Security and Communication Networks

Security Threats to Artificial Intelligence-Driven Wireless Communication Systems

Publishing date
01 Sep 2020
Submission deadline
08 May 2020

Lead Editor

1Tianjin University, Tianjin, China

2Nanjing University of Information Science and Technology, Nanjing, China

3University of Surrey, Guildford, UK

4Michigan State University, Michigan, USA

5University of Liverpool, Liverpool, UK

Security Threats to Artificial Intelligence-Driven Wireless Communication Systems

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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 modelling 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
Security and Communication Networks
 Journal metrics
Acceptance rate30%
Submission to final decision92 days
Acceptance to publication47 days
Impact Factor1.376
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