关键词: 入侵检测；特征选择；类不平衡；时空特征提取；膨胀卷积

Abstract: Aiming at the problems of complex feature redundancy, class imbalance, and insufficient extraction of spatiotemporal features in network intrusion detection, a multi-scale spatiotemporal feature extraction intrusion detection system based on subspace distance is proposed. First, variance-covariance subspace distance is applied to feature selection on preprocessed data to obtain a representative feature subset that effectively approximates the original feature space. Secondly, an improved Focal Loss (FL) is utilized to dynamically adjust the loss contribution of different categories to address the class imbalance problem. Finally, we propose a traffic anomaly detection model based on spatiotemporal feature extraction named Gated Dilated Convolution and LSTM-Channel Attention (GDC-LA) that integrates gated dilated convolution, long short-term memory networks and channel attention. Experimental results on the UNSW-NB15 dataset demonstrate that the proposed model effectively alleviates sample imbalance and achieves high detection accuracy.

Key words: intrusion detection; feature selection; class imbalance; spatiotemporal feature extraction; dilated convolution