Intelligent Detection of Abnormal Traffic Based on SCN-BiLSTM

To address the limitations of existing abnormal traffic detection methods, such as insufficient temporal and spatial feature extraction, high false positive rate (FPR), poor generalization, and class imbalance, this study proposed an intelligent detection method that combines a Stacked Convolutional Network (SCN), Bidirectional Long Short-Term Memory (BiLSTM) network, and Equalization Loss v2 (EQL v2). This method was divided into two components: a feature extraction model and a classification and detection model. First, SCN was constructed by combining a Convolutional Neural Network (CNN) with a Depthwise Separable Convolution (DSC) network to capture the abstract spatial features of traffic data. These features were then input into the BiLSTM to capture temporal dependencies. An attention mechanism was incorporated after SCN and BiLSTM to enhance the extraction of key spatiotemporal features. To address class imbalance, the classification detection model applied EQL v2 to adjust the weights of the minority classes, ensuring that they received equal focus during training. The experimental results indicated that the proposed method outperformed the existing methods in terms of accuracy, FPR, and F1-score and significantly improved the identification rate of minority classes.