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Abstract Reinforcement of the Internet of Medical Things (IoMT) network security has become extremely significant as these networks enable both patients and healthcare providers to communicate with each other by exchanging medical signals, data, and vital reports in a safe way. To ensure the safe transmission of sensitive information, robust and secure access mechanisms are paramount. Vulnerabilities in these networks, particularly at the access points, could expose patients to significant risks. Among the possible security measures, biometric authentication is becoming a more feasible choice, with a focus on leveraging regularly-monitored biomedical signals like Electrocardiogram (ECG) signals due to their unique characteristics. A notable challenge within all biometric authentication systems is the risk of losing original biometric traits, if hackers successfully compromise the biometric template storage space. Current research endorses replacement of the original biometrics used in access control with cancellable templates. These are produced using encryption or non-invertible transformation, which improves security by enabling the biometric templates to be changed in case an unwanted access is detected. This study presents a comprehensive framework for ECG-based recognition with cancellable templates. This framework may be used for accessing IoMT networks. An innovative methodology is introduced through non-invertible modification of ECG signals using blind signal separation and lightweight encryption. The basic idea here depends on the assumption that if the ECG signal and an auxiliary audio signal for the same person are subjected to a separation algorithm, the algorithm will yield two uncorrelated components through the minimization of a correlation cost function. Hence, the obtained outputs from the separation algorithm will be distorted versions of the ECG as well as the audio signals. The distorted versions of the ECG signals can be treated with a lightweight encryption stage and used as cancellable templates. Security enhancement is achieved through the utilization of the lightweight encryption stage based on a user-specific pattern and XOR operation, thereby reducing the processing burden associated with conventional encryption methods. The proposed framework efficacy is demonstrated through its application on the ECG-ID and MIT-BIH datasets, yielding promising results. The experimental evaluation reveals an Equal Error Rate (EER) of 0.134 on the ECG-ID dataset and 0.4 on the MIT-BIH dataset, alongside an exceptionally large Area under the Receiver Operating Characteristic curve (AROC) of 99.96% for both datasets. These results underscore the framework potential in securing IoMT networks through cancellable biometrics, offering a hybrid security model that combines the strengths of non-invertible transformations and lightweight encryption.

Katerina Svandova,* Zdenek Smutny* Faculty of Informatics and Statistics, Prague University of Economics and Business, Prague, Czech Republic*These authors contributed equally to this workCorrespondence: Zdenek Smutny, Faculty of Informatics and Statistics, Prague University of Economics and Business, W. Churchill Sq. 1938/4, 130 67 Prague 3, Prague, Czech Republic, Email zdenek.smutny@vse.czBackground: The massive expansion of the Internet of medical things (IoMT) technology brings many opportunities for improving healthcare. At the same time, their use increases security risks, brings security and privacy concerns, and threatens the functioning of healthcare facilities or healthcare provision.Purpose: This scoping review aims to identify progress in designing risk assessment and management frameworks for IoMT security. The frameworks found are divided into two groups according to whether frameworks address the technological design of risk management or assess technological measures to ensure the security of the IoMT environment. Furthermore, the article intends to find out whether frameworks also include an assessment of organisational measures related to IoMT security.Methods: This review was prepared using PRISMA ScR guidelines. Relevant studies were searched in the citation databases Web of Science and Scopus. The search was limited to articles published in English between 2018 and 17 September 2023. The initial search yielded 1341 articles, of which 44 (3.3%) were included in the scoping review. A qualitative content analysis focused on selected security perspectives and progress in the given area was carried out.Results: Thirty-two articles describe the design of risk assessment and management frameworks. Twelve articles describe the design of frameworks for assessing the security of IoMT devices and possibly offer a comparison of different IoMT alternatives. A description of the included articles was prepared from the selected security perspectives.Conclusion: The review shows the need to create comprehensive or holistic frameworks for operational security and privacy risk management at all layers of the IoMT architecture. It includes the design of specific technological solutions and frameworks for continuously assessing the overall level of information security and privacy of the IoMT environment. Unfortunately, none of the found frameworks offer an assessment of organizational measures even though the importance of the organization measures was highlighted in articles. Another area of interest for researchers could be the design of a general risk management database for IoMT, which would include potential IoMT-related risks connected to a particular device.Keywords: cybersecurity, healthcare, information systems, IoMT, internet of things, IoT, threat, sensors

