Due to the openness of wireless channels, wireless communication systems are extremely vulnerable to attacks, counterfeiting, and eavesdropping. With the widespread adoption of artificial intelligence (AI) technologies in fifth generation (5G) and beyond fifth generation (B5G) networks, AI-based attacks have emerged as new threats to wireless communication systems. Deep learning-based end-to-end communication systems are extremely susceptible to physical adversarial attacks which can cause a serious reduction in the accuracy of signal classification or radio modulation recognition. Intelligent threats can utilize AI to attack future networks, related services and applications that use deep learning algorithms where small disturbances can be easily designed and generated by attackers. However, researchers still need to consider how best to protect 5G and B5G networks from AI-related attacks. Furthermore, defense strategies of adversarial attacks for future communication systems are still underdeveloped and inefficient.

This Special Issue welcomes novel theoretical contributions, practical research and review articles that analyze the security threats, challenges, and mechanisms inherent in AI-driven wireless communication systems. Research that highlights potential defense approaches and strategies to combat intelligent attacks on such systems are particularly encouraged.

Potential topics include but are not limited to the following:

• Architectures, simulators, scenarios, and applications tuned to security and privacy issues for AI-driven wireless communication systems
• Adversarial attacks on AI-based signal classification or radio modulation recognition
• White-box or black-box-based attacks on signal/modulation classifiers
• Effective attacks detection and prediction based on deep learning techniques, e.g. autoencoder (AE), deep neural network (DNN), generative adversarial network (GAN) and deep reinforcement learning (DRL)
• Defense mechanisms and theories of adversarial attacks against end-to-end communication systems
• Adversarial modeling or adversarial deep learning for future wireless networks
• Security threats to 5G and B5G-based applications, services, and IoT devices
• Defense strategies and solutions for AI-related attacks on wireless communication systems
• Robust algorithms to protect against AI-related attacks on wireless communication systems