Recently, we have seen several machine learning (ML) and artificial intelligence (AI) applications such as game, machine vision, image processing, natural language processing, self-driving car, robotics and data analytics, where AI exhibits better machine cognition than the human cognition system. This result attracts the cyber defense teams to leverage data-driven techniques with AI for cybersecurity, where AI learns and enhances its knowledge base more quickly to better detect, predict and respond to cyber-attacks. At the same time, ML algorithms and AI systems can be controlled, dodged, biased and misled through flawed ML/AI learning models, input training/actual data or decision classifier; thus, ML algorithms and AI systems need robust security for trustworthy AI. Emerging networked systems and applications are expected to rely on AI/ML; it is essential to consider cybersecurity for AI for trustworthy AI systems. Emerging networked systems are expected to collect, store and share personally identifiable information (PII) to some extent, to offer accountability and non-repudiation. Cyber attackers could steal PII or related information such as bank account number, utility account number, date of birth, credit card numbers, medical records, social security numbers, drivers' license numbers and state IDs to compromise the privacy of the person. Moreover, cloud-based storage can easily lead to personal information being inferred about individuals. When cloud storage happens outside the country, it adds more privacy challenges because different laws are applied on different countries. Similarly, GPS traces can be used to track vehicles and thereby the drivers/owners/renters of the vehicles. Medical records are essential for personal health and medication. Recently, caused by medical IoT devices, healthcare system breaches affected nearly one million us patients [17]. When medical record or data is compromised, it is a life-or-death situation when medication information is compromised [7, 17]. Smart home systems could be exploited to understand the lifestyle of a person and/or could provide the means to track individuals and their personal activities. While addressing these challenges related to cybersecurity and privacy in smart networked systems and critical infrastructure, it is essential to explore and study the systems and solutions from legal, ethical and policy point of view, to make the systems acceptable, usable and trustworthy. All in all, it is crucial to have security and privacy for emerging networked systems and critical infrastructure, considering the importance and constraints imposed by such systems. The goal of the _Journal of Cybersecurity and Privacy_ is to provide a venue for practitioners and researchers from academia, governments and industries, to advance the state of the art in security and privacy research and practice, present new results and provide future visions on these topics for an increasingly connected cyber world. The technical scope of this journal is interdisciplinary, involving contributions from different technical disciplines addressing both cybersecurity and privacy, such as cryptography, wireless security, computer security, network security, information security, socioeconomic aspects of cybersecurity and privacy, legal/ethical/policy aspects of cybersecurity and privacy, signal processing for secure systems, information and communications theory for secure communications, game theoretic security and AL/ML for security, security for AI/ML, data mining and data analytics.