Work

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

Year: 2024

Type: article

Abstract: Millimeter-wave (mm-wave) technology, crucial for future networks and vehicle-to-everything (V2X) communication in intelligent transportation, offers high data rates and bandwidth but is vulnerable to adversarial attacks, like interference and eavesdropping. It is crucial to protect V2X mm-wave communication from cybersecurity attacks, as traditional security measures often fail to counter sophisticated threats and complex attacks. To tackle these difficulties, the current study introduces an attention-enhanced defensive distillation network (AEDDN) to improve robustness and accuracy in V2X mm-wave communication under adversarial attacks. The AEDDN model combines the transformer algorithm with defensive distillation, leveraging the transformer's attention mechanism to focus on critical channel features and adapt to complex conditions. This helps mitigate adversarial examples by filtering misleading data. Defensive distillation further strengthens the model by smoothing decision boundaries, making it less sensitive to small perturbations. To evaluate and validate the AEDDN model, this study uses a publicly available dataset called 6g-channel-estimation and a proprietary dataset named MMMC, comparing the simulation results with the convolutional neural network (CNN) model. The findings from the experiments indicate that the AEDDN, especially in the complex V2X mm-wave environment, demonstrates enhanced performance.

Source: Sensors

DOI: 10.3390/s24196464

Authors: Xingyu Qi, Yuanjian Liu, Yingchun Ye

Institution: Nanjing University of Posts and Telecommunications

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,955