Federated Learning for Anomaly-Based Intrusion Detection
Bibliographic record
Abstract
We are attending a severe zero-day cyber attacks. Machine learning based anomaly detection is definitely the most efficient defence in depth approach. It consists to analyzing the network traffic in order to distinguish the normal behaviour from the abnormal one. This approach is usually implemented in a central server where all the network traffic is analyzed which can rise privacy issues. In fact, with the increasing adoption of Cloud infrastructures, it is important to reduce as much as possible the outsourcing of such sensitive information to the several network nodes. A better approach is to ask each node to analyze its own data and then to exchange its learning finding (model) with a coordinator. In this paper, we investigate the application of federated learning for network-based intrusion detection. Our experiment was conducted based on the C ICIDS2017 dataset. We present a f ederated learning on a deep learning algorithm C NN based on model averaging. It is a self-learning system for detecting anomalies caused by malicious adversaries without human intervention and can cope with new and unknown attacks without decreasing performance. These experimentation demonstrate that this approach is effective in detecting intrusion.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".