Poisoning Attacks on Deep Learning based Wireless Traffic Prediction
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 108times 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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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".