Abstract The management of medications is a crucial component of healthcare, and pharmaceutical errors can have detrimental effects on patients, healthcare professionals, and healthcare systems. By utilizing patient-specific data and cutting-edge technology like the Internet of Medical Things (IoMT) and machine learning, customized drug management systems have the potential to increase patient safety and healthcare effectiveness. In this study, we reviewed a large body of literature on the subject of medication management in healthcare and the potential advantages of personalized medication management. We then assessed how IoMT and machine learning might be used to enhance medication management in smart hospitals. Then, we created a framework for assessing how personalized medication management utilizing IoMT and machine learning affects patient safety and healthcare effectiveness. Our study's findings demonstrate that in smart hospitals, tailored medication management with IoMT and machine learning can drastically lower medication errors while also enhancing patient safety and healthcare effectiveness. Our findings have important ramifications for the future of medication administration in smart hospitals, and we advise healthcare professionals and policymakers to give priority to integrating cutting-edge technology like IoMT and machine learning for customized medication management.

Internet of Medical Things (IoMT) can be leveraged for periodic sensing and recording of different health parameters using sensors, wireless communications, and computation platforms. Health care systems can be enhanced by using IoMT for remote patient monitoring and data-driven diagnosis powered by machine learning algorithms. In the context of IoMT, federated learning (FL) is an excellent choice to manage machine learning (ML) algorithms to drive this analysis. This is because FL models can be trained in a distributed manner on local heterogeneous datasets that all contribute to the "collective wisdom". The model parameters can be regulated and shared without sharing the actual health data, ensuring confidentiality and security. This paper makes a case for the viability of FL-based analysis of data acquired via IoMT by presenting some use cases and recent work in this area and proposing a novel framework for data analysis using FL specifically in the context of mental stress detection. It shows that FL-based methods can significantly reduce the required communication overhead for each local device from 10.02MB/day up to only 754B/day as compared to non-FL techniques. [ABSTRACT FROM AUTHOR]

The rapidly expanding Internet of Medical Things (IoMT) landscape fosters enormous opportunities for personalized healthcare, yet it also exposes patients and healthcare systems to diverse security threats. Heterogeneous IoMT devices present challenges that need comprehensive risk assessment due to their varying functionality, protocols, and vulnerabilities. Hence, to achieve the goal of having risk-free IoMT devices, the authors used a hybrid approach using fuzzy logic and the Fuzzy Analytical Hierarchy Process (FAHP) to evaluate risks, providing effective and useful results for developers and researchers. The presented approach specifies qualitative descriptors such as the frequency of occurrence, consequence severity, weight factor, and risk level. A case study with risk events in three different IoMT devices was carried out to illustrate the proposed method. We performed a Bluetooth Low Energy (BLE) attack on an oximeter, smartwatch, and smart peak flow meter to discover their vulnerabilities. Using the FAHP method, we calculated fuzzy weights and risk levels, which helped us to prioritize criteria and alternatives in decision-making. Smartwatches were found to have a risk level of 8.57 for injection attacks, which is of extreme importance and needs immediate attention. Conversely, jamming attacks registered the lowest risk level of 1, with 9 being the maximum risk level and 1 the minimum. Based on this risk assessment, appropriate security measures can be implemented to address the severity of potential threats. The findings will assist healthcare industry decision-makers in evaluating the relative importance of risk factors, aiding informed decisions through weight comparison. [ABSTRACT FROM AUTHOR]

