Combating Malware Traffic in Emerging Networks: A Collaborative Learning Approach
Bibliographic record
Abstract
Identifying and mitigating malicious traffic poses a major challenge in the evolving field of network security. As threat profiles rapidly evolve, dynamic and adaptive methods are needed to match the swiftly changing attack patterns. This paper introduces a novel application of Federated Learning (FL) in the realm of malware traffic detection. As a distributed training framework, FL allows multiple network nodes to collaboratively learn a shared prediction model while keeping all the training data localized, thus ensuring data privacy and security. We present an effective malware identification and classification architecture utilizing Convolutional Neural Networks (CNN) within the FL framework distributed among network nodes. The paper details the design and implementation of this system, highlighting the integration of FL to harness the collective intelligence of diverse data sources without compromising data privacy. Our approach provides a scalable and efficient solution adaptable to diverse network environments. The results showcase the potential of FL in enhancing network security mechanisms, opening new avenues for combatting sophisticated cyber threats in an increasingly connected world.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".