信息工程大学学报2024，Vol.25Issue(2)：213-218,6.DOI:10.3969/j.issn.1671-0673.2024.02.013
深度网络异常检测模型的泛化性能研究
Generalization Ability of Network Anomaly Detection Models Based on Deep Learning
摘要
Abstract
In recent years,network anomaly detection model based on deep learning has become a research hotspot in the area,getting outstanding achievements in experimental environments.Howev-er,there is a lack of research related with the generalization ability of those models.The paper con-structed three representative network anomaly detection models based on multi-layer perceptron,1-D convolutional neural network and deep auto-encoder,and trained on CICIDS2017 and CICIDS2018.Then,the evaluation experiments are carried out in a cross way to quantify its generalization ability.The experimental results show that the accuracy of the models has declined by 20.78％,23.18％and 11.13％on average,which proves that the generalization performance of the deep network anomaly detection model is a serious problem,and reveals the pitfall of applying deep learning technology to network security and the key obstacle to its practical deployment.Finally,the summary and analysis of this problem is discussed and the potential solutions are put forward.关键词
网络安全/网络异常检测/深度学习/泛化性能Key words
network security/network anomaly detection/deep learning/generalization ability分类
信息技术与安全科学引用本文复制引用
曲彦泽,马海龙,江逸茗..深度网络异常检测模型的泛化性能研究[J].信息工程大学学报,2024,25(2):213-218,6.