Reinforcement of the Internet of Medical Things (IoMT) network security has become extremely significant as these networks enable both patients and healthcare providers to communicate with each other by exchanging medical signals, data, and vital reports in a safe way. To ensure the safe transmission of sensitive information, robust and secure access mechanisms are paramount. Vulnerabilities in these networks, particularly at the access points, could expose patients to significant risks. Among the possible security measures, biometric authentication is becoming a more feasible choice, with a focus on leveraging regularly-monitored biomedical signals like Electrocardiogram (ECG) signals due to their unique characteristics. A notable challenge within all biometric authentication systems is the risk of losing original biometric traits, if hackers successfully compromise the biometric template storage space. Current research endorses replacement of the original biometrics used in access control with cancellable templates. These are produced using encryption or non-invertible transformation, which improves security by enabling the biometric templates to be changed in case an unwanted access is detected. This study presents a comprehensive framework for ECG-based recognition with cancellable templates. This framework may be used for accessing IoMT networks. An innovative methodology is introduced through non-invertible modification of ECG signals using blind signal separation and lightweight encryption. The basic idea here depends on the assumption that if the ECG signal and an auxiliary audio signal for the same person are subjected to a separation algorithm, the algorithm will yield two uncorrelated components through the minimization of a correlation cost function. Hence, the obtained outputs from the separation algorithm will be distorted versions of the ECG as well as the audio signals. The distorted versions of the ECG signals can be treated with a lightweight encryption stage and used as cancellable templates. Security enhancement is achieved through the utilization of the lightweight encryption stage based on a user-specific pattern and XOR operation, thereby reducing the processing burden associated with conventional encryption methods. The proposed framework efficacy is demonstrated through its application on the ECG-ID and MIT-BIH datasets, yielding promising results. The experimental evaluation reveals an Equal Error Rate (EER) of 0.134 on the ECG-ID dataset and 0.4 on the MIT-BIH dataset, alongside an exceptionally large Area under the Receiver Operating Characteristic curve (AROC) of 99.96% for both datasets. These results underscore the framework potential in securing IoMT networks through cancellable biometrics, offering a hybrid security model that combines the strengths of non-invertible transformations and lightweight encryption. [ABSTRACT FROM AUTHOR]

The management of medications is a crucial component of healthcare, and pharmaceutical errors can have detrimental effects on patients, healthcare professionals, and healthcare systems. By utilizing patient-specific data and cutting-edge technology like the Internet of Medical Things (IoMT) and machine learning, customized drug management systems have the potential to increase patient safety and healthcare effectiveness. In this study, we reviewed a large body of literature on the subject of medication management in healthcare and the potential advantages of personalized medication management. We then assessed how IoMT and machine learning might be used to enhance medication management in smart hospitals. Then, we created a framework for assessing how personalized medication management utilizing IoMT and machine learning affects patient safety and healthcare effectiveness. Our study's findings demonstrate that in smart hospitals, tailored medication management with IoMT and machine learning can drastically lower medication errors while also enhancing patient safety and healthcare effectiveness. Our findings have important ramifications for the future of medication administration in smart hospitals, and we advise healthcare professionals and policymakers to give priority to integrating cutting-edge technology like IoMT and machine learning for customized medication management. [ABSTRACT FROM AUTHOR]

The medical cyber-physical system utilizes various Internet of Medical Things (IoMT) devices that are connected to the network for real-time management and medication of patient. The resource constraint IoMT devices require energy-efficient lightweight security schemes to protect medical data. To the best of our knowledge, majority of the existing studies concentrate either on security or on energy efficiency issues. The main contribution of this research is to develop an energy-efficient lightweight authentication method without sacrificing the security level. The authentication is done using multiple factors, namely password, physical unclonable function (PUF), Chebyshev polynomial, smartcard and fuzzy extractor. Chebyshev polynomial is used for non-identification of the private key by any attacker, whereas PUF protects the smart card from cloning by generating unique challenge–response pairs. Apart from authentication, the proposed scheme includes Blockchain-enabled distributed trustable ecosystem among independent participants where miner is selected using lightweight fuzzy system. The proposed scheme carries out the formal security analysis using real or random model which is perseverance against different external attacks and the security verification has been done using AVISPA (Automate Validation of Internet Security Protocols and Applications) tool. As a major finding, the simulation result using NS-3 simulator confirms that the proposed study outperforms the existing studies in terms of packet loss rate, throughput, end-to-end delay, computational cost and communication cost. [ABSTRACT FROM AUTHOR]

