A novel fusion feature imageization with improved extreme learning machine for network anomaly detection

As the complexity and quantity of network data continue to increase, accurate and efficient anomaly detection methods become critical. Deep learning-based methods are suitable for real-time detection because they leverage neural networks to efficiently process massive amounts of data. However, for complex network environments and unknown threats, it is difficult to acquire balanced datasets for training, resulting in low model accuracy. Moreover, in a large-scale network environment, the model training process is complicated and resource-consuming, ignoring the important information hidden behind the data and poor scalability. To address these issues, we develop a novel network anomaly detection method that integrates fused feature imageization with an enhanced extreme learning machine, termed GMD-DELM. The network data stream features are transformed into images by an adaptive transformation method, generating a feature representation with highly enhanced data recognition capability. In addition, a ResNeXt network embedded with an attention mechanism is used to extract high-level features from images, enhancing the ability of deep learning networks to extract important features from network streams. Finally, we implement a network anomaly detection method established on an improved adaptive differential evolution kernel extreme learning machine. The experimental results demonstrate that the proposed model achieves notable enhancements achieved by the proposed model in reducing feature redundancy and improving accuracy compared to existing network anomaly detection models. Furthermore, our model exhibits improved stability and robustness in detecting corrupted network data containing noise.