Project: RePO and RePO+ Evaluation under Simulated Attacks
Reproducing and extending RePO and RePO+ evaluation in Mininet, introducing new metrics and performance analysis.
Overview
This project reproduces the results of RePO and RePO+—robust path selection algorithms—under simulated network attack scenarios using the Mininet environment. Beyond reproduction, it addresses model shortcomings by incorporating additional performance metrics and suggesting future improvements.
Key Features
- Reproduction of RePO and RePO+ experiments under controlled attack simulations.
- Evaluation with extended metrics like Channel State Information (CSI) and Matthews Correlation Coefficient (MCC) to gain deeper insights into model performance.
- Visual performance assessment through scatterplots and ROC curves.
- Formulation of potential future directions for enhanced robustness.
Approach
- Experiment Setup
- Implemented RePO and RePO+ in Mininet to simulate attack scenarios.
- Controlled network topology to isolate the effect of attacks.
- Metric Extension
- Added CSI and MCC to evaluate the classification and detection capabilities more comprehensively.
- Compared results to original evaluation metrics.
- Visualization
- Generated scatterplots for CSI and MCC distribution analysis.
- Constructed ROC curves to visualize trade-offs between true and false positive rates.
Results & Evaluation
- Successfully reproduced baseline results for RePO and RePO+.
- CSI and MCC revealed performance nuances missed by original metrics.
- ROC curves indicated RePO+ consistently outperformed RePO in most attack scenarios.
- Scatterplot patterns highlighted areas of model instability under high attack intensity.
Future Directions
- Explore hybrid approaches combining RePO+ with adaptive path re-selection strategies.
- Investigate deep learning–based anomaly detection to complement path selection.
- Extend testing to real-world network traces for validation.
Conclusion
- RePO+ offers measurable improvements over RePO in robustness against simulated attacks.
- Incorporating richer metrics like CSI and MCC allows for more holistic evaluation.
- Visual analysis tools enhance interpretability of network performance.