A Data Taxonomy for Adaptive Multifactor Authentication in the Internet of Health Care Things.

The health care industry has faced various challenges over the past decade as we move toward a digital future where services and data are available on demand. The systems of interconnected devices, users, data, and working environments are referred to as the Internet of Health Care Things (IoHT). IoHT devices have emerged in the past decade as cost-effective solutions with large scalability capabilities to address the constraints on limited resources. These devices cater to the need for remote health care services outside of physical interactions. However, IoHT security is often overlooked because the devices are quickly deployed and configured as solutions to meet the demands of a heavily saturated industry. During the COVID-19 pandemic, studies have shown that cybercriminals are exploiting the health care industry, and data breaches are targeting user credentials through authentication vulnerabilities. Poor password use and management and the lack of multifactor authentication security posture within IoHT cause a loss of millions according to the IBM reports. Therefore, it is important that health care authentication security moves toward adaptive multifactor authentication (AMFA) to replace the traditional approaches to authentication. We identified a lack of taxonomy for data models that particularly focus on IoHT data architecture to improve the feasibility of AMFA. This viewpoint focuses on identifying key cybersecurity challenges in a theoretical framework for a data model that summarizes the main components of IoHT data. The data are to be used in modalities that are suited for health care users in modern IoHT environments and in response to the COVID-19 pandemic. To establish the data taxonomy, a review of recent IoHT papers was conducted to discuss the related work in IoHT data management and use in next-generation authentication systems. Reports, journal articles, conferences, and white papers were reviewed for IoHT authentication data technologies in relation to the problem statement of remote authentication and user management systems. Only publications written in English from the last decade were included (2012-2022) to identify key issues within the current health care practices and their management of IoHT devices. We discuss the components of the IoHT architecture from the perspective of data management and sensitivity to ensure privacy for all users. The data model addresses the security requirements of IoHT users, environments, and devices toward the automation of AMFA in health care. We found that in health care authentication, the significant threats occurring were related to data breaches owing to weak security options and poor user configuration of IoHT devices. The security requirements of IoHT data architecture and identified impactful methods of cybersecurity for health care devices, data, and their respective attacks are discussed. Data taxonomy provides better understanding, solutions, and improvements of user authentication in remote working environments for security features.

Biometric Security: A Novel Ear Recognition Approach Using a 3D Morphable Ear Model.

Biometrics is a critical component of cybersecurity that identifies persons by verifying their behavioral and physical traits. In biometric-based authentication, each individual can be correctly recognized based on their intrinsic behavioral or physical features, such as face, fingerprint, iris, and ears. This work proposes a novel approach for human identification using 3D ear images. Usually, in conventional methods, the probe image is registered with each gallery image using computational heavy registration algorithms, making it practically infeasible due to the time-consuming recognition process. Therefore, this work proposes a recognition pipeline that reduces the one-to-one registration between probe and gallery. First, a deep learning-based algorithm is used for ear detection in 3D side face images. Second, a statistical ear model known as a 3D morphable ear model (3DMEM), was constructed to use as a feature extractor from the detected ear images. Finally, a novel recognition algorithm named you morph once (YMO) is proposed for human recognition that reduces the computational time by eliminating one-to-one registration between probe and gallery, which only calculates the distance between the parameters stored in the gallery and the probe. The experimental results show the significance of the proposed method for a real-time application.

Infrequent Pattern Detection for Reliable Network Traffic Analysis Using Robust Evolutionary Computation.

While anomaly detection is very important in many domains, such as in cybersecurity, there are many rare anomalies or infrequent patterns in cybersecurity datasets. Detection of infrequent patterns is computationally expensive. Cybersecurity datasets consist of many features, mostly irrelevant, resulting in lower classification performance by machine learning algorithms. Hence, a feature selection (FS) approach, i.e., selecting relevant features only, is an essential preprocessing step in cybersecurity data analysis. Despite many FS approaches proposed in the literature, cooperative co-evolution (CC)-based FS approaches can be more suitable for cybersecurity data preprocessing considering the Big Data scenario. Accordingly, in this paper, we have applied our previously proposed CC-based FS with random feature grouping (CCFSRFG) to a benchmark cybersecurity dataset as the preprocessing step. The dataset with original features and the dataset with a reduced number of features were used for infrequent pattern detection. Experimental analysis was performed and evaluated using 10 unsupervised anomaly detection techniques. Therefore, the proposed infrequent pattern detection is termed Unsupervised Infrequent Pattern Detection (UIPD). Then, we compared the experimental results with and without FS in terms of true positive rate (TPR). Experimental analysis indicates that the highest rate of TPR improvement was by cluster-based local outlier factor (CBLOF) of the backdoor infrequent pattern detection, and it was 385.91% when using FS. Furthermore, the highest overall infrequent pattern detection TPR was improved by 61.47% for all infrequent patterns using clustering-based multivariate Gaussian outlier score (CMGOS) with FS.