The emergence of remote health monitoring and increased at-home care emphasizes the importance of patient adherence outside the clinical setting. This is particularly pertinent in the treatment of Attention Deficit Hyperactivity Disorder (ADHD) in pediatric patients, as the population inherently has difficulty remembering and initiating treatment tasks. Neurostimulation is an emerging treatment modality for pediatric ADHD and requires strict adherence to a treatment regimen to be followed in an at-home setting. Thus, to achieve the desired therapeutic effect, careful attention must be paid to design features that can passively promote and effectively monitor therapeutic adherence. This work describes instrumentation designed to support a clinical trial protocol that tests whether choice of color, or color itself, can statistically significantly increase adherence rates in pediatric ADHD patients in an extraclinical environment. This is made possible through the development and application of an internet-of-things approach in a remote adherence monitoring technology that can be implemented in forthcoming neurostimulation devices for pediatric patient use. This instrumentation requires minimal input from the user, is durable and resistant to physical damage, and provides accurate adherence data to parents and physicians, increasing assurance that neurostimulation devices are effective for at-home care. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) has recently become the norm in medical operations and emergency situations. A physician or a medical personnel is interested in accessing the patients' data from remote locations using IoMT. The patients' medical management system should allow their doctors and family members to have access to the data, especially in emergency situations. Deploying sensor nodes and managing the large amount of medical data generated by these sensors require a system that provides protection against known security threats and a robust mechanism to recover when subjected to attacks. The proposed scheme that provides mutual authentication between user(s) and power generation nodes, and also mitigate against attacks by validating the identity during the authentication phase. Moreover, the proposed scheme, called Grid‐Based Authentication, uses the existing smart grid infrastructure for communications, which is a model of integrated infrastructure in a smart city. Furthermore, the simulation results show that our proposed scheme yields better performance in terms of average throughput, end to end delay, and computation and communication costs compared with other state‐of‐the‐art approaches. [ABSTRACT FROM AUTHOR]

Advancements in information and communications technology (ICT) have fundamentally transformed computing, notably through the internet of things (IoT) and its healthcare-focused branch, the internet of medical things (IoMT). These technologies, while enhancing daily life, face significant security risks, including ransomware. To counter this, the authors present a scalable, hybrid machine learning framework that effectively identifies IoMT ransomware attacks, conserving the limited resources of IoMT devices. To assess the effectiveness of their proposed solution, the authors undertook an experiment using a state-of-the-art dataset. Their framework demonstrated superiority over conventional detection methods, achieving an impressive 87% accuracy rate. Building on this foundation, the framework integrates a multi-faceted feature extraction process that discerns between benign and malign actions, with a subsequent in-depth analysis via a neural network. This advanced analysis is pivotal in precisely detecting and terminating ransomware threats, offering a robust solution to secure the IoMT ecosystem. [ABSTRACT FROM AUTHOR]

Millennials have the advantage of accessing readily available modern scientific advancements, particularly in technology. One of these technologies that encompasses varied functionalities is the Internet of Things (IoT). In the midst of the Covid-19 pandemic, IoT, specifically Internet of Medical Things (IoMT), had pivotal significance in monitoring and tracking different health parameters. It autonomously manages an individual's health data and stores the same as Electronic Health Records (EHRs). However, the networking protocols used by IoMT are not adequate enough to ensure the security and privacy of EHRs. Consequently, such technology is susceptible to cyber-attacks, which have become more prevalent over time and have taken various forms, that generally the stakeholders are not aware of. This paper introduces machine learning-driven intrusion detection systems as a solution to tackle this issue. The focus of this study is on devising a Machine Learning (ML) oriented Intrusion Detection System (IDS) designed to identify cyber-attacks targeting IoMT based systems. Several classification based ML techniques such as Multinomial Naive Bayes, Logistic Regression, Logistic Regression with Stochastic Gradient Descent, Linear Support Vector Classification, Decision Tree, Ensemble Voting Classifier, Bagging, Random Forest, Adaptive Boosting, Gradient Boosting and Extreme Gradient Boosting were used, whereupon the Adaptive Boosting was experimentally found to perform the best on performance metrics such as accuracy, precision, recall, F1-score, False Detection Rate (FDR) and False Positive Rate (FPR). Further, it was found that Adaptive boosting based IDS for IoMT performed comparatively better than the existing ToN_IoT based IDS models on performance metrics such as accuracy, F1-score, FPR and FDR. [ABSTRACT FROM AUTHOR]

