指标关联预测
如今,网络威胁情报(CTI)在检测和防御网络威胁方面发挥着越来越重要的作用。研究人员经常构建CTI异构图来描述威胁指标及其关联。但是,现有的常规异构图的链路预测方法主要关注拓扑特征,忽略了威胁指标的属性,在CTI图上的预测性能较差。为了解决这一问题,本文提出了一种基于语义和拓扑信息加权融合的网络威胁指标关联预测模型Ctiap。该模型首先针对威胁指标的语义特征和CTI图的拓扑结构。我们通过开放的web平台采集了2万多个样本,构建了一个真实世界的威胁指标异构图数据集。实验结果表明,该模型的准确率达到93.08%,优于现有的基线方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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