Lightweight AI-Based Attack Detection for LED VLC in Multi-Channel Airborne Radar Systems
Saint Petersburg State University of Aerospace and Instrumentation · Saint-Petersburg State University of Technology and Design · +2 more institutions
Abstract
Compact multi-channel airborne radar stations increasingly rely on an LED-based visible light communication (VLC) service link under radio-frequency spectrum restrictions and strict end-to-end delay constraints. Despite the directional nature of optical links, the VLC channel remains vulnerable to active optical interference and signal injection; furthermore, when an AI-enabled integrity monitor is embedded into the receiver, the AI decision layer becomes a direct target of evasion and online poisoning. This paper proposes a lightweight, interpretable AI-based attack detection architecture in which a Poisson photon-counting observation model is used to form physically consistent features over the preamble and…
Citation impact
- FWCI
- 43.76
- Percentile
- 100%
- References
- 0
Authors
4- VAVadim A. NenashevCorresponding
Saint Petersburg State University of Aerospace and Instrumentation, Saint-Petersburg State University of Technology and Design
- VPVladimir P. Kuzmenko
Saint Petersburg State University of Aerospace and Instrumentation
- SSSvetlana S. Dymkova
Moscow Technical University of Communication and Informatics, Institute of Radio and Information Systems (IRIS)
- OVOleg V. Varlamov
Moscow Technical University of Communication and Informatics, Institute of Radio and Information Systems (IRIS)
Topics & keywords
- Spoofing attack
- Radar
- Detector
- Preamble
- Latency (audio)
- Interference (communication)
- Waveform
- Poisson distribution
- Peace, Justice and strong institutions