Network Traffic Anomaly Detection Method Based on CTA-BiLSTM

Network traffic represents the volume of data sent and received during online website visits. Anomalies in network traffic indicate unusual variations in traffic, which are crucial to detecting in a timely and accurate manner in complex computer network systems to ensure efficient operation. Existing methods for anomaly detection in network traffic have rarely focused on effectively handling time-series data in the temporal dimension. Addressing the limitations of traditional Temporal Convolutional Networks in capturing local and significant features of time series, this paper proposes the CTA-BiLSTM model. Convolutional Neural Networks extract local features from sequences, while Temporal Convolutional Networks learn abstract and high-level sequence patterns to capture long-term dependencies and hierarchical features. Bidirectional Long Short-Term Memory Networks capture long-term dependencies from both directions, incorporating attention mechanisms in both Temporal Convolutional Networks and Bidirectional Long Short-Term Memory Networks modules. The CTA-BiLSTM model performs bidirectional temporal modeling of sequence features, capturing longterm dependencies and temporal patterns, thereby enhancing the capability to detect anomalous behaviors in network traffic. The proposed CTA-BiLSTM model is evaluated through experiments on a public network traffic dataset. The experimental results demonstrate that the CTA-BiLSTM model outperforms existing state-of-the-art techniques in terms of accuracy, precision, and F1-score.