Summary of Predictive Covert Communication Against Multi-uav Surveillance Using Graph Koopman Autoencoder, by Sivaram Krishnan et al.
Predictive Covert Communication Against Multi-UAV Surveillance Using Graph Koopman Autoencoder
by Sivaram Krishnan, Jihong Park, Gregory Sherman, Benjamin Campbell, Jinho Choi
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Networking and Internet Architecture (cs.NI); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework for predictive covert communication is introduced to minimize detectability in terrestrial ad-hoc networks under multi-UAV surveillance. By synergistically integrating graph neural networks with Koopman theory, the approach models complex interactions within a multi-UAV network and facilitates long-term predictions even with limited historical data. Simulation results demonstrate at least 63%-75% lower probability of detection compared to state-of-the-art baseline approaches, showing promise for enabling low-latency covert operations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to hide radio signals from being detected is developed. This helps keep communication secret when using drones (UAVs) to monitor things. To do this, the location of the drones must be predicted accurately in real-time. The paper presents a method that uses special networks and math to make predictions. It works by looking at how the drones move and then uses that information to guess where they will go next. This makes it harder for others to detect the communication signals. The results show that this method is better than other existing methods, making it promising for real-world use. |
Keywords
* Artificial intelligence * Probability
