Abstract Machine Learning (ML) has been enjoying an unprecedented surge in applications
that solve problems and enable automation in diverse domains. Primarily, this is due to the …

Driven by the increasing scale and high profile cybersecurity incidents related public data,
recent years we have witnessed a paradigm shift in understanding and defending against …

This survey paper describes the significant literature survey of Sustainable Smart Cities
(SSC), Machine Learning (ML), Data Mining (DM), datasets, feature extraction and selection …

Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as
the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of …

Machine-learning (ML) enables computers to learn how to recognise patterns, make
unintended decisions, or react to a dynamic environment. The effectiveness of trained …

Modern society is increasingly moving toward complex and distributed computing systems.
The increase in scale and complexity of these systems challenges O&M teams that perform …

Machine learning is widely used to solve networking challenges, ranging from traffic
classification and anomaly detection to network configuration. However, machine learning …

As a fundamental tool for network management and security, traffic classification has
attracted increasing attention in recent years. A significant challenge to the robustness of …

The conformal predictions framework is a recent development in machine learning that can
associate a reliable measure of confidence with a prediction in any real-world pattern …

Traffic classification has wide applications in network management, from security monitoring
to quality of service measurements. Recent research tends to apply machine learning …