基于双曲率黎曼流体的网络攻击检测模型

Dual Curvatured Riemannian Manifold-based Cyber Attack Detection Model

基于深度学习的网络攻击检测方法以其强大的特征表示及提取能力得到了迅速发展。然而,传统欧氏空间中的深度学习网络攻击检测模型无法有效捕获具有复杂调用关系的网络拓扑结构。为高效建模网络攻击图中潜在的数据模式,提高网络攻击检测的准确性,提出一种基于双曲率黎曼流体的网络攻击检测模型。与现有方法不同的是,该模型将传统欧氏空间中的网络攻击检测模型迁移到异质化非欧式表示空间中,利用曲率黎曼几何空间所具备的大规模层次性和环形图结构模式建模能力,获取高质量的攻击图表示向量,进而提高网络攻击检测准确性。实验结果表明,基于双曲率黎曼流体的网络攻击检测模型Precision、Recall、F1-score分别为0.963、0.964、0.964,能够对网络攻击行为进行有效分析与检测。

Deep learning-based cyber attack detection methods have been rapidly developed with their powerful feature representation and extraction capabilities.However,deep learning-based cyber attack detection models built in traditional Euclidean space cannot effectively capture the rich topology in the attack graphs.In order to efficiently model the potential data patterns in the cyber attack graph and thus improve the detection accuracy,a cyber attack detection model based on dual curvatured Riemannian manifold is proposed.this model migrates the cyber attack detection model in the traditional Euclidean space to a heterogeneous non-Euclidean representation space,and uses the large-scale hierarchical and circular graph structure pattern modeling capabilities possessed by the non-Euclidean curvatured Riemannian geometry space to obtain high-quality attack graph representation vectors,thereby improves the cyber attack detection accuracy.The experimental results show that Precision,Recall,F1-score of the proposed dual curvatured Riemannian manifold-based cyber attack detection model were 0.963,0.964,0.964,respectively,which can effectively analyze and detect attacks.