The problems of data storage and sharing have been well solved with cloud storage. However, the disadvantage is that users' messages are stored in the cloud without encryption protection. Cloud storage managers can access or even obtain user data, which brings significant security risks to data owners. Therefore, some cloud storage centers adopt ciphertext storage, which can protect them from the risk of user privacy disclosure. However, there are also problems, that is, how to find the desired data achieved in the cloud server search and how to ensure information integrity in the search process. Moreover, the encryption operation destroys the value and size relation of the original plaintext data. It does not have semantic and statistical characteristics when retrieving. Combining the hidden access structure in an attribute-based file searchable encryption model with proxy Re-Encryption technology, we propose an attribute-based file retrieval model with a partially hidden access structure supporting Proxy Re-Encryption. The model solves the above problems effectively and supports keyword updating. Finally, the security of the model is proved based on the hypothesis of D-linear and decisional q-parallel bilinear Diffie-Hellman exponent under the random oracle model. [ABSTRACT FROM AUTHOR]

The availability of drugs across the country is a direct measure for fairer public health. Several issues have been reported drastically related to various organizations that fail to provide quality medicines on time. There has been a consistent increase in cases where the treatment, as well as exempted drugs, were supplied due to the unavailability of proper traceability of the supply chain. Several parties are involved in the supply and have similar interests that may defer the adequate shareability of the drugs. The existing system for managing the drug supply chain suffers from several backlogs. The loss of information, unavailability of resources to track the proper medicinal storage, transparency of information sharing between various stakeholders and sequential access. The applicability of the decentralized model emerging from the blockchain can apply to one of the perfect solutions in this case. The drug traceability chain can be deployed to a Ledger-based blockchain that may result in decentralized information. Continuous supply from the Internet of Things (IoT) based devices might be handy as the middleware for providing a trustworthy, safe, and proper transaction-oriented system. The data integrity, along with the provenance resulting from the IoT-connected devices, is an effective solution towards managing the supply chain and drug traceability. This study presents a model that can provide a token-based blockchain that will help provide a cost-efficient and secure system for a reliable drug supply chain. [ABSTRACT FROM AUTHOR]

Mental health diagnosis often relies on subjective evaluations, which can be intrusive and lack objectivity. With the current global situation brought about by the COVID-19 pandemic, the need for real-time, on-demand healthcare services has become more apparent than ever. Fortunately, wearable Internet of Medical Things (IoMT) devices, such as wrist actigraphs and smartphones, offer a promising solution by generating objective data that can aid in early-stage mental health diagnosis. This paper presents a novel Deep Convolutional Neural Architecture (CNN) with a split attention mechanism, that outperforms the traditional methods of analyzing motor activity data in diagnosing schizophrenia. To address the unique characteristics of motor activity, the proposed method includes a novel imputation method, a sampling technique based on a sliding window for sample expansion, and the Synthetic Minority Over-sampling Technique (SMOTE) technique for class balancing. The results demonstrate the highest accuracy of 94% using 24-h actigraphy data. Overall, the application of this methodology can greatly contribute to the development of pervasive healthcare systems, providing non-invasive, objective, and real-time mental healthcare services. [ABSTRACT FROM AUTHOR]

One of the challenges that arise when utilizing real-time reaction services, such as constructing deep learning models within the Internet of Medical Things (IoMT) infrastructure, is effectively balancing the computation load between the cloud and fog computing layers. This paper proposes a unified framework of offline training and online response to the healthcare professional. The framework gathers medical images from various heterogeneous IoMT devices and then arranges them into homogeneous locations in the cloud, using a stage-one classification stage (or offline training). Furthermore, the stage-two classification (or online response) is employed to detect the type of cancer for each homogeneous location containing the same image type within the cloud. To evaluate the framework, we conducted extensive experiments on six well-known cancer datasets of multiple types. The stage-one classification shows superior results of the error rates for the InceptionResNetV2 and DenseNet201 pre-trained transfer learning models of 0.33% and 0.43% with accuracy values of 99.67% and 99.57% respectively. In the stage-two classification, the results show different performances on each dataset. The point is that each dataset is organized separately which helps in studying the influence of pre-trained transfer learning models and improving their performance in the absence of intervention and bias in datasets. [ABSTRACT FROM AUTHOR]

