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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]

The Internet of Medical Things (IoMT) has transformed healthcare by connecting medical devices, sensors, and patients, significantly improving patient care. However, the sensitive data exchanged through IoMT is vulnerable to security attacks, raising serious privacy concerns. Traditional key sharing mechanisms are susceptible to compromise, posing risks to data integrity. This paper proposes a Timestamp-based Secret Key Generation (T-SKG) scheme for resource-constrained devices, generating a secret key at the patient's device and regenerating it at the doctor's device, thus eliminating direct key sharing and minimizing key compromise risks. Simulation results using MATLAB and Java demonstrate the T-SKG scheme's resilience against guessing, birthday, and brute force attacks. Specifically, there is only a 9 % chance of key compromise in a guessing attack if the attacker knows the key sequence pattern, while the scheme remains secure against brute force and birthday attacks within a specified timeframe. The T-SKG scheme is integrated into a healthcare framework to securely transmit health vitals collected using the MySignals sensor kit. For confidentiality, the Data Encryption Standard (DES) with various Cipher Block modes (ECB, CBC, CTR) is employed.

In recent days, the Internet of Medical Things (IoMT) is commonly employed in different aspects of healthcare applications. Owing to the increasing necessitates of IoT, a huge amount of sensing data is collected from distinct IoT gadgets. To investigate the generated data, artificial intelligence (AI) models plays an important role to achieve scalability and accurate examination in real-time environment. However, the characteristics of IoMT result in certain design challenges, namely, security and privacy, resource limitation, and inadequate training data. At the same time, blockchain, an upcoming technology, has offered a decentralized architecture, which gives secured data transmission and resources to distinct nodes of the IoT environment and is stimulated for eliminating centralized management and eliminates the challenges involved in it. This paper designs deep learning (DL) with blockchain-assisted secure image transmission and diagnosis model for the IoMT environment. The presented model comprises a few processes namely data collection, secure transaction, hash value encryption, and data classification. Primarily, elliptic curve cryptography (ECC) is applied, and the optimal key generation of ECC takes place using hybridization of grasshopper with fruit fly optimization (GO-FFO) algorithm. Then, the neighborhood indexing sequence (NIS) with burrow wheeler transform (BWT), called NIS-BWT, is employed to encrypt the hash values. At last, a deep belief network (DBN) is utilized for the classification process to diagnose the existence of disease. An extensive experimental validation takes place to determine the analysis of the optimal results of the presented model, and the results are investigated under diverse aspects. [ABSTRACT FROM AUTHOR]

Electronic health records (EHRs) security is a critical challenge in the implementation and administration of Internet of Medical Things (IoMT) systems within the healthcare sector's heterogeneous environment. As digital transformation continues to advance, ensuring privacy, integrity, and availability of EHRs become increasingly complex. Various imaging modalities, including PET, MRI, ultrasonography, CT, and X-ray imaging, play vital roles in medical diagnosis, allowing healthcare professionals to visualize and assess the internal structures, functions, and abnormalities within the human body. These diagnostic images are typically stored, shared, and processed for various purposes, including segmentation, feature selection, and image denoising. Cryptography techniques offer a promising solution for protecting sensitive medical image data during storage and transmission. Deep learning has the potential to revolutionize cryptography techniques for securing medical images. This paper explores the application of deep learning techniques in medical image cryptography, aiming to enhance the privacy and security of healthcare data. It investigates the use of deep learning models for image encryption, image resolution enhancement, detection and classification, encrypted compression, key generation, and end-to-end encryption. Finally, we provide insights into the current research challenges and promising directions for future research in the field of deep learning applications in medical image cryptography. [ABSTRACT FROM AUTHOR]

