A Network Anomaly Traffic Detection Method Based on <scp>CNN</scp> ‐ <scp>LSTM</scp>

ABSTRACT As information technology advances swiftly, the internet has become an indispensable component of modern life. The proliferation of various applications and services has led to a surge in network traffic, posing unprecedented challenges for network security. Traditional machine learning algorithms have faced challenges in accurately detecting abnormal network traffic, prompting the adoption of deep learning technologies. These technologies are recognized as pivotal tools for network traffic anomaly detection due to their robust feature extraction capabilities and their ability to handle complex patterns. However, existing deep learning methods often grapple with high false positive rates and limited generalization ability. To address these issues, this research proposes a combined CNN‐LSTM framework, with the CNN component dedicated to extracting spatial features and the LSTM component responsible for detecting temporal sequences. This hybrid approach leverages the CNN's ability to capture spatial correlations in data and the LSTM's strength in capturing long‐term temporal dependencies, effectively enhancing the accuracy and reliability of network traffic anomaly detection. Experimental results demonstrate that, when applied to the dataset of UNSW‐NB15, the CNN‐LSTM model achieves a binary classification detection accuracy of 96.67%, representing a 4.14% enhancement over the CNN model and a 2.16% improvement compared to the LSTM model. The multi‐class classification detection accuracy is 96.20%, showing improvements of 0.38% and 4.4% over CNN and LSTM, respectively. These findings underscore the superiority of the proposed method in detecting network anomaly traffic, with enhanced accuracy observed on the dataset of UNSW‐NB15. The model of this research is particularly promising for real‐world applications such as intrusion detection systems and network monitoring, and future work could explore its scalability and the integration of additional features.