Abstract
Nowadays, data security is becoming an emerging and challenging issue due to the growth in web-connected devices and significant data generation from information and communication technology (ICT) platforms. Many existing types of research from industries and academic fields have presented their methodologies for supporting defense against security threats. However, these existing approaches have failed to deal with security challenges in next-generation ICT systems due to the changing behaviors of security threats and zero-day attacks, including advanced persistent threat (APT), ransomware, and supply chain attacks. The symmetry-adapted machine-learning approach can support an effective way to deal with the dynamic nature of security attacks by the extraction and analysis of data to identify hidden patterns of data. It offers the identification of unknown and new attack patterns by extracting hidden data patterns in next-generation ICT systems. Therefore, we accepted twelve articles for this Special Issue that explore the deployment of symmetry-adapted machine learning for information security in various application areas. These areas include malware classification, intrusion detection systems, image watermarking, color image watermarking, battlefield target aggregation behavior recognition models, Internet Protocol (IP) cameras, Internet of Things (IoT) security, service function chains, indoor positioning systems, and cryptoanalysis.
Highlights
In the current era, information, and communication technology (ICT) supports a large amount of data to provide intelligent services to next-generation industries
We accepted twelve articles for this Special Issue that explore the deployment of symmetry-adapted machine learning for information security in various application areas
Defense attempts against security threats still fail due to a lack of skilled cyber talent and the existence of low-security policies
Summary
Information, and communication technology (ICT) supports a large amount of data to provide intelligent services to next-generation industries. Various symmetry-adapted machine-learning paradigms, including generative adversarial networks, continuous learning, one-shot learning, and deep learning have been developed to perform data processing and analysis tasks in ICT systems. These paradigms can be adapted to detect security threats, such as software exploits and unknown malware. This Special Issue, Symmetry-Adapted Machine Learning for Information Security, includes the development of novel approaches with innovative architectural designs and frameworks for security attack mitigation in ICT systems by employing various machine learning paradigms. The accepted papers focus on various application areas: malware classification, intrusion detection systems, image watermarking, color image watermarking, the battlefield target aggregation behavior recognition model, IP cameras, IoT security, service function chains, indoor positioning systems, and cryptoanalysis
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