Attention-Enhanced Defensive Distillation Network for Channel Estimation in V2X mm-Wave Secure Communication.

Area of Science:

• Cybersecurity
• Wireless Communications
• Intelligent Transportation Systems

Background:

• Millimeter-wave (mm-wave) technology is vital for advanced networks and vehicle-to-everything (V2X) communications, offering high bandwidth.
• V2X mm-wave systems face significant cybersecurity threats, including interference and eavesdropping, which traditional methods struggle to address.
• Adversarial attacks pose a critical risk to the reliability and security of V2X mm-wave communication.

Purpose of the Study:

• To develop a robust defense mechanism against adversarial attacks in V2X mm-wave communication.
• To enhance the accuracy and resilience of V2X mm-wave systems under sophisticated cyber threats.
• To introduce the attention-enhanced defensive distillation network (AEDDN) for improved V2X mm-wave security.

Main Methods:

• The study proposes an Attention-Enhanced Defensive Distillation Network (AEDDN) model.
• AEDDN integrates the transformer algorithm's attention mechanism with defensive distillation techniques.
• The model was evaluated using the 6g-channel-estimation and MMMC datasets, with comparisons to a Convolutional Neural Network (CNN).

Main Results:

• The AEDDN model demonstrated superior performance in enhancing robustness and accuracy compared to the CNN model.
• The attention mechanism effectively focuses on critical channel features, adapting to complex communication conditions.
• Defensive distillation smoothed decision boundaries, reducing sensitivity to adversarial perturbations.

Conclusions:

• The AEDDN model offers a promising solution for securing V2X mm-wave communications against adversarial attacks.
• The proposed method significantly improves the performance and reliability of intelligent transportation systems.
• AEDDN shows enhanced capabilities, particularly within complex V2X mm-wave environments.