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A multi-level dynamic sample augmentation and heterogeneous feature fusion framework for intelligent intrusion
Ben Li1, Wenwen Sun1, Dongdong Su1
1State Grid Xinjiang Electric Power Co., Ltd. Bazhou Power Supply Company, Bazhou, 841000, China.
Scientific Reports
|April 30, 2026
Summary
This study introduces a hybrid intrusion detection framework using advanced data augmentation and feature fusion to improve network security. The novel approach enhances detection of rare attacks in imbalanced network traffic data.
Area of Science:
- Cybersecurity
- Machine Learning
- Network Intrusion Detection
Background:
- Class imbalance and temporal complexity are significant challenges in network intrusion detection.
- Existing methods struggle to effectively handle imbalanced datasets and capture complex temporal dependencies in network traffic.
Purpose of the Study:
- To propose a hybrid intrusion detection framework addressing class imbalance and temporal complexity.
- To enhance the detection of rare and sophisticated network attacks.
Main Methods:
- Implemented ADASYN-FLS-SMOTEBoost for adaptive oversampling and Focal Loss-Softmax (FLS) weighting.
- Utilized a multi-branch CNN with FLS-guided dynamic fusion for heterogeneous feature integration.
- Employed a hierarchical LSTM (HLSTM) for capturing short-term and long-term temporal dependencies.
- Optimized hyperparameters using an improved Grey Wolf Optimizer (IGWO).
Main Results:
- Achieved significant performance improvements over existing baselines on the NSL-KDD dataset.
- Demonstrated up to 10% improvement in Macro-F1 score.
- Markedly increased recall for rare attack types (R2L, U2R) while maintaining overall accuracy.
Conclusions:
- The proposed hybrid framework offers robust, interpretable, and generalizable intrusion detection.
- Effectively addresses challenges of imbalanced data and temporal complexity in network security.
- Represents a significant advancement in detecting diverse and rare network intrusions.
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