Currently, Internet of Medical Things (IoMT) gained popularity because of an ongoing pandemic. A few developed countries plan to deploy the IoMT for improving the security and safety of frontline workers to decrease the mortality rates of COVID‐19 patients. However, IoMT devices share the information through an open network which leads to increased vulnerability to various attacks. Hence, electronic health management systems remain many security challenges, like recording sensitive patient data, secure communication, transferring patient information to other doctors, providing the data for future medical diagnosis, collecting data from WBAN, etc. In addition, the sensor devices attached to the human body are resource‐limited and have minimal power capacity. Hence, to protect the medical privacy of patients, confidentiality and reliability of the system, the register sensor, doctor and server need to authenticate each other. Therefore, rather than two factors, in this work, a multifactor authentication protocol has been proposed to provide more secure communication. The presented scheme uses biometric and fuzzy extractors for more security purposes. Furthermore, the scheme is proved using informal and formal security verification BAN logic, ProVerif and AVISPA tools. The ProVerif simulation result of the suggested scheme shows that the proposed protocol achieves session key secrecy and mutual authentication [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) applications are among the major trends of nowadays. Billions of connected devices are creating great business profits and performing a multitude of automated tasks in many daily human activities. In healthcare service delivery, IoT capabilities are difficult to overestimate, they are progressively becoming entangled and commonly coined Internet of Medical Things (IoMT). The participating nodes in IoMT networks generate, collect and exchange huge amounts of extremely private and sensitive data. Numerous security vulnerabilities arise due to the complexity and the heterogeneity of the technology. New risks, born out of IoMT systems, cannot easily be supported by existing risk management frameworks. The existing cyber-security risk assessment methods and approaches, deployed in several organizations, will not address the IoMT inherent risks properly. This study includes a comprehensive review of IoMT systems. Popular risk assessment methods are discussed and their suitability to IoMT is dealt with in detail. Based on this study, we propose a framework to enhance trust and help with decision making in e-healthcare environments given its high-risk exposure. The proposal is based on a quantified risk assessment approach. Our aim is to define a novel approach/model for improving trust and risk management in an e-health context. [ABSTRACT FROM AUTHOR]

Sleep apnea syndrome (SAS) is a breathing disorder while a person is asleep. The traditional method for examining SAS is Polysomnography (PSG). The standard procedure of PSG requires complete overnight observation in a laboratory. PSG typically provides accurate results, but it is expensive and time consuming. However, for people with Sleep apnea (SA), available beds and laboratories are limited. Resultantly, it may produce inaccurate diagnosis. Thus, this paper proposes the Internet of Medical Things (IoMT) framework with a machine learning concept of fully connected neural network (FCNN) with k-nearest neighbor (k-NN) classifier. This paper describes smart monitoring of a patient's sleeping habit and diagnosis of SA using FCNN-KNN+ average square error (ASE). For diagnosing SA, the Oxygen saturation (SpO2) sensor device is popularly used for monitoring the heart rate and blood oxygen level. This diagnosis information is securely stored in the IoMT fog computing network. Doctors can carefully monitor the SA patient remotely on the basis of sensor values, which are efficiently stored in the fog computing network. The proposed technique takes less than 0.2 s with an accuracy of 95%, which is higher than existing models. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is an online device that senses and transmits medical data from users to physicians within a time interval. In, recent years, IoMT has rapidly grown in the medical field to provide healthcare services without physical appearance. With the use of sensors, IoMT applications are used in healthcare management. In such applications, one of the most important factors is data security, given that its transmission over the network may cause obtrusion. For data security in IoMT systems, blockchain is used due to its numerous blocks for secure data storage. In this study, Blockchain-assisted secure data management framework (BSDMF) and Proof of Activity (PoA) protocol using malicious code detection algorithm is used in the proposed data security for the healthcare system. The main aim is to enhance the data security over the networks. The PoA protocol enhances high security of data from the literature review. By replacing the malicious node from the block, the PoA can provide high security for medical data in the blockchain. Comparison with existing systems shows that the proposed simulation with BSD-Malicious code detection algorithm achieves higher accuracy ratio, precision ratio, security, and efficiency and less response time for Blockchain-enabled healthcare systems. [ABSTRACT FROM AUTHOR]

