The complexity and variability of Internet traffic data present significant challenges in feature extraction and selection, often resulting in ineffective abnormal traffic monitoring. To address these challenges, an improved Bidirectional Long Short-Term Memory (BiLSTM) network-based approach for Internet abnormal traffic monitoring was proposed. In this method, a constrained minimum collection node coverage strategy was first applied to optimize the selection of collection nodes, ensuring comprehensive data coverage across network nodes while minimizing resource consumption. The collected traffic dataset was then transformed to enhance data validity. To enable more robust feature extraction, a combined Convolutional Neural Network (CNN) and BiLSTM model was employed, allowing for a comprehensive analysis of data characteristics. Additionally, an attention mechanism was incorporated to weigh the significance of attribute features, further enhancing classification accuracy. The final traffic monitoring results were produced through a softmax classifier, demonstrating that the proposed method yields a high monitoring accuracy with a low false positive rate of 0.2, an Area Under the Curve (AUC) of 0.95, and an average monitoring latency of 5.7 milliseconds (ms). These results indicate that the method provides an efficient and rapid response to Internet traffic anomalies, with a marked improvement in monitoring performance and resource efficiency.
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