In recent years, the collection, processing, and analysis of enterprise, government, and personal data have become greatly convenient and widespread, as the continuous advancement of emerging applications such as Cyber-physical systems, social networks, E-commerce, and 5G Systems. This also makes sensitive information more vulnerable to abuses, and thus secure mechanisms and technologies tailored for emerging applications need to be explored urgently.

Machine learning (ML) has recently gained a renewed interest as the technology powering it has become more widely available and accessible to organizations of all sizes. Applications using machine learning are being deployed in contexts and for purposes that were not even imaginable a few years ago. From a Cybersecurity, Privacy, and Public Safety angle, ML brings about both opportunities and challenges for emerging applications. On the one hand, ML can help interested parties to better protect privacy in challenging situations, improving the state-of-the-art security solutions. On the other hand, ML also presents risks of opaque decision making, biased algorithms, and safety vulnerabilities, challenging traditional notions of privacy protection.

This Special Issue aims to provide a forum for those from academia and industry to communicate their latest results on theoretical advances and industrial case studies that combine ML techniques, such as reinforcement learning, adversarial machine learning, and deep learning, with significant problems in Cybersecurity, Privacy, and Public Safety. Research papers can be focused on offensive and defensive applications of ML to security. Submissions can contemplate original research, serious dataset collection and benchmarking, or critical surveys. Review articles are also welcome.

Potential topics include but are not limited to the following:

• Security machine learning modelling and architecture
• Secure multi-party computation techniques for machine learning
• Attacks against machine learning
• Machine learning threat intelligence
• Machine learning for Cybersecurity
• Machine learning for intrusion detection and response
• Machine learning for multimedia data security
• Machine learning for public safety