Bioengineering due to advancement in technological innovation has grown and expanded its horizons in various allied field but this paper will deal with biomedical engineering, and it's interlinked with Internet of Medical Things (IoMT) its applicational concerns and issues relating to lack of standardization and regulation of IoMT across the world. Herein the paper the author firstly will discuss about the distinction and interrelationship between bioengineering and biomedical engineering and how they are overlapping with each other. Bioengineering is a broader canopy that encompasses various allied fields within itself of which biomedical engineering is one. Due to technologically innovative advancement because of Internet of Things (IoT) rapid growth that has been adapted in medical health care sector too and medical healthcare sector are using varied IoMT devices that had eased the functionality of medical professionals but at the same time had raised various concerns and challenges pertaining to data security, cybersecurity, privacy, and confidentiality. Medical professionals are regulated strictly nationally and internationally but regulation of IoMT is challenging due to lack of standardize practices, policies and procedures not at place, lack of trained IoT medical professionals and when such devices connected with internet networks that may faces data breaches which could risk the data that could be tampered, privacy infringement when not authorized and how it shared at different levels could lead concerns about IoMT. Biomedical devices preventive and protective measures mechanisms could be place are dealt. [ABSTRACT FROM AUTHOR]

The Internet of medical things (IoMT) provides an ecosystem in which to connect humans, devices, sensors, and systems and improve healthcare services through modern technologies. The IoMT has been around for quite some time, and many architectures/systems have been proposed to exploit its true potential. Healthcare through the Internet of things (IoT) is envisioned to be efficient, accessible, and secure in all possible ways. Even though the personalized health service through IoT is not limited to time or location, many associated challenges have emerged at an exponential pace. With the rapid shift toward IoT-enabled healthcare systems, there is an extensive need to examine possible threats and propose countermeasures. Authentication is one of the key processes in a system's security, where an individual, device, or another system is validated for its identity. This survey explores authentication techniques proposed for IoT-enabled healthcare systems. The exploration of the literature is categorized with respect to the technology deployment region, as in cloud, fog, and edge. A taxonomy of attacks, comprehensive analysis, and comparison of existing authentication techniques opens up possible future directions and paves the road ahead. [ABSTRACT FROM AUTHOR]

In the healthcare applications, Internet of Medical Things (IoMT) comforts the communication processes between the medical devices and the humans via wireless network. Moreover, this communication helps both the physicians and the patients to contact remotely for the diagnosis of the disease's wearable devices sensor signals. However, IoMT system violates the privacy preserving of Patient's Health Record (PHR) as well as self-sovereign identity of patient. In this regard, security action should be taken. Previous techniques used in IoMT are in lack of data consistency, confidentiality, and inaccessible of data. To overcome these issues, the fog computing-based technology is used in order to handle the health data of the patient for digital identity management. Self-Sovereign Identity empowers the patient to handle and control their own data. This paper proposes Fog Computing based Self-Sovereign Identity with Rivest, Shamir, Adleman (RSA) for IoMT (FCSSI+RSA-IoMT). For that, it keeps the Patient Health Record (PHR) securely and effectively using RSA to make the identification and authentication procedures at ease. Besides, PHR is used to store the sensitive information regarding identity and privacy of the patient. Additionally, it records the diagnosis history of patient by the doctors and radiology images. Therefore, the result evaluation proves that, for 100 users, the proposed technique takes only 631 secs and also the throughput performance is better when compared to the existing techniques. [ABSTRACT FROM AUTHOR]

The integration of medical equipment into the Internet of Things (IoT) led to the introduction of Internet of Medical Things (IoMT). Variation of IoT devices have been equipped in medical facilities. These devices provided convenience to healthcare provider since they can continuously monitor their patients in real-time, while allowing them to have greater physical flexibility and mobility. However, users of healthcare services (such as patients and medical staff) often are less concerned about security issues associated with IoT. These alleviate existing problems and jeopardize the lives of their patients by making them susceptible to attacks. Furthermore, IoMT applications have direct access to healthcare services because it handles sensitive patient information. Therefore, it is extremely important to preserve and establish the security and privacy of IoMT. This further justifies the need to investigate and address the related issues. Despite existing literature on security and privacy mechanisms, the domain still requires more attention. Therefore, this paper aims to discuss the security and privacy principles, as well as challenges associated with IoMT. Besides, a comprehensive analysis of privacy and security solutions for IoMT is also presented. In addition, we introduced a novel taxonomy of IoMT security and privacy based on cyber security principles such as “govern,” “protect,” and “detect”. In conclusion, this paper provides a discussion on existing challenges and future direction for researchers.

