Integrated Sensing and Communications Using Generative AI: Countering Adversarial Machine Learning Attacks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the field of Integrated Sensing and Commu-nication (ISAC) systems, several challenges emerge, such as obtaining the infinitesimal Cramer-Ran lower bound (CRLB) for sensing outcomes and addressing the vulnerabilities of ISAC to adversarial machine learning (AML) attacks. To address this, we propose a Smart ISAC (S-ISAC) system, which incorporates a unique generative adversarial network (GAN) combined with a differentiable Kolmogorov-Smirnov (KS) loss function, named KSGAN. This KSGAN is tailor-made to identify AML attacks on range-Doppler heatmap features. Only after ensuring that the range-Doppler heatmap is free from AML attacks using KSGAN, do we apply the Constant False Alarm Rate (CFAR) for accurate estimation of target vehicle parameters. We implement a rigorous ISAC system under AML attacks using Matlab Toolboxes and the adversarial robustness toolbox (ART). Our numerical findings indicate that the proposed KSGAN offers greater accuracy in detecting AML than a standalone GAN. Additionally, our results show that the MIMO S-ISAC Beamforming surpasses the performance of the standalone ISAC system.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it