Security Information and Event Management (SIEM) systems have been widely deployed as a powerful tool to prevent, detect, and react against cyber-attacks. SIEM solutions have evolved to become comprehensive systems that provide a wide visibility to identify areas of high risks and proactively focus on mitigation strategies aiming at reducing costs and time for incident response. Currently, SIEM systems and related solutions are slowly converging with big data analytics tools. We survey the most widely used SIEMs regarding their critical functionality and provide an analysis of external factors affecting the SIEM landscape in mid and long-term. A list of potential enhancements for the next generation of SIEMs is provided as part of the review of existing solutions as well as an analysis on their benefits and usage in critical infrastructures.

Addressing cyber and privacy risks has never been more critical for organisations. While a number of risk assessment methodologies and software tools are available, it is most often the case that one must, at least, integrate them into a holistic approach that combines several appropriate risk sources as input to risk mitigation tools. In addition, cyber risk assessment primarily investigates cyber risks as the consequence of vulnerabilities and threats that threaten assets of the investigated infrastructure. In fact, cyber risk assessment is decoupled from privacy impact assessment, which aims to detect privacy-specific threats and assess the degree of compliance with data protection legislation. Furthermore, a Privacy Impact Assessment (PIA) is conducted in a proactive manner during the design phase of a system, combining processing activities and their inter-dependencies with assets, vulnerabilities, real-time threats and Personally Identifiable Information (PII) that may occur during the dynamic life-cycle of systems. In this paper, we propose a cyber and privacy risk management toolkit, called AMBIENT (Automated Cyber and Privacy Risk Management Toolkit) that addresses the above challenges by implementing and integrating three distinct software tools. AMBIENT not only assesses cyber and privacy risks in a thorough and automated manner but it also offers decision-support capabilities, to recommend optimal safeguards using the well-known repository of the Center for Internet Security (CIS) Controls. To the best of our knowledge, AMBIENT is the first toolkit in the academic literature that brings together the aforementioned capabilities. To demonstrate its use, we have created a case scenario based on information about cyber attacks we have received from a healthcare organisation, as a reference sector that faces critical cyber and privacy threats.

Point-of-care systems are generally used in healthcare to respond rapidly and prevent critical health conditions. Hence, POC systems often handle personal health information; and consequently, their cybersecurity and privacy requirements are of crucial importance. While, assessing these requirements is a significant task. In this work, we propose a use case approach to assess specifications of cybersecurity and privacy requirements of POC systems in a structured and self-contained form. Such an approach is appropriate since use cases are one of the most common means adopted by developers to derive requirements. As a result, we detail a use case approach in the framework of a real-based healthcare IT infrastructure that includes a health information system, integration engines, application servers, web services, medical devices, smartphone apps and medical modalities (all data simulated) together with the interaction with participants. Since our use case also sustains the analysis of cybersecurity and privacy risks in different threat scenarios, it also supports decision making and the analysis of compliance considerations.

As the confidentiality and integrity of modern health infrastructures is threatened by intrusions and real-time attacks related to privacy and cyber-security, there is a need for proposing novel methodologies to predict future incidents and identify new threat patterns. The main scope of this article is to propose an advanced extension to current Intrusion Detection System (IDS) solutions, which (i) harvests the knowledge out of health data sources or network monitoring to construct models for new threat patterns and (ii) encompasses methods for detecting threat patterns utilizing also advanced unsupervised machine learning data analytic methodologies. Although the work is motivated by the health sector, it is developed in a manner that is directly applicable to other domains.