A lightweight anomaly detection model for network traffic using multi scale spatio temporal residual learning
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a lightweight knowledge transfer network for efficient network abnormal traffic detection, improving accuracy and reducing computational load for enhanced network security.
Area Of Science
- Cybersecurity
- Network Security
- Machine Learning
Background
- Network attacks are evolving, necessitating advanced abnormal traffic detection.
- Existing methods struggle with large-scale data, class imbalance, and computational efficiency.
Purpose Of The Study
- To develop a lightweight knowledge transfer anomaly detection network for improved network security.
- To address challenges in large-scale traffic analysis, class imbalance, and computational efficiency.
Main Methods
- Utilized multi-scale residual networks for spatio-temporal feature extraction.
- Implemented a lightweight knowledge transfer anomaly detection network with knowledge distillation.
- Migrated knowledge from a teacher model to a lightweight student model.
Main Results
- Achieved 0.93 accuracy and 0.27 loss at 100 iterations.
- Reached 0.97 specificity with a sample size of 5000.
- Demonstrated low training (22.8s) and inference (0.06s) times.
- Showcased 0.07% traffic loss under high attack intensity.
Conclusions
- The proposed method effectively handles complex network traffic data.
- Enhanced precision and deployment effectiveness for anomalous detection.
- Shows significant promise for real-world network security applications.
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