基于One-Hot编码和改进的ResNet18的DDoS攻击检测方法

2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2023-06-16 DOI:10.1109/AINIT59027.2023.10210725

Hanlin Lu, Beining Ying, Xujun Che, Zhaoning Jin, Mingxuan Wang, Shuhui Wu

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引用次数: 0

摘要

DDoS攻击具有易实现、隐蔽性强、破坏性强等特点。它已经严重威胁到网络安全。本文选择CIC-DDoS2019作为数据集，通过数据预处理去除数据集中无效的冗余特征和异常数据，利用One-Hot编码重构预处理后的网络流量数据，最后选择改进的ResNet18作为分类器检测DDoS攻击。实验结果表明，该方法可以有效地将网络流量数据转换为二值图像，将ResNet18的检测准确率提高到98%。

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DDoS attack detection method based on One-Hot coding and improved ResNet18

DDoS attack is easy to implement, concealed, and destructive. It has been a serious threat to network security. This paper chooses CIC-DDoS2019 as the dataset, removes the invalid redundant features and abnormal data from the dataset through data preprocessing, reconstructs the preprocessed network traffic data using One-Hot encoding, and finally selects the improved ResNet18 as the classifier to detect DDoS attacks. The experimental results show that the method can convert the network traffic data into binary images efficiently and improve the detection accuracy of ResNet18 to 98%.

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2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)

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