Poisoning Attacks on Deep Learning based Wireless Traffic Prediction
Why this work is in the frame
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Bibliographic record
Abstract
Big client data and deep learning bring a new level of accuracy to wireless traffic prediction in non-adversarial environments. However, in a malicious client environment, the training-stage vulnerability of deep learning (DL) based wireless traffic prediction remains under-explored. In this paper, we conduct the first systematic study on training-stage poisoning attacks against DL-based wireless traffic prediction in both centralized and distributed training scenarios. In contrast to previous poisoning attacks on computer vision, we consider a more practical threat model, specific to wireless traffic prediction, to design these poisoning attacks. In particular, we assume that potential malicious clients do not collude or have any additional knowledge about the other clients’ data. We propose a perturbation masking strategy and a tuning-and-scaling method to fit data and model poisoning attacks into the practical threat model. We also explore potential defenses against these poisoning attacks and propose two defense methods. Through extensive evaluations, we show the mean square error (MSE) can be increased by over 50% to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> times with our proposed poisoning attacks. We also demonstrate the effectiveness of our data sanitization approach and anomaly detection method against our poisoning attacks in centralized and distributed scenarios.
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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 | 0.001 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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