Examining Adversarial Examples Impact on Artificial Intelligence Based Blockage Prediction Systems for Ultra-Wideband Communication in Indoor Environments

Abstract

Recently, much effort and interest have been devoted to developing accurate and reliable blockage prediction systems. The latest research tends to apply artificial intelligence to enhance blockage prediction systems' accuracy, especially when dealing with non-line-of-sight radio signal propagation. However, AI models can carry inherent vulnerabilities that might damage the relevant system. Therefore, this paper aims to examine the effect of adversarial examples on AI-based blockage prediction systems. By performing adversarial example attacks on the AI-based blockage prediction model, we have shown the drastic impact on root mean square error that has arisen from 0.26 to 0.82 after introducing adversarial examples with 0.08 magnitude. Additionally, we have proposed a defensive approach based on encryption techniques to proactively prevent further compromise of the targeted system. The proposed method not only enhances data privacy but also help to prevent adversarial attacks that exploit transmitted data.