With superior computing power and efficient data collection capability, Internet of Medical Things (IoMT) significantly improves the accuracy and convenience of medical work. As most communications are over open networks, it is critical to encrypt data to ensure confidentiality before uploading them to cloud storage servers (CSSs). Public key encryption with keyword search (PEKS) allows users to search for specific keywords in ciphertext and plays an essential role in IoMT. However, PEKS still has the following problems: 1. As a semi-trusted third party, the CSSs may provide wrong search results to save computing and bandwidth resources. 2. Single-keyword searches often produce many irrelevant results, which is undoubtedly a waste of computing and bandwidth resources. 3. Most PEKS schemes rely on bilinear pairings, resulting in computational inefficiencies. 4. Public key infrastructure (PKI)-based or identity-based PEKS schemes face the problem of certificate management or key escrow. 5. Most PEKS schemes are vulnerable to offline keyword guessing attacks, online keyword guessing attacks, and insider keyword guessing attacks. We present a certificateless verifiable and pairing-free conjunctive public keyword searchable encryption (CLVPFC-PEKS) scheme. An efficiency analysis shows that the performance advantage of the new scheme is far superior to that of the existing scheme. More importantly, we provide proof of security under the standard model (SM) to ensure the reliability of the scheme in practical applications. [ABSTRACT FROM AUTHOR]

Irregular fuzzy mapping is used for encryption and scrambling of image information in this paper. The proposed image encryption algorithm has many characteristics including large key space, few relationships between the pixels of the encrypted image, high sensitivity to the key and high security, which can effectively protect the security of the encrypted image. Therefore, a robust audio watermarking method that hides encrypted image information in the time domain is presented. After dividing the audio signal into consecutive parts, five parameters are calculated independently, including the average frequency, edge, energy, zero crossing rate, and standard deviation for different parts of the signal. With the LBP method, the encoded image is placed in different parts based on the weights determined by the fuzzy system defined on the five parameters for each part of the audio signal. Consequently, the distortion caused by hiding the watermark will not result in adverse listening effects, and the watermark will remain resistant to attacks and common audio processing. Based on the simulation results of the proposed technique under MATLAB software, it reveals the resistance of the watermark against various attacks and increasing the speed of calculations. Due to the combination of watermarking and simultaneous encoding of image information, as well as examining a large number of image evaluation criteria, including information entropy criteria and peak signal-to-noise ratio (PSNR), number of pixel change rate (NPCR), unified average changing intensity (UACI), we have been able to create a good improvement for the security of information transmission in the IoMT field. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is still a relatively new field of research, and its potential to be used in the healthcare and medical sectors is enormous. In the last five years, IoT has been a go-to option for various applications such as using sensors for different features, machine-to-machine communication, etc., but precisely in the medical sector, it is still lagging far behind compared to other sectors. Hence, this study emphasises IoT applications in medical fields, Medical IoT sensors and devices, IoT platforms for data visualisation, and artificial intelligence in medical applications. A systematic review considering PRISMA guidelines on research articles as well as the websites on IoMT sensors and devices has been carried out. After the year 2001, an integrated outcome of 986 articles was initially selected, and by applying the inclusion–exclusion criterion, a total of 597 articles were identified. 23 new studies have been finally found, including records from websites and citations. This review then analyses different sensor monitoring circuits in detail, considering an Intensive Care Unit (ICU) scenario, device applications, and the data management system, including IoT platforms for the patients. Lastly, detailed discussion and challenges have been outlined, and possible prospects have been presented. [ABSTRACT FROM AUTHOR]

The introduction of IoT technologies, such as those used in remote health monitoring applications, has revolutionized conventional medical care. Furthermore, the approach utilized to obtain insights from the scrutiny of lifestyle elements and activities is crucial to the success of tailored healthcare and disease prevention services. Intelligent data retrieval and classification algorithms allow for the investigation of disease and the prediction of aberrant health states. The convolutional neural network (CNN) strategy is utilized to forecast such anomaly because it can successfully recognize the knowledge significant to disease anticipation from amorphous medical heath records. Conversely, if a fully coupled network-topology is used, CNN guzzles a huge memory. Furthermore, the complexity analysis of the model may rise as the number of layers grows. Therefore, we present a CNN target recognition and anticipation strategy based on the Pearson correlation coefficient (PCC) and standard pattern activities to address these shortcomings of the CNN model. It is built in this framework and used for classification purposes. In the initial hidden layer, the most crucial health-related factors are chosen, and in the next, a correlation-coefficient examination is performed to categorize the health factors into positively and negatively correlated groups. Mining the occurrence of regular patterns among the categorized health parameters also reveals the behavior of regular patterns. The model output is broken down into obesity, hypertension, and diabetes-related factors with known correlations. To lessen the impact of the CNN-typical knowledge discovery paradigm, we use two separate datasets. The experimental results reveal that the proposed model outperforms three other machine learning techniques while requiring less computational effort. [ABSTRACT FROM AUTHOR]

