Deep Feature Fusion via Transfer Learning for Multi-Class Network Intrusion Detection

Abstract

With the rapid advancement of network technologies, cyberthreats have become increasingly sophisticated, posing significant challenges to traditional intrusion detection systems. Conventional machine learning and deep learning approaches frequently experience performance degradation when confronted with imbalanced datasets and novel attack vectors. To address these limitations, this study proposes a deep learning-based intrusion detection framework that employs feature fusion through incremental transfer learning between source and target domains. The proposed architecture integrates convolutional neural networks (CNNs) with an attention mechanism to extract and aggregate salient features, thereby enhancing the model’s discriminative capacity between normal traffic and various network attack categories. Experimental results demonstrate that the proposed model achieves a detection accuracy of 94.21% even when trained on only 33% of the available data, outperforming conventional models. These findings underscore the effectiveness of the proposed feature fusion strategy via transfer learning in improving detection capabilities within dynamic and evolving cyberthreat environments.