Network intrusion detection is a crucial approach in the field of cybersecurity, with anomaly traffic detection being a key technique in intrusion detection. To address the issues of high false alarm rates and low efficiency in traditional detection models, this paper proposes an anomaly traffic detection model based on MA-GRUCNN. XGBoost is used for feature dimensionality reduction, and the reduced-dimensional data is then fed into the MA-GRUCNN model. CNN is employed to extract high-dimensional features from the traffic data, an attention mechanism is used to capture global dependencies, and GRU is utilized to capture long-term dependencies in the time series. Experimental results on the NSL-KDD dataset demonstrate that the proposed method outperforms other approaches in terms of detection accuracy, precision, recall, and F1 score, achieving a detection accuracy up to 97.45%.
Key words
CNN /
GRU /
feature extraction /
network traffic /
anomaly detection
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