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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".