Editorial: recent advances in machine learning for cybersecurity

Cybersecurity has become a very hot topic in recent years. Many communities, groups, and governments start to realize the importance and urgency to deal with the ever‐changing cyberattacks.1, 2 Experts in the industry and scholars in the academia strive to innovate the next‐generation solutions. Among the technical solutions, machine learning–based methods receive an increasingly popular favor due to its superior efficiency comparing with manual analysis.2 In addition, the instantaneous protection brought by the machine learning–based solutions surpasses most reactive technologies including the automated protection systems in terms of reaction time. The general approach of machine learning–based cybersecurity solutions includes establishment of ground‐truth data, feature extraction and engineering, and model tuning. To perform these steps, one needs domain‐specific knowledge in cybersecurity and insights to machine learning principles and skills. This special issue aims to solicit cybersecurity researchers to publish the latest research findings in cybersecurity and privacy with the use of machine learning. With the conjunction of cybersecurity and machine learning, the submitted manuscripts have been evaluated in a rigorous and critical manner by professionals from different sections including both industry and academia. This review process leads to the seven accepted papers that are included in this special issue. These selected papers belong to three main research directions: system security, cybersecurity applications, and privacy applications.