Advances in Machine Learning Technology in Information and Cyber Security

Dear Colleagues,

Due to the rapid spread of sensors and mobile devices, we have been experiencing a new revolution in communication in recent years. Every firm now places a high focus on cyber security. The proper controls and processes must be put in place in order to detect such attacks and protect against them. However, there will always be more cyberattacks than people attempting to defend themselves from them. Daily risks are being found, making it more difficult for present solutions to handle a lot of data to analyze.

Cyber security and the detection of cyberthreats could greatly benefit from the advancement of artificial intelligence (AI). Machine learning algorithms can be trained to look for assaults that resemble other types of attacks. By doing so, we are able to identify even the first of their sort of breach and create improved security measures.

Machine learning techniques have shown to be advantageous for the entire security business, i.e., they can aid in the automatic learning of information from data sources and lessen the workload of analysts. Deep face recognition and natural language processing are two further areas where cutting-edge methods such as reinforcement learning and deep learning can be employed. However, the lack of standardized datasets, overfitting difficulties, the cost of the architecture, and other factors frequently restrict the implementation of machine learning. Thus, it is crucial to implement and create new strategies in order to keep the advantages of machine learning algorithms while addressing the practical constraints.

This Special Issue will focus on the cutting-edge research from both academia and industry, and it aims to solicit original research papers with a particular emphasis on the challenges and future trends in cyber security with machine learning applications.

Potential topics include, but are not limited to, the following:

• Cyber security management in cyber-physical systems using AI;
• Security, privacy, and trust issues in cyber-physical systems;
• Blockchain-enabled cyber-physical systems;
• Utilizing AI technologies for cyber investigation and threat intelligence;
• The integration of AI and blockchains for security critical infrastructures;
• Design, optimization, and modeling of cyber security management systems;
• AI and ML for intrusion detection/prevention in sensitive environments;
• Advanced AI techniques to secure future Internet architectures/protocols;
• Trust management in cyber-physical networks and systems;
• Privacy management at the edge of the network using machine learning;
• Trustworthy data collection and processing using intelligent learning techniques;
• Cyber security management of big data;
• AI-based cyber security techniques for IoT, IoE, IoH, and IoV;
• Cyber security of connected and autonomous vehicles;
• Cyber security and AI for digital twins;
• Management framework for intelligent secure networking;
• Cyber security management to protect organizations’ sensitive data using intelligent learning techniques;
• AI-enabled digital investigation.

Prof. Dr. Ming Hour Yang
Prof. Dr. Vijayalakshmi Murugesan
Prof. Dr. Mercy Shalinie Selvaraj
Guest Editors

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