Network Attack Dataset Construction Using Causal Graph
Advanced persistent threat attack has become the main form of network attack because of its multi-stage sustainable characteristics.Datasets are necessary for researches on the detection and prediction of this kind of attack.Real network and host data are superior when constructing datasets.However,few publicly available datasets can meet the requirements,due to the privacy and security issues.The available datasets often supply original network flows and system logs,but the absence of analysis on the long-term attack context results in that a straightforward using of deep neural networks to detect and predict malicious packets is not practical enough.In order to overcome these problems,a causal graph based network attack dataset is constructed and released,based on the real attack data of a network scene.Compared with the other datasets supplying original network flows and system logs simply,such dataset explores the causality of attach context deeply and can model the long-term advanced persistent threat attack.This makes the dataset more applicable for attack detection and prediction.The dataset is released at https://github.com/GuangmingZhu/CausalGraphAPTDataset.
万方数据
维普
