IRS-UAV Assisted Secure Integrated Sensing and Communication
Bibliographic record
Abstract
Thanks to its concurrent and dual functions, integrated sensing and communication (ISAC) has been considered as a promising technology for 6G networks. However, ISAC systems may suffer from security threats due to the broadcast nature of wireless channels. By combining the maneuverability of unmanned aerial vehicle (UAV) and the propagation environment reconfiguration capability of intelligent reflecting surface (IRS), the security challenges of ISAC can be effectively addressed. In this article, we first outline the advantages that IRS-UAV may bring to the ISAC, and introduce several typical security techniques. Then, we propose two security schemes for IRS-UAV enabled ISAC to deal with the jamming and eavesdropping attacks, respectively. In the anti-jamming design, deploying IRS-UAV can provide a line-of-sight link for the blocked target and leverage the passive beamforming to fight against the malicious jammer. Furthermore, the aerial IRS can coordinate artificial noise to ensure the accuracy of target sensing while suppressing eavesdropping. Numerical results are presented to verify the effectiveness of the proposed schemes. Finally, future challenges in this direction are outlined.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".