About this Research Topic
Cyber-physical systems (CPSs) have become a common component in critical infrastructures thanks to the economic benefits they bring by improving productivity. These systems depend on computer and information technologies (CITs) to maintain operation, supporting communication within sub-system components as well as the outside world. Despite the economic advantages of CITs, they also have made critical infrastructure and CPSs vulnerable to various cyber threats such as interception, replacement, and removal of information from communication channels. This is particularly troublesome in mission-critical systems such as power plants, medical infrastructures, and transportation infrastructures as they constantly carry sensitive information. If attackers gain access to these systems, it can result in massive economic losses and at worst, threaten human lives.
CPSs produce massive amounts of data, which creates opportunities to use predictive Machine Learning (ML) as a viable solution to enhance the cybersecurity of these systems. Machine Learning (ML) is a branch of computer science and artificial intelligence. Over the last couple of decades, ML has transformed the world by releasing its immense power of extracting knowledge in big data streams. This Research Topic focuses on developing, adapting, and optimizing machine learning approaches for enhancing cybersecurity.
This Research Topic will provide researchers a platform for the convergence of interdisciplinary research techniques that combine methods from computer science, machine learning, and social science towards designing, developing, optimizing, and evaluating AI systems applied to improve cybersecurity. The scope of this special issue includes but is not limited to:
Use of Machine Learning/ Artificial Intelligence/ Neural Networks for Cyber Security: Theory, Recent Advancements and Applications
Explainable Artificial Intelligence for CyberSecurity Application
Cyber-physical health characterization in CPSs
Application of Large Language Models for CyberSecurity
Uncertainty quantification in cyber security
Trustworthy AI in CyberSecurity
The article types accepted in this topic are Original Research, Methods, Reviews, Brief Research Reports, Perspectives, Hypothesis and Theory.
CPSs produce massive amounts of data, which creates opportunities to use predictive Machine Learning (ML) as a viable solution to enhance the cybersecurity of these systems. Machine Learning (ML) is a branch of computer science and artificial intelligence. Over the last couple of decades, ML has transformed the world by releasing its immense power of extracting knowledge in big data streams. This Research Topic focuses on developing, adapting, and optimizing machine learning approaches for enhancing cybersecurity.
This Research Topic will provide researchers a platform for the convergence of interdisciplinary research techniques that combine methods from computer science, machine learning, and social science towards designing, developing, optimizing, and evaluating AI systems applied to improve cybersecurity. The scope of this special issue includes but is not limited to:
Use of Machine Learning/ Artificial Intelligence/ Neural Networks for Cyber Security: Theory, Recent Advancements and Applications
Explainable Artificial Intelligence for CyberSecurity Application
Cyber-physical health characterization in CPSs
Application of Large Language Models for CyberSecurity
Uncertainty quantification in cyber security
Trustworthy AI in CyberSecurity
The article types accepted in this topic are Original Research, Methods, Reviews, Brief Research Reports, Perspectives, Hypothesis and Theory.
Keywords: Cyber Security, Machine Learning, Artificial Intelligence, Neural Networks, XAI, Model Interpretability, Big Data, LLM
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
