Despite its technological benefits, the Internet of Things (IoT) has cyber weaknesses due to
vulnerabilities in the wireless medium. Machine Larning (ML)-based methods are widely …

The Internet of Things (IoT) has transformed various domains in our lives by enabling
seamless communication and data exchange between interconnected devices …

Machine Learning (ML) represents a pivotal technology for current and future information
systems, and many domains already leverage the capabilities of ML. However, deployment …

The incremental diffusion of machine learning algorithms in supporting cybersecurity is
creating novel defensive opportunities but also new types of risks. Multiple researches have …

Despite decades of research in network traffic analysis and incredible advances in artificial
intelligence, network intrusion detection systems based on machine learning (ML) have yet …

Machine Learning (ML) has become a valuable asset to solve many real-world tasks. For
Network Intrusion Detection (NID), however, scientific advances in ML are still seen with …

The security model relying on the traditional defense of the perimeter cannot protect modern
dynamic organizations. The emerging paradigm called zero trust proposes a modern …

Machine-learning solutions are successfully adopted in multiple contexts but the application
of these techniques to the cyber security domain is complex and still immature. Among the …

Intrusion alert analysis is an attractive and active topic in the area of intrusion detection
systems. In recent decades, many research communities have been working in this field …

Machine learning is increasingly adopted for a wide array of applications, due to its
promising results and autonomous capabilities. However, recent research efforts have …