<ul><li>Machine learning (ML) has revolutionized wireless communication systems, enhancing applications like modulation recognition, resource allocation, and signal detection.</li><li>However, the growing reliance on ML models has increased the risk of adversarial attacks, which threaten the integrity and reliability of these systems.</li><li>A recent paper at the International Conference on Computing, Control and Industrial Engineering 2024 explores adversarial machine learning in wireless communication systems and discusses potential defense mechanisms to enhance their robustness.</li><li>The paper highlights vulnerabilities in ML models used in wireless communication systems, such as the susceptibility during spectrum sensing, and proposes defense mechanisms like adversarial training and statistical methods to mitigate adversarial risks.</li></ul>

Adversarial Machine Learning in Wireless Communication Systems

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