CBAN:基于 CNN、BiGRU 和注意力机制的 DDoS 检测方法

Bing Wang, Yankun Yu, Chunlan Zhao, Jing Jiang
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引用次数: 0

摘要

针对物联网安全问题日益突出给大数据发展带来的障碍,本文对危害最大的DDoS攻击防御进行了深入研究。本文设计了一种用于 DDoS 检测的深度学习模型--CBAN。CBAN 模型在结构设计上集成了一维-CNN、BiGRU 和注意力机制等技术。该模型能有效提取网络流量数据的时空特征,从而高效检测潜在的 DDoS 攻击。CBAN 模型在 CIC-DDoS-2019 数据集上表现出色。
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CBAN: A DDoS detection method based on CNN, BiGRU, and attention mechanism
In order to address the obstacles posed by the growing security issues of the Internet of Things to the development of big data, this paper conducts in-depth research on the defense of the most harmful DDoS attack. This paper designs a deep learning model for DDoS detection - CBAN. The CBAN model integrates technologies such as 1D-CNN, BiGRU, and attention mechanism for structural design. This model can effectively extract spatial and temporal features of network traffic data for efficient detection of potential DDoS attacks. The CBAN model has shown excellent performance on the CIC-DDoS-2019 dataset.
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