The proliferation of chronic disorders such as COVID-19 has recognized the importance of people all over the world having immediate access to healthcare. The recent pandemic has shown deficiencies in the traditional healthcare infrastructure, namely that hospitals and clinics alone are inadequate for grappling with such a disaster. One of the key technologies that favours new healthcare solutions is smart and interconnected wearables. Thanks to developments in the Internet of Things (IoT), these wearables will now collect data on an unprecedented scale. However, as a result of their extensive use, security in these critical systems has become a major concern. This paper presents an intrusion detection mechanism based on Machine Learning Algorithms for healthcare applications used in home network environments. Experiments are carried out on a home network to detect attacks against a health care application. Experiments using the proposed mechanism based on Machine Learning algorithms to detect attacks against a healthcare application are carried out on a home network, and the results show a good performance of the used algorithms.

The Internet of Things (IoT) has radically transformed how patient information and healthcare monitoring are monitored and recorded and has revolutionized the area by ensuring regular 24 × 7 tracking without costly and restricted human resources and with a low mistake probability. The Internet of Medical Things (IoMT) is a subsection of the Internet of things (IoT) that uses medical equipment as things or nodes to enable cost-effective and efficient patient monitoring and recording. The IoMT can cope with a wide range of problems, including observing patients in hospitals, monitoring patients in their homes, and assisting consulting physicians and nurses in monitoring health conditions at regular intervals and issuing warning signals if emergency care is necessary. EEG signals, electrocardiograms (ECGs), blood sugar levels, blood pressure levels, and other conditions can be examined. In crucial situations, quick and real-time analysis is essential, and failure to provide careful attention can be fatal. A cloud-based IoT platform cannot handle these latency-sensitive conditions. Fog computing (FC) is a novel paradigm for assigning, processing, and storing resources to IoT devices with limited resources. Where substantial processing power or storage is required, all nodes in a fog computing scheme can delegate their jobs to local fog nodes rather than forwarding them to the cloud module at a greater distance. Identifying potential security risks and putting in place adequate security measures are critical. This work aims to examine a blockchain (BC) as a potential tool for mitigating the impact of these difficulties in conjunction with fog computing. This research shows that blockchain can overcome fog computing's privacy and security concerns. It also discusses blockchain's issues and limitations from the perspective of fog computing (FC) and the IoMT. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) has become a strategic priority for future e-healthcare because of its ability to improve patient care and its scope of providing more reliable clinical data, increasing efficiency, and reducing costs. It is no wonder that many healthcare institutions nowadays like to harness the benefits offered by the IoMT. In fact, it is an infrastructure with connected medical devices, software applications, and care systems and services. However, the accelerated adoption of connected devices also has a serious side effect: it obscures the broader need to meet the requirements of standard security for modern converged environments (even beyond connected medical devices). Adding up different types and numbers of devices risks creating significant security vulnerabilities. In this paper, we have undertaken a study of various security techniques dedicated to this environment during recent years. This study enables us to classify these techniques and to characterize them in order to benefit from their positive aspects. [ABSTRACT FROM AUTHOR]

Recently internet of medical things (IoMT) act as a smart doctor using sensor wearable's device in human body. This smart doctor device senses necessary medical data from human and transfer via network immediately to physician. It is important to transfer sensitive data very securely. Blockchain becomes trending technology to provide high security to both end users in the network. Traditionally security structure is relying on cryptographic techniques which is very expensive and takes more time in securely transmitting data. To overcome this issue, this paper builds a cost effective, blockchain with IoMT using fog-cloud computing. The aim of research is to provide cost effective healthcare services in the present system. This study develops an IoMT systems using fusion of scheduling techniques in blockchain. We propose a Smart Contract with Function Based Cost Efficient Task Scheduling (FTS-SCON) algorithm in blockchain framework. The proposed algorithm uses functions to schedule the task in the blockchain which is cost effective. With the help of cryptography based Blockchain schemes with smart contracts enables consistency and validation of data with symmetric cryptography. Simulation results shows the proposed outperform all existing elements regarding data security, validation by 10%, and cost of application execution by 30% in IoMT. [ABSTRACT FROM AUTHOR]

