Boosting Few-Shot Network Intrusion Detection with Adaptive Feature Fusion Mechanism

In network security, intrusion detection systems (IDSs) are essential for maintaining network integrity. Traditional IDSs primarily use supervised learning, relying on extensive datasets for effective training, which limits their ability to address rapidly evolving cyber threats, especially with limited data samples. To overcome this, prior research has applied meta-learning methods to distinguish between normal and malicious network traffic, showing promising results mainly in binary classification scenarios. However, challenges remain in model information acquisition within few-shot learning (FSL) frameworks. This study introduces a metric-based meta-learning strategy that constructs prototypes for each sample category, improving the model’s ability to manage multi-class scenarios. Additionally, we propose an Adaptive Feature Fusion (AFF) mechanism that dynamically integrates statistical features and binary data streams to extract meaningful insights from limited datasets, thereby enhancing the effectiveness of IDSs in few-shot learning contexts. By introducing a metric-based meta-learning method and the Adaptive Feature Fusion mechanism, this study provides a feasible solution for developing a high-accuracy, multi-class few-shot intrusion detection system. A series of experiments show that this approach significantly improves the effectiveness of the intrusion detection system, achieving an impressive accuracy of 97.78% in multi-class tasks, even when the sample size is reduced to just one.