Biometrics for Internet-of-Things Security: A Review.

The large number of Internet-of-Things (IoT) devices that need interaction between smart devices and consumers makes security critical to an IoT environment. Biometrics offers an interesting window of opportunity to improve the usability and security of IoT and can play a significant role in securing a wide range of emerging IoT devices to address security challenges. The purpose of this review is to provide a comprehensive survey on the current biometrics research in IoT security, especially focusing on two important aspects, authentication and encryption. Regarding authentication, contemporary biometric-based authentication systems for IoT are discussed and classified based on different biometric traits and the number of biometric traits employed in the system. As for encryption, biometric-cryptographic systems, which integrate biometrics with cryptography and take advantage of both to provide enhanced security for IoT, are thoroughly reviewed and discussed. Moreover, challenges arising from applying biometrics to IoT and potential solutions are identified and analyzed. With an insight into the state-of-the-art research in biometrics for IoT security, this review paper helps advance the study in the field and assists researchers in gaining a good understanding of forward-looking issues and future research directions.

A Blockchain-Based Authentication Protocol for Cooperative Vehicular Ad Hoc Network.

The efficiency of cooperative communication protocols to increase the reliability and range of transmission for Vehicular Ad hoc Network (VANET) is proven, but identity verification and communication security are required to be ensured. Though it is difficult to maintain strong network connections between vehicles because of there high mobility, with the help of cooperative communication, it is possible to increase the communication efficiency, minimise delay, packet loss, and Packet Dropping Rate (PDR). However, cooperating with unknown or unauthorized vehicles could result in information theft, privacy leakage, vulnerable to different security attacks, etc. In this paper, a blockchain based secure and privacy preserving authentication protocol is proposed for the Internet of Vehicles (IoV). Blockchain is utilized to store and manage the authentication information in a distributed and decentralized environment and developed on the Ethereum platform that uses a digital signature algorithm to ensure confidentiality, non-repudiation, integrity, and preserving the privacy of the IoVs. For optimized communication, transmitted services are categorized into emergency and optional services. Similarly, to optimize the performance of the authentication process, IoVs are categorized as emergency and general IoVs. The proposed cooperative protocol is validated by numerical analyses which show that the protocol successfully increases the system throughput and decreases PDR and delay. On the other hand, the authentication protocol requires minimum storage as well as generates low computational overhead that is suitable for the IoVs with limited computer resources.

Evaluating Staff Attitudes, Intentions, and Behaviors Related to Cyber Security in Large Australian Health Care Environments: Mixed Methods Study.

BACKGROUND: Previous studies have identified that the effective management of cyber security in large health care environments is likely to be significantly impacted by human and social factors, as well as by technical controls. However, there have been limited attempts to confirm this by using measured and integrated studies to identify specific user motivations and behaviors that can be managed to achieve improved outcomes. OBJECTIVE: This study aims to document and analyze survey and interview data from a diverse range of health care staff members, to determine the primary motivations and behaviors that influence their acceptance and application of cyber security messaging and controls. By identifying these issues, recommendations can be made to positively influence future cyber security governance in health care. METHODS: An explanatory sequential mixed methods approach was undertaken to analyze quantitative data from a web-based staff survey (N=103), with a concurrent qualitative investigation applied to data gathered via in-depth staff interviews (N=9). Data from both stages of this methodology were mapped to descriptive variables based on a modified version of the Technology Acceptance Model (TAM; TAM2). After normalization, the quantitative data were verified and analyzed using descriptive statistics, distribution and linearity measures, and a bivariate correlation of the TAM variables to identify the Pearson coefficient (r) and significance (P) values. Finally, after confirming Cronbach α, the determinant score for multicollinearity, and the Kaiser-Meyer-Olkin measure, and applying the Bartlett test of sphericity (χ2), an exploratory factor analysis (EFA) was conducted to identify the primary factors with an eigenvalue (λ) >1.0. Comments captured during the qualitative interviews were coded using NVivo software (QSR International) to create an emic-to-etic understanding, which was subsequently integrated with the quantitative results to produce verified conclusions. RESULTS: Using the explanatory sequential methodology, this study showed that the perceived usefulness of security controls emerged as the most significant factor influencing staff beliefs and behaviors. This variable represented 24% of all the variances measured in the EFA and was also the most common category identified across all coded interviews (281/692, 40.6%). The word frequency analysis showed that systems, patients, and people represented the top 3 recurring themes reported by the interviewees. CONCLUSIONS: To improve cyber security governance in large health care environments, efforts should be focused on demonstrating how confidentiality, integrity, availability, policies, and cloud or vendor-based controls (the main contributors of usefulness measured by the EFA) can directly improve outcomes for systems, staff, and patients. Further consideration also needs to be given to how clinicians should share data and collaborate on patient care, with tools and processes provided to support and manage data sharing securely and to achieve a consistent baseline of secure and normalized behaviors.

