Background: Cybersecurity is increasingly becoming a prominent concern among healthcare providers in adopting digital technologies for improving the quality of care delivered to patients. The recent reports on cyber attacks, such as ransomware and WannaCry, have brought to life the destructive nature of such attacks upon healthcare. In complement to cyberattacks, which have been targeted against the vulnerabilities of information technology (IT) infrastructures, a new form of cyber attack aims to exploit human vulnerabilities; such attacks are categorised as social engineering attacks. Following an increase in the frequency and ingenuity of attacks launched against hospitals and clinical environments with the intention of causing service disruption, there is a strong need to study the level of awareness programmes and training activities offered to the staff by healthcare organisations. Objective: The objective of this systematic review is to identify commonly encountered factors that cybersecurity postures of a healthcare organisation, resulting from the ignorance of cyber threat to healthcare. The systematic review aims to consolidate the current literature being reported upon human behaviour resulting in security gaps that mitigate the cyber defence strategy adopted by healthcare organisations. Additionally, the paper also reviews the organisational risk assessment methodology implemented and the policies being adopted to strengthen cybersecurity. Methods: The topic of cybersecurity within healthcare and the clinical environment has attracted the interest of several researchers, resulting in a broad range of literature. The inclusion criteria for the articles in the review stem from the scope of the five research questions identified. To this end, we conducted seven search queries across three repositories, namely (i) PubMed^®/MED-LINE; (ii) Cumulative Index to Nursing and Allied Health Literature (CINAHL); and (iii) Web of Science (WoS), using key words related to cybersecurity awareness, training, organisation risk assessment methodologies, policies and recommendations adopted as counter measures within health care. These were restricted to around the last 12 years. Results: A total of 70 articles were selected to be included in the review, which addresses the complexity of cybersecurity measures adopted within the healthcare and clinical environments. The articles included in the review highlight the evolving nature of cybersecurity threats stemming from exploiting IT infrastructures to more advanced attacks launched with the intent of exploiting human vulnerability. A steady increase in the literature on the threat of phishing attacks evidences the growing threat of social engineering attacks. As a countermeasure, through the review, we identified articles that provide methodologies resulting from case studies to promote cybersecurity awareness among stakeholders. The articles included highlight the need to adopt cyber hygiene practices among healthcare professionals while accessing social media platforms, which forms an ideal test bed for the attackers to gain insight into the life of healthcare professionals. Additionally, the review also includes articles that present strategies adopted by healthcare organisations in countering the impact of social engineering attacks. The evaluation of the cybersecurity risk assessment of an organisation is another key area of study reported in the literature that recommends the organisation of European and international standards in countering social engineering attacks. Lastly, the review includes articles reporting on national case studies with an overview of the economic and societal impact of service disruptions encountered due to cyberattacks. Discussion: One of the limitations of the review is the subjective ranking of the authors associated to the relevance of literature to each of the research questions identified. We also acknowledge the limited amount of literature that focuses on human factors of cybersecurity in health care in general; therefore, the search queries were formulated using well-established cybersecurity related topics categorised according to the threats, risk assessment and organisational strategies reported in the literature.

When investing in cyber security resources, information security managers have to follow effective decision-making strategies. We refer to this as the cyber security investment challenge.In this paper, we consider three possible decision support methodologies for security managers to tackle this challenge. We consider methods based on game theory, combinatorial optimisation, and a hybrid of the two. Our modelling starts by building a framework where we can investigate the effectiveness of a cyber security control regarding the protection of different assets seen as targets in presence of commodity threats. As game theory captures the interaction between the endogenous organisation's and attackers' decisions, we consider a 2-person control game between the security manager who has to choose among different implementation levels of a cyber security control, and a commodity attacker who chooses among different targets to attack. The pure game theoretical methodology consists of a large game including all controls and all threats. In the hybrid methodology the game solutions of individual control-games along with their direct costs (e.g. financial) are combined with a Knapsack algorithm to derive an optimal investment strategy. The combinatorial optimisation technique consists of a multi-objective multiple choice Knapsack based strategy. To compare these approaches we built a decision support tool and a case study regarding current government guidelines. The endeavour of this work is to highlight the weaknesses and strengths of different investment methodologies for cyber security, the benefit of their interaction, and the impact that indirect costs have on cyber security investment. Going a step further in validating our work, we have shown that our decision support tool provides the same advice with the one advocated by the UK government with regard to the requirements for basic technical protection from cyber attacks in SMEs

The modern Internet of Things (IoT)-based smart home is a challenging environment to secure: devices change, new vulnerabilities are discovered and often remain unpatched, and different users interact with their devices differently and have different cyber risk attitudes. A security breach’s impact is not limited to cyberspace, as it can also affect or be facilitated in physical space, for example, via voice. In this environment, intrusion detection cannot rely solely on static models that remain the same over time and are the same for all users. We present MAGPIE, the first smart home intrusion detection system that is able to autonomously adjust the decision function of its underlying anomaly classification models to a smart home’s changing conditions (e.g., new devices, new automation rules and user interaction with them). The method achieves this goal by applying a novel probabilistic cluster-based reward mechanism to non-stationary multi-armed bandit reinforcement learning. MAGPIE rewards the sets of hyperparameters of its underlying isolation forest unsupervised anomaly classifiers based on the cluster silhouette scores of their output. Experimental evaluation in a real household shows that MAGPIE exhibits high accuracy because of two further innovations: it takes into account both cyber and physical sources of data; and it detects human presence to utilise models that exhibit the highest accuracy in each case. MAGPIE is available in open-source format, together with its evaluation datasets, so it can benefit from future advances in unsupervised and reinforcement learning and be able to be enriched with further sources of data as smart home environments and attacks evolve.

The advent of the Smart Grid (SG) raises severe cybersecurity risks that can lead to devastating consequences. In this paper, we present a novel anomaly-based Intrusion Detection System (IDS), called ARIES (smArt gRid Intrusion dEtection System), which is capable of protecting efficiently SG communications. ARIES combines three detection layers that are devoted to recognising possible cyberattacks and anomalies against (a) network flows, (b) Modbus/Transmission Control Protocol (TCP) packets and (c) operational data. Each detection layer relies on a Machine Learning (ML) model trained using data originating from a power plant. In particular, the first layer (network flow-based detection) performs a supervised multiclass classification, recognising Denial of Service (DoS), brute force attacks, port scanning attacks and bots. The second layer (packet-based detection) detects possible anomalies related to the Modbus packets, while the third layer (operational data based detection) monitors and identifies anomalies upon operational data (i.e., time series electricity measurements). By emphasising on the third layer, the ARIES Generative Adversarial Network (ARIES GAN) with novel error minimisation functions was developed, considering mainly the reconstruction difference. Moreover, a novel reformed conditional input was suggested, consisting of random noise and the signal features at any given time instance. Based on the evaluation analysis, the proposed GAN network overcomes the efficacy of conventional ML methods in terms of Accuracy and the F1 score.