Impact Score 6.6
With the prevalence of the Internet of Things (IoT) and Industrial IoT (IIoT), there are large-scale data sources collected from distributed and heterogeneous systems and networks which need significant effort to process them at the edge and/or cloud layers. Existing computing paradigms, such as osmotic computing, would integrate orchestrated and distributed services, where they may help to develop distributed Artificial Intelligence (AI)-enabled cyber defence techniques that would be then embedded at computing systems and networks.
AI-based techniques, including machine learning, deep learning, data mining, optimization, text mining, and network science, have been recently employed in developing cybersecurity techniques and tools. Existing AI-enabled cyber defences, involving intrusion detection and prevention systems, security information and event management, firewalls, digital forensics, authentication, and privacy preservation, face challenges in discovering distributed cyberattacks, preserving sensitive data, and tracing the originality of security events, from distributed systems and networks.
Microservices, such as Docker Swarm and Kubernetes, which decompose applications, manage, and deploy a set of services to applications, can be used to develop reliable AI-enabled cyber defence techniques. There are no standard architectures or approaches that explain the development of AI-enabled microservices techniques, enabling their deployment and embedding at lightweight systems and distributed networks. Existing microservice approaches, methods and frameworks have challenges related to flexibility, security and privacy of distributed systems and networks, which do not enable the automation of the service orchestration management of distributed systems and networks.
This special issue seeks high-quality papers related to novel techniques, algorithms, architectures, and methodologies related to developing embedded AI-based cyber defence microservices in distributed systems and networks. Topics of interest for this special issue include, but not limited to:
Authors should follow the Journal of Parallel and Distributed Computing manuscript format described at the journal site: https://www.elsevier.com/journals/journal-of-parallel-and-distributed-computing/0743-7315/guide-for-authors. Manuscripts that extend research published previously (e.g., in conference or workshop proceedings) will only be considered if they include at least 30% of significantly new material; the submission of such manuscripts must be accompanied by a “Summary of Differences” letter explaining how the authors extended their previously published work. All manuscripts and any supplementary material should be submitted through Editorial Manager (EM), available at: https://www.editorialmanager.com/jpdc/default.aspx. The authors must select "VSI: AI-CDM " when they reach the "Article Type" in the submission process.