CYBER-AIDD: A novel approach to implementing improved cyber security resilience for large Australian healthcare providers using a Unified Modelling Language ontology.

Purpose: This paper proposes a novel cyber security risk governance framework and ontology for large Australian healthcare providers, using the structure and simplicity of the Unified Modelling Language (UML). This framework is intended to mitigate impacts from the risk areas of: (1) cyber-attacks, (2) incidents, (3) data breaches, and (4) data disclosures. Methods: Using a mixed-methods approach comprised of empirical evidence discovery and phenomenological review, existing literature is sourced to confirm baseline ontological definitions. These are supplemented with Australian government reports, professional standards publications and legislation covering cyber security, data breach reporting and healthcare governance. Historical examples of healthcare cyber security incidents are reviewed, and a cyber risk governance UML presented to manage the defined problem areas via a single, simplified ontological diagram. Results: A clear definition of 'cyber security' is generated, along with the 'CYBER-AIDD' risk model. Specific examples of cyber security incidents impacting Australian healthcare are confirmed as N = 929 over 5 years, with human factors the largest contributor. The CYBER-AIDD UML model presents a workflow across four defined classes, providing a clear approach to implementing the controls required to mitigate risks against verified threats. Conclusions: The governance of cyber security in healthcare is complex, in part due to a lack of clarity around key terms and risks, and this is contributing to consistently poor operational outcomes. A focus on the most essential avenues of risk, using a simple UML model, is beneficial in describing these risks and designing governance controls around them.

A review of multi-factor authentication in the Internet of Healthcare Things.

Objective: This review paper aims to evaluate existing solutions in healthcare authentication and provides an insight into the technologies incorporated in Internet of Healthcare Things (IoHT) and multi-factor authentication (MFA) applications for next-generation authentication practices. Our review has two objectives: (a) Review MFA based on the challenges, impact and solutions discussed in the literature; and (b) define the security requirements of the IoHT as an approach to adapting MFA solutions in a healthcare context. Methods: To review the existing literature, we indexed articles from the IEEE Xplore, ACM Digital Library, ScienceDirect, and SpringerLink databases. The search was refined to combinations of 'authentication', 'multi-factor authentication', 'Internet of Things authentication', and 'medical authentication' to ensure that the retrieved journal articles and conference papers were relevant to healthcare and Internet of Things-oriented authentication research. Results: The concepts of MFA can be applied to healthcare where security can often be overlooked. The security requirements identified result in stronger methodologies of authentication such as hardware solutions in combination with biometric data to enhance MFA approaches. We identify the key vulnerabilities of weaker approaches to security such as password use against various cyber threats. Cyber threats and MFA solutions are categorised in this paper to facilitate readers' understanding of them in healthcare domains. Conclusions: We contribute to an understanding of up-to-date MFA approaches and how they can be improved for use in the IoHT. This is achieved by discussing the challenges, benefits, and limitations of current methodologies and recommendations to improve access to eHealth resources through additional layers of security.

Leveraging machine learning to analyze sentiment from COVID-19 tweets: A global perspective.

Since the advent of the worldwide COVID-19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision-makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using different state-of-the-art technologies has been focused on during the COVID-19 pandemic, the reasons behind the variations in public sentiment are yet to be explored. Moreover, how user sentiment varies due to the COVID-19 pandemic from a cross-country perspective has been less focused on. Therefore, the objectives of this study are: to identify the most effective machine learning (ML) technique for classifying public sentiments, to analyze the variations of public sentiment across the globe, and to find the critical contributing factors to sentiment variations. To attain the objectives, 12,000 tweets, 3000 each from the USA, UK, and Bangladesh, were rigorously annotated by three independent reviewers. Based on the labeled tweets, four different boosting ML models, namely, CatBoost, gradient boost, AdaBoost, and XGBoost, are investigated. Next, the top performed ML model predicted sentiment of 300,000 data (100,000 from each country). The public perceptions have been analyzed based on the labeled data. As an outcome, the CatBoost model showed the highest (85.8%) F1-score, followed by gradient boost (84.3%), AdaBoost (78.9%), and XGBoost (83.1%). Second, it was revealed that during the time of the COVID-19 pandemic, the sentiments of the people of the three countries mainly were negative, followed by positive and neutral. Finally, this study identified a few critical concerns that impact primarily varying public sentiment around the globe: lockdown, quarantine, hospital, mask, vaccine, and the like.

