The tampering of information has been proven to be very dangerous, especially for industrial control systems that support or offer critical services to citizens, such as energy plants, and solutions that can secure those systems are in need. On June 2022, the EU Member States agreed on revising the EU Network and Information Security Directive: NIS2. One of the additions of NIS2 as compared to the NIS was the extension of the scope of the NIS with the addition of new sectors, such as the food sector. The authors in [14] discuss the new threats and security challenges that the digitization of agriculture has to face. Among the many open issues, challenges, and future directions that both organizations and governments will face, the authors propose the development of proper incident response and business continuity plans. Focusing on the security of industrial control systems (ICS), the authors in [15] propose a configurable dependency model that can be used for risk assessment. The authors identified correctly the specific requirements of an ICS risk model that includes the need to cope with the diversity of experts that cooperate in an ICS along with the need to create a model that can offer an overview of the system at a high level of abstraction while capturing all inter-dependencies. Using the proposed configurable model organisations can save time and resources dedicated to risk assessment and thus be better prepared for cybersecurity incidents. It has been proven that machine learning principles can be used in order to deploy efficient intrusion detection systems. One-class models have long been proven to be very efficient in circumstances where there is a need for the detection of both known and unknown (novel) attacks; the model must be robust to noise samples and where there is a lack of datasets that include attacks when training the model, all of the above being the standard situation for an ICS. The authors in [16] propose a new IDS that integrates long short-term memory principles into the one-class model and has been tested through extensive experiments on three complex network security data sets. Following a similar approach, the authors in [17] propose an intelligence system based on machine learning and deep learning approaches to detect serious attacks on ICSs. In another article in this collection, the authors in [18] analyzed the classification of 0-days threats and anomalous intrusion in a novel dataset that includes cloud services. In addition, the authors in [19] propose a malicious anonymous proxy traffic that is based on the principles of deep learning and image transformation. In order to keep the computational overhead of image transformation low, the method converts the sequences of the size and inter-arrival time of the first N packets of a flow into images before applying classification. The proposed methods achieve high accuracy while keeping the sizes of the produced images more than 90% smaller than that of existing image-based deep learning methods. All of the aforementioned works of the SI collection are very important since they provide valuable conclusions about the performance of several machine-learning-based intrusion-detection systems against sophisticated attacks, methods to improve their performance and possible future improvements or research directions. Mobile phones have been used recently for tasks that are not directly linked to communication between users. E-banking, e-commerce, emails, and remote desktop services can help users stay always connected and to perform many of their activities through their mobile devices. The vulnerabilities in these services must be discovered through the execution of the code into a safe or isolated environment. Often, this leads the normal operation of the OS to halt, thus making it unable to offer any services. The authors in [20] try to solve this problem by proposing a secure service provisioning platform that guarantees the execution time of the normal OS while providing hypervisor-level security services.