An identity management system is essential in any organisation to provide quality services to each authenticated user. The smart healthcare system should use reliable identity management to ensure timely service to authorised users. Traditional healthcare uses a paper-based identity system which is converted into centralised identity management in a smart healthcare system. Centralised identity management has security issues such as denial of service attacks, single-point failure, information breaches of patients, and many privacy issues. Decentralisedidentity management can be a robust solution to these security and privacy issues. We proposed a Self-Sovereign identity management system for the smart healthcare system (SSI-SHS), which manages the identity of each stakeholder, including medical devices or sensors, in a decentralisedmanner in the Internet of Medical Things (IoMT) Environment. The proposed system gives the user complete control of their data at each point. Further, we analysed the proposed identity management system against Allen and Cameron's identity management guidelines. We also present the performance analysis of SSI as compared to the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Internet of Multimedia Things (IoMT) has gained popularity due to its immersive growth and real-time applications range from smart health to smart transportation systems. These systems always have various threats related to security breaches and privacy. With the passage of time, all types of cybercrimes and terrorism activities and its planning were initiated by using these networks and technologies. Terrorist attacks are unavoidable because of well-organized and well-planned attack planning. The all the data among these threat actors transmitted by using IoMT sensors and devices. Therefore, there is a need to analyze this type of data and predict the terrorist activities for in-time decision making. This paper presents a complete overview of existing models for such type of data and proposes a Terrorist Attacks Internet of Multimedia Things (TA-IoMT) model and a predictive model for public security in IoMT networks. The proposed model provides more effective data handling, cloud storage data management, and prediction to control and detect these kinds of activities. The results show an average of 89% accuracy, 0.73% sensitivity, and 0.92% specificity as compared to existing solutions. [ABSTRACT FROM AUTHOR]

Internet of Medical Things (IoMT) is a breakthrough technology in the transfer of medical data via a communication system. Wearable sensor devices collect patient data and transfer them through mobile internet, that is, the IoMT. Recently, the shift in paradigm from manual data storage to electronic health recording on fog, edge, and cloud computing has been noted. These advanced computing technologies have facilitated medical services with minimum cost and available conditions. However, the IoMT raises a high concern on network security and patient data privacy in the health care system. The main issue is the transmission of health data with high security in the fog computing model. In today's market, the best solution is blockchain technology. This technology provides high-end security and authentication in storing and transferring data. In this research, a blockchain-based fog computing model is proposed for the IoMT. The proposed technique embeds a block chain with the yet another consensus (YAC) protocol building security infrastructure into fog computing for storing and transferring IoMT data in the network. YAC is a consensus protocol that authenticates the input data in the block chain. In this scenario, the patients and their family members are allowed to access the data. The empirical outcome of the proposed technique indicates high reliability and security against dangerous threats. The major advantages of using the blockchain model are high transparency, good traceability, and high processing speed. The technique also exhibits high reliability and efficiency in accessing data with secure transmission. The proposed technique achieves 95% reliability in transferring a large number of files up to 10,000. [ABSTRACT FROM AUTHOR]

The seamless integration of medical sensors and the Internet of Things (IoT) in smart healthcare has leveraged an intelligent Internet of Medical Things (IoMT) framework to detect the criticality of the patients. However, due to the limited storage capacity and computation power of the local IoT devices, patient’s health data needs to transfer to remote computing devices for analysis, which can easily result in privacy leakage due to lack of control over the patient’s health data and the vulnerability of the network for various types of attacks. Motivated by this, in this paper, an Empirical Intelligent Agent (EIA) based on a unique Swarm-Neural Network (Swarm-NN) method is proposed to identify attackers in the edge-centric IoMT framework. The major outcome of the proposed strategy is to identify the attacks during data transmission through a network and analyze the health data efficiently at the edge of the network with higher accuracy. The proposed Swarm-NN strategy is evaluated with a real-time secured dataset, namely the ToN-IoT dataset that collected Telemetry, Operating systems, and Network data for IoT applications and compares the performance over the standard classification models using various performance metrics. The test results demonstrate that the proposed Swarm-NN strategy achieves 99.5% accuracy over the ToN-IoT dataset. [ABSTRACT FROM AUTHOR]

