As organisations are vulnerable to cyberattacks, their protection becomes a significant issue. Capability Maturity Models can enable organisations to benchmark current maturity levels against best practices. Although many maturity models have been already proposed in the literature, a need for models that integrate several regulations exists. This article presents a light, web-based model that can be used as a cybersecurity assessment tool for Higher Education Institutes (HEIs) of the United Kingdom. The novel Holistic Cybersecurity Maturity Assessment Framework incorporates all security regulations, privacy regulations, and best practices that HEIs must be compliant to, and can be used as a self assessment or a cybersecurity audit tool.

The surge in cyber security breaches including the shortage of skilled cyber incident response (CSIR) professionals and the ever‐changing cyber threat landscape is a big concern for the security industry. As a result, training providers are seeking innovative ways to tackle current security challenges. Businesses in public and private sectors recognize the importance of implementing effective cyber security measures, one of which is training their employees. Many are taking active steps to ensure that employees and cyber security incident response teams (CSIRTs) can identify and respond to breaches through state‐of‐the‐art training. There are indications that pioneering training programs like serious games (SGs), including tabletop exercises (TTXs), can play a role in CSIR training. This paper reviewed TTX related SGs literature, analyzed existing CSIR training exercises and reported how TTXs are currently being used in CSIR training. It also discussed why TTXs are increasingly becoming a popular tool for CSIR and emergency response (ER) training, analyzed the strengths and weaknesses of the current research and identified areas for future research. The findings suggest that TTX training improves the awareness, understanding, and preparation levels of CSIRTs. That TTXs enhance their strategic decision‐making, enabling CSIRTs to be better prepared when dealing with security incidents. It observed that TTX related training improved the skills and aptitudes of CSIRTs and security operative center personnel. TTXs assist trainees to acquire and demonstrate both technical and nontechnical skills, including soft skills which are essential but often observed to be lacking in new graduates and some experienced technically minded personnel. TTX training augments traditional methods like classroom lectures by providing opportunities for experiential learning and practice‐based approaches in dealing with real‐life problems.

This paper presents OntologyFixer, a web-based tool that supports a methodology to build, assess, and improve the quality of ontology web language (OWL) ontologies. Using our software, knowledge engineers are able to fix low-quality OWL ontologies (such as those created from natural language documents using ontology learning processes). The fixing process is guided by a set of metrics and fixing mechanisms provided by the tool, and executed primarily through automated changes (inspired by quick fix actions used in the software engineering domain). To evaluate the quality, the tool supports numerical and graphical quality assessments, focusing on ontology content and structure attributes. This tool follows principles, and provides features, typical of scientific software, including user parameter requests, logging, multithreading execution, and experiment repeatability, among others. OntologyFixer architecture takes advantage of model view controller (MVC), strategy, template, and factory design patterns; and decouples graphical user interfaces (GUI) from ontology quality metrics, ontology fixing, and REST (REpresentational State Transfer) API (Application Programming Interface) components (used for pitfall identification, and ontology evaluation). We also separate part of the OntologyFixer functionality into a new package called OntoMetrics, which focuses on the identification of symptoms and the evaluation of the quality of ontologies. Finally, OntologyFixer provides mechanisms to easily develop and integrate new quick fix methods.

Background: Health information systems (HISs) are continuously targeted by hackers, who aim to bring down critical health infrastructure. This study was motivated by recent attacks on health care organizations that have resulted in the compromise of sensitive data held in HISs. Existing research on cybersecurity in the health care domain places an imbalanced focus on protecting medical devices and data. There is a lack of a systematic way to investigate how attackers may breach an HIS and access health care records. Objective: This study aimed to provide new insights into HIS cybersecurity protection. We propose a systematic, novel, and optimized (artificial intelligence–based) ethical hacking method tailored specifically for HISs, and we compared it with the traditional unoptimized ethical hacking method. This allows researchers and practitioners to identify the points and attack pathways of possible penetration attacks on the HIS more efficiently. Methods: In this study, we propose a novel methodological approach to ethical hacking in HISs. We implemented ethical hacking using both optimized and unoptimized methods in an experimental setting. Specifically, we set up an HIS simulation environment by implementing the open-source electronic medical record (OpenEMR) system and followed the National Institute of Standards and Technology’s ethical hacking framework to launch the attacks. In the experiment, we launched 50 rounds of attacks using both unoptimized and optimized ethical hacking methods. Results: Ethical hacking was successfully conducted using both optimized and unoptimized methods. The results show that the optimized ethical hacking method outperforms the unoptimized method in terms of average time used, the average success rate of exploit, the number of exploits launched, and the number of successful exploits. We were able to identify the successful attack paths and exploits that are related to remote code execution, cross-site request forgery, improper authentication, vulnerability in the Oracle Business Intelligence Publisher, an elevation of privilege vulnerability (in MediaTek), and remote access backdoor (in the web graphical user interface for the Linux Virtual Server). Conclusions: This research demonstrates systematic ethical hacking against an HIS using optimized and unoptimized methods, together with a set of penetration testing tools to identify exploits and combining them to perform ethical hacking. The findings contribute to the HIS literature, ethical hacking methodology, and mainstream artificial intelligence–based ethical hacking methods because they address some key weaknesses of these research fields. These findings also have great significance for the health care sector, as OpenEMR is widely adopted by health care organizations. Our findings offer novel insights for the protection of HISs and allow researchers to conduct further research in the HIS cybersecurity domain.