Preamble Injection-Based Jamming Method for UAV LoRa Communication Links.

Area of Science:

• Electrical Engineering
• Cybersecurity
• Signal Processing

Background:

• Widespread use of low-cost unmanned aerial vehicles (UAVs) presents security challenges.
• LoRa (Long Range) communication protocols used by UAVs are difficult to counter with traditional methods due to their range and anti-interference capabilities.
• Existing countermeasures for LoRa links exhibit delayed response, poor adaptability, and high power consumption.

Purpose of the Study:

• To develop an efficient and adaptable countermeasure technique for LoRa-based UAV communication.
• To address the limitations of traditional methods in detecting and jamming UAV signals.
• To enhance the security of airspace against unauthorized UAVs.

Main Methods:

• Utilizing neural networks for efficient detection and parameter extraction of LoRa signals in complex electromagnetic environments.
• Designing a continuous preamble injection jamming method based on extracted LoRa signal parameters.
• Implementing protocol-level injection to disrupt UAV communication synchronization and demodulation.

Main Results:

• Achieved high accuracy in detecting LoRa signals using neural networks.
• Demonstrated significantly enhanced jamming efficiency and reduced energy consumption compared to conventional methods.
• Validated improved signal detection accuracy, jamming energy efficiency, and effective range.

Conclusions:

• The proposed protocol-aware scheme offers a promising and cost-effective technical pathway for UAV countermeasures.
• Integration of neural network-based signal perception and denoising enhances countermeasure effectiveness.
• This method provides a robust solution for addressing security concerns posed by LoRa-equipped UAVs.