Intelligent Anti-Jamming Based on Deep Reinforcement Learning and Transfer Learning
Bibliographic record
Abstract
One of the security issues in a wireless network is jamming attacks, where the jammer causes congestion and significant decrement in the network throughput by obstructing channels and disrupting user signals. Recent works have proposed using a deep-reinforcement learning (DRL) model to confront the jamming attacker due to its capability in predicting the jammer decisions and pattern recognition. Training a DRL model from scratch may take a long time. We first propose a recurrent neural network architecture to minimize the number of parameters for training a DRL model. We further propose a transfer learning (TL) approach to enable the DRL agent to learn fast in dynamic wireless networks to confront jamming attacks effectively. To make our proposed TL method adaptive to different network environments, we propose a novel method to quantitatively measure the difference between the source and target domains, using an integrated feature extractor. Afterward, based on the measured difference, we can choose an optimal setting for the TL model. We also show that the proposed TL method can effectively reduce the training time for the DRL model and outperforms other existing TL methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".