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引用次数: 7
摘要
监视网络或系统恶意活动的设备或软件设备称为入侵检测系统(IDS)。传统的IDS不能检测复杂的网络攻击,比如低频率的DoS攻击和未知的攻击。近年来,机器学习已经吸引了越来越多的兴趣来克服这些限制。本文提出了一种新的方法,通过在GRU层中嵌入具有pca - standardization和PCA-MinMax两种选项的PCA-Scale来提高门控循环单元(GRU)的入侵检测精度。这两种可选方法都通过影响正协方差的最大方差方向来显式地强化学习到的对象特征映射。该方法可以应用于GRU模型,而额外的计算成本可以忽略不计。在KDD Cup 99和NSL-KDD两个真实数据集上的实验结果表明,用pca - scale方法训练的GRU模型取得了显著的性能改进。
The Impact of PCA-Scale Improving GRU Performance for Intrusion Detection
A device or software appliance monitors a network or systems for malicious activity is an Intrusion Detection System (IDS). Conventional IDS does not detect elaborate cyber-attacks such as a low-rate DoS attack as well as unknown attacks. Machine Learning has attracted more and more interests in recent years to overcome these limitations. In this paper, we propose a novel method to improve intrusion detection accuracy of Gated Recurrent Unit (GRU) by embedding the proposed PCA-Scale with two options including PCA-Standardized and PCA-MinMax into the layer of GRU. Both optional methods explicitly enforce the learned object feature maps by affecting the direction of maximum variance with positive covariance. This approach can be applied to GRU model with negligible additional computation cost. We present experimental results on two real-world datasets such as KDD Cup 99 and NSL-KDD demonstrate that GRU model trained with PCA-Scaled method achieves remarkable performance improvements.