The hype of the Internet of Things as an enabler for intelligent applications and related promise for ushering accessibility, efficiency, and quality of service is met with hindering security and data privacy concerns. It follows that such IoT systems, which are empowered by artificial intelligence, need to be investigated with cognisance of security threats and mitigation schemes that are tailored to their specific constraints and requirements. In this work, we present a comprehensive review of security threats in IoT and emerging countermeasures with a particular focus on malware and man-in-the-middle attacks. Next, we elaborate on two use cases: the Internet of Energy Things and the Internet of Medical Things. Innovative artificial intelligence methods for automating energy theft detection and stress levels are first detailed, followed by an examination of contextual security threats and privacy breach concerns. An artificial immune system is employed to mitigate the risk of malware attacks, differential privacy is proposed for data protection, and federated learning is harnessed to reduce data exposure. [ABSTRACT FROM AUTHOR]

Lactate, once merely regarded as an indicator of tissue hypoxia and muscular fatigue, has now gained prominence as a pivotal biomarker across various medical disciplines. Recent research has unveiled its critical role as a high-value prognostic marker in critical care medicine. The current practice of lactate detection involves periodic blood sampling. This approach is invasive and confined to measurements at six-hour intervals, leading to resource expenditure, time consumption, and patient discomfort. This review addresses non-invasive sensors that enable continuous monitoring of lactate in critical care patients. After the introduction, it discusses the iontophoresis system, followed by a description of the structural materials that are universally employed to create an interface between the integumentary system and the sensor. Subsequently, each method is detailed according to its physical principle, outlining its advantages, limitations, and pertinent aspects. The study concludes with a discussion and conclusions, aiming at the design of an intelligent sensor (Internet of Medical Things or IoMT) to facilitate continuous lactate monitoring and enhance the clinical decision-making support system in critical care medicine. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is an evolving field in healthcare that connects medical devices to the Internet to enable efficient data sharing and health information collection. The IoMT aims to improve the quality of healthcare, facilitate diagnosis and treatment, and enhance patient safety. Nonetheless, the IoMT networks are usually exposed to multiple security attacks. Also, recent studies indicate that security protocols contain flaws in protecting patient data. Thus, data must be protected by innovative security protocols. In our work, we propose a Medical Security Protocol (MedSecP) to support security in IoMT. The proposed protocol adopts the Twofish encryption, Naive Bayes (NB), and decision tree (DT) within the private blockchain (PBC) Fog Computing (FC) to build robust security procedures. The Twofish encryption algorithm is used to provide medical information concealment. In our proposed protocol, the type of data is first determined, and accurate and appropriate medical decisions are made based on the collected data using a decision tree algorithm, and then rapid classification of the patient data is done using the Naive Bayes algorithm. Confidential medical data is then encrypted using the Twofish algorithm to ensure the confidentiality of this data and prevent unauthorized access. Finally, this encrypted medical data is stored using blockchain technology. Twofish, NB, and DT are organized to work harmoniously with the PBC. The latter manages and distributes data peer-to-peer in IoMT. We leverage Fog Computing to speed up decision-making without resorting to the remote cloud. We analyzed our protocol in terms of security and performance. Our results indicate that MedSecP provides reliable security against attacks as the protocol demonstrated an average security attack response rate of 97.20%, demonstrating its resistance to external threats by keeping the encrypted medical data, classified and achieving appropriate medical decisions. In terms of performance, MedSecP has demonstrated an average security response time of around 50ms, providing fast and efficient performance. In MedSecP, the highest value for encryption is 0.000015 ms, and decryption is 0.000017 ms when applying the Twofish algorithm which is considered extremely suitable for implementing health systems operations compared to existing encryption algorithms. Consequently, MedSecP provides lightweight operations in support of complex security measures that qualify it to support healthcare institutions.