The proliferation of chronic disorders such as COVID-19 has recognized the importance of people all over the world having immediate access to healthcare. The recent pandemic has shown deficiencies in the traditional healthcare infrastructure, namely that hospitals and clinics alone are inadequate for grappling with such a disaster. One of the key technologies that favours new healthcare solutions is smart and interconnected wearables. Thanks to developments in the Internet of Things (IoT), these wearables will now collect data on an unprecedented scale. However, as a result of their extensive use, security in these critical systems has become a major concern. This paper presents an intrusion detection mechanism based on Machine Learning Algorithms for healthcare applications used in home network environments. Experiments are carried out on a home network to detect attacks against a health care application. Experiments using the proposed mechanism based on Machine Learning algorithms to detect attacks against a healthcare application are carried out on a home network, and the results show a good performance of the used algorithms. [ABSTRACT FROM AUTHOR]

The Internet of medical things (IoMT) provides an ecosystem in which to connect humans, devices, sensors, and systems and improve healthcare services through modern technologies. The IoMT has been around for quite some time, and many architectures/systems have been proposed to exploit its true potential. Healthcare through the Internet of things (IoT) is envisioned to be efficient, accessible, and secure in all possible ways. Even though the personalized health service through IoT is not limited to time or location, many associated challenges have emerged at an exponential pace. With the rapid shift toward IoT-enabled healthcare systems, there is an extensive need to examine possible threats and propose countermeasures. Authentication is one of the key processes in a system’s security, where an individual, device, or another system is validated for its identity. This survey explores authentication techniques proposed for IoT-enabled healthcare systems. The exploration of the literature is categorized with respect to the technology deployment region, as in cloud, fog, and edge. A taxonomy of attacks, comprehensive analysis, and comparison of existing authentication techniques opens up possible future directions and paves the road ahead.

Nowadays healthcare systems have started to be integrated with Internet of Things (IoT) in order to deliver some benefits in diagnosis and treatment process, such as remote patient monitoring and data usage for analytics and fast treatment. With the rise of IoT healthcare devices, number of electronic health records are increased to a rate that it is estimated to exceed billions in the next few years. Although cloud computing is a practical solution for processing this kind of data, healthcare records contain confidential and sensitive patient data which makes this system very vulnerable to the security and privacy threats, so it needs more investigation. For making this critical information more secure, researchers have come up with a solution of applying blockchain technology in healthcare. In this paper, we review the latest literature of blockchain application in healthcare from the security and privacy perspective. Several existing works have been discussed and a comparative study is done among the published works, along with potential future perspectives. [ABSTRACT FROM AUTHOR]

Security and privacy are among the key barriers to adopting the Internet of Medical Things (IoMT) solutions. IoMT adopters have to adhere to security and privacy policies to ensure that patient data remains confidential and secure. However, there is confusion among IoMT stakeholders as to what security measures they should expect from the IoMT manufacturers and whether these measures would comply with the adopter's security and compliance requirements. In this paper, we present a recommendation tool that models IoMT concepts and security issues in addition to successively recommending security measures. The presented tool utilizes semantically enriched ontology to model the IoMT components, security issues, and measures. The developed ontology is equipped with context-aware rules to enable reasoning in order to build a recommendation system that empowers users to make well-educated decisions. The recommendation tool classifies IoMT security threats faced by IoMT stakeholders and automatically recommends security controls that have to be enforced for each threat. We have experimented the proposed tool with respect to the completeness and effectiveness of its output (i.e., security issues and recommended security measures). The results show that the tool was effectively able to recommend necessary security measures.

