基于堆叠稀疏收缩变分自编码器和不平衡XGBoost的网络异常检测

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Jing Bi;Ziyue Guan;Haitao Yuan;Jinhong Yang;Jia Zhang
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引用次数: 0

摘要

有效、准确地识别网络异常对网络安全系统具有重要意义。在日益增长的网络数据中,如何准确地检测异常行为是一个非常具有挑战性的问题。目前,基于自编码器特征提取的分类方法已被证明适用于网络异常检测。然而,传统的带有自编码器的检测模型在面对海量网络特征时,检测精度并不理想。此外,它们的模型的超参数优化问题也不能得到有效解决。本文在改进变分自编码器的基础上,设计了堆叠稀疏收缩变分自编码器(S3VAEs)。此外,提出了一种基于遗传模拟退火粒子群优化(UXG)的不平衡XGBoost分类器。最后,将S3VAEs特征提取器与UXG分类器相结合,得到异常检测模型。基于4个真实数据集的实验结果表明,该异常检测模型比现有的几种算法具有更高的分类精度和F1。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Network Anomaly Detection With Stacked Sparse Shrink Variational Autoencoders and Unbalanced XGBoost
Efficient and accurate identification of network anomalies is significant to network security systems. It is highly challenging to detect abnormal behaviors in the increasing network data accurately. Currently, classification methods based on feature extraction of autoencoders have been proven to be suitable for network anomaly detection. However, traditional detection models with autoencoders have unsatisfying detection accuracy in the face of massive network features. In addition, the hyperparameter optimization of their models cannot be effectively solved. In this letter, based on the improvement of variational autoencoders, stacked sparse shrink variational autoencoders (S3VAEs) are designed. In addition, an Unbalanced XGBoost classifier based on Genetic simulated annealing particle swarm optimization (UXG) is proposed. Finally, the feature extractor of S3VAEs is combined with the UXG classifier, and the anomaly detection model is obtained. Experimental results based on four real-life data sets demonstrate that the proposed anomaly detection model achieves higher classification accuracy and F1 than several state-of-the-art algorithms.
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来源期刊
IEEE Transactions on Sustainable Computing
IEEE Transactions on Sustainable Computing Mathematics-Control and Optimization
CiteScore
7.70
自引率
2.60%
发文量
54
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