A Data-Driven Approach to Mitigate Evolving Volumetric Attacks in Programmable Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In-network machine learning (ML) offers a cutting-edge approach for promptly detecting malicious traffic. Existing methods often rely on one-size-fits-all ML models that fail to adapt to evolving attack traffic patterns, leading to a time-consuming and labor-intensive process for updating ML model from the control to the data plane. To address these limitations, we propose an automated, data-driven method for identifying novel malicious traffic patterns and updating ML model seamlessly in programmable networks. The proposed method sets drift detection thresholds based on baseline performance from historical (i.e., training) data and uses these thresholds to detect anomalies in unseen (i.e., testing) data. We continuously adjust the thresholds to accommodate data distribution changes and in-network inference results while minimizing sensitivity to minor fluctuations. We evaluate the proposed method using two intrusion detection datasets, CICIDS2017 and UNSW-NB15. The experimental results demonstrate its efficacy in safeguarding against evolving volumetric attacks. Additionally, we compare the conventional model performance-based drift detection method with an adaptive monitoring window-based approach, highlighting the latter’s advantage in balancing drift detection efficacy and minimizing its adaptation impact, i.e., disruptions to normal network traffic are reduced by an average of 20%. The adaptive method dynamically adjusts the drift monitoring window size to adapt to the characteristics of the unseen traffic patterns.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it