With the increasing demands on quality healthcare and the raising cost of care, pervasive healthcare is considered as a technological solutions to address the global health issues. In particular, the recent advances in Internet of Things have led to the development of Internet of Medical Things (IoMT). Although such low cost and pervasive sensing devices could potentially transform the current reactive care to preventative care, the security and privacy issues of such sensing system are often overlooked. As the medical devices capture and process very sensitive personal health data, the devices and their associated communications have to be very secured to protect the user's privacy. However, the miniaturized IoMT devices have very limited computation power and fairly limited security schemes can be implemented in such devices. In addition, with the widespread use of IoMT devices, managing and ensuring the security of IoMT systems are very challenging and which are the major issues hindering the adoption of IoMT for clinical applications. In this paper, the security and privacy challenges, requirements, threats, and future research directions in the domain of IoMT are reviewed providing a general overview of the state-of-the-art approaches.

An identity management system is essential in any organisation to provide quality services to each authenticated user. The smart healthcare system should use reliable identity management to ensure timely service to authorised users. Traditional healthcare uses a paper-based identity system which is converted into centralised identity management in a smart healthcare system. Centralised identity management has security issues such as denial of service attacks, single-point failure, information breaches of patients, and many privacy issues. Decentralisedidentity management can be a robust solution to these security and privacy issues. We proposed a Self-Sovereign identity management system for the smart healthcare system (SSI-SHS), which manages the identity of each stakeholder, including medical devices or sensors, in a decentralisedmanner in the Internet of Medical Things (IoMT) Environment. The proposed system gives the user complete control of their data at each point. Further, we analysed the proposed identity management system against Allen and Cameron’s identity management guidelines. We also present the performance analysis of SSI as compared to the state-of-the-art techniques.

The Internet of Things is visualized as a fundamental networking model that bridges the gap between the cyber and real-world entity. Uniting the real-world object with virtualization technology is opening further opportunities for innovation in nearly every individual's life. Moreover, the usage of smart heterogeneous multimedia devices is growing extensively. These multimedia devices that communicate among each other through the Internet form a unique paradigm called the Internet of Multimedia Things (IoMT). As the volume of the collected data in multimedia application increases, the security, reliability of communications, and overall quality of service need to be maintained. Primarily, distributed denial of service attacks unveil the pervasiveness of vulnerabilities in IoMT systems. However, the Software Defined Network (SDN) is a new network architecture that has the central visibility of the entire network, which helps to detect any attack effectively. In this regard, the combination of SDN and IoMT, termed SD-IoMT, has the immense ability to improve the network management and security capabilities of the IoT system. This article proposes an SDNassisted two-phase detection framework, namely SD-IoMT-Protector, in which the first phase utilizes the entropy technique as the detection metric to verify and alert about the malicious traffic. The second phase has trained with an optimized machine learning technique for classifying different attacks. The outcomes of the experimental results signify the usefulness and effectiveness of the proposed framework for addressing distributed denial of service issues of the SD-IoMT system. [ABSTRACT FROM AUTHOR]

The concept of Internet of Multimedia Things (IoMT) is becoming popular nowadays and can be used in various smart city applications, e.g., traffic management, healthcare, and surveillance. In the IoMT, the devices, e.g., Multimedia Sensor Nodes (MSNs), are capable of generating both multimedia and non-multimedia data. The generated data are forwarded to a cloud server via a Base Station (BS). However, it is possible that the Internet connection between the BS and the cloud server may be temporarily down. The limited computational resources restrict the MSNs from holding the captured data for a longer time. In this situation, mobile sinks can be utilized to collect data from MSNs and upload to the cloud server. However, this data collection may create privacy issues, such as revealing identities and location information of MSNs. Therefore, there is a need to preserve the privacy of MSNs during mobile data collection. In this paper, we propose an efficient privacy-preserving-based data collection and analysis (P2DCA) framework for IoMT applications. The proposed framework partitions an underlying wireless multimedia sensor network into multiple clusters. Each cluster is represented by a Cluster Head (CH). The CHs are responsible to protect the privacy of member MSNs through data and location coordinates aggregation. Later, the aggregated multimedia data are analyzed on the cloud server using a counter-propagation artificial neural network to extract meaningful information through segmentation. Experimental results show that the proposed framework outperforms the existing privacy-preserving schemes, and can be used to collect multimedia data in various IoMT applications. [ABSTRACT FROM AUTHOR]