Jamming Attacks and Mitigation in Transfer Learning Enabled 5G RAN Slicing
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Bibliographic record
Abstract
Radio access technology is crucial in both 5G and 6G cellular networks, providing differentiated services that demand reliability, low latency, and high throughput. To meet these requirements, machine learning (ML) has demonstrated considerable progress by facilitating resource allocation. However, these ML techniques can be susceptible to attacks, and the jamming attack is one of the most considered attacks in the literature, disrupting network functionality by sending interference signals. This paper, to the best of our knowledge for the first time, examines the vulnerability of radio access networks (RANs) to jamming attacks on resource allocation of a transfer reinforcement learning (TRL) based system and provides a mitigation approach to such attacks. A system model is presented for RAN slicing, followed by an introduction of the TRL algorithm for resource allocation. Afterward, we investigate covert patterned jamming attack (CPJA) on the TRL algorithm in downlink communication which decreases system throughput by 17% and 38.14% in the expert and learner agents and increases latency by 7.36% and 9.37% respectively. In addition, we propose a neural network (NN) solution to mitigate the CPJA trained on the network side and provide the trained NN model to the users' equipment (UEs) to eliminate interference from the signal by the filter. The trained NN is applied to predict the future activity of the interference generated by the attacker. Attack mitigation reduces the impact of the attack while the system's throughput suffers a 6% and 1.8% degradation, and its latency increases by 6.5% and 3.83% compared to the original system for expert and learner agents, respectively.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it