Health hazards related to using masks and/or personal protective equipment among physicians working in public hospitals in Dhaka: A cross-sectional study.

BACKGROUND: Wearing masks or personal protective equipment (PPE) has become an integral part of the occupational life of physicians due to the coronavirus disease 2019 (COVID-19) pandemic. Most physicians have been developing various health hazards related to the use of different protective gears. This study aimed to determine the burden and spectrum of various health hazards associated with using masks or PPE and their associated risk factors. METHODS: This cross-sectional survey was conducted in Dhaka Medical College from March 01-May 30, 2021, among physicians from different public hospitals in Dhaka, Bangladesh. We analyzed the responses of 506 physicians who completed case record forms through Google forms or hard copies. FINDINGS: The mean (SD) age of the respondents was 35.4 [7.7], and 69.4% were men. Approximately 40% were using full PPE, and 55% were using N-95 masks. A total of 489 (96.6%) patients experienced at least one health hazard. The reported severe health hazards were syncope, severe dyspnea, severe chest pain, and anaphylaxis. Headache, dizziness, mood irritation, chest pain, excessive sweating, panic attack, and permanent facial disfigurement were the minor health hazards reported. Extended periods of work in the COVID-19-unit, reuse of masks, diabetes, obesity, and mental stress were risk factors for dyspnea. The risk factors for headaches were female sex, diabetes, and previous primary headaches. Furthermore, female sex and reusing masks for an extended period (> 6 h) were risk factors for facial disfigurement. The risk factors for excessive sweating were female sex and additional evening office practice for an extended period. CONCLUSIONS: Healthcare workers experienced several occupational hazards after using masks and PPE. Therefore, an appropriate policy is required to reduce such risks.

Severe acute myopathy following SARS-CoV-2 infection: a case report and review of recent literature.

BACKGROUND: SARS-CoV2 virus could be potentially myopathic. Serum creatinine phosphokinase (CPK) is frequently found elevated in severe SARS-CoV2 infection, which indicates skeletal muscle damage precipitating limb weakness or even ventilatory failure. CASE PRESENTATION: We addressed such a patient in his forties presented with features of severe SARS-CoV2 pneumonia and high serum CPK. He developed severe sepsis and acute respiratory distress syndrome (ARDS) and received intravenous high dose corticosteroid and tocilizumab to counter SARS-CoV2 associated cytokine surge. After 10 days of mechanical ventilation (MV), weaning was unsuccessful albeit apparently clear lung fields, having additionally severe and symmetric limb muscle weakness. Ancillary investigations in addition with serum CPK, including electromyogram, muscle biopsy, and muscle magnetic resonance imaging (MRI) suggested acute myopathy possibly due to skeletal myositis. CONCLUSION: We wish to stress that myopathogenic medication in SARS-CoV2 pneumonia should be used with caution. Additionally, serum CPK could be a potential marker to predict respiratory failure in SARS-CoV2 pneumonia as skeletal myopathy affecting chest muscles may contribute ventilatory failure on top of oxygenation failure due to SARS-CoV2 pneumonia.

Rationale for emphasis on management over treatment of schizophrenia in clinical practice.

There is a need for clinicians to emphasize "management" over "treatment" in their pharmacologic approach to schizophrenia. Using the term "treatment" may set up unrealistic expectations for a fuller recovery to a premorbid level of functioning, with potential use of overmedication and polypharmacy as well as possible unwanted adverse effects. The "management" concept allows clinicians to work with the best current medication practice, taking into account the wide variability in the course of illness and response to treatment. Prescribing clinicians should work collaboratively with other caregivers to enhance adaptive behavior and functional outcome through psychosocial rehabilitative interventions, within the context of recovery.

Exploring unique roles for psychologists.

Presents a comment on "Psychological Treatments" by D. H. Barlow. Barlow highlighted unique roles that psychologists can play in mental health service delivery by providing psychological treatments--treatments that psychologists would be uniquely qualified to design and deliver. In support of Barlow's position, the authors draw from their own clinical practice with special psychiatric populations, such as adults with severe and persistent mental illness and behaviorally disordered youths, to illustrate some potential unique roles for psychologists. The authors believe psychologists are uniquely trained to design such individualized functional behavioral analysis protocols because of their training in research design, behavior analysis, learning theory, and behavior change. Psychologists may also be uniquely qualified to design, implement, and evaluate many specialized therapy techniques, as Barlow has outlined and suggested.