Secure Peer-to-Peer Federated Learning for Efficient Cyberattacks Detection in 5G and Beyond Networks
Bibliographic record
Abstract
The Open radio access network (ORAN) supports the multiclass wireless services required in beyond 5th-generation (B5G) mobile networks. However, it also increases the threat surface, thus requiring enhanced cyberattack detection mechanisms. To do so, advanced Artificial Intelligence (AI) algorithms combined with RAN intelligent controllers (RICs) can be leveraged to detect cyberattacks, such as distributed denial-of-service (DDoS) attacks. Nevertheless, data privacy becomes a significant concern when using AI-based operations. To bypass this issue, secured Federated Learning (FL) can be leveraged. Specifically, training cyberattack detection models locally and securely communicating the models' data for aggregation would guarantee protection against eavesdropping. In addition, the usage of Peer-to-Peer (P2P) FL would allow to avoid the centralized FL's single point of failure. However, securing P2P FL with encryption/decryption or using the Secure Average Computation (SAC) would incur high communication costs that scale poorly with the number of FL clients. Hence, we propose in this paper a novel P2P FL strategy that guarantees secure FL, while significantly reducing the communication cost. Specifically, we incorporate client selection and transfer learning within the RIC-based P2P FL system to detect cyberattacks. Through experiments, we demonstrate our method's performances across different scenarios with both balanced and unbalanced dataset distributions. Finally, its superiority in terms of accuracy, robustness, and cost, compared to existing benchmarks, is illustrated.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".