Security and Privacy Management in Internet of Medical Things (IoMT): A Synthesis

Hireche, Rachida; Mansouri, Houssem; Pathan, Al-Sakib Khan

doi:10.3390/jcp2030033

Cited by 43 publications

(16 citation statements)

References 80 publications

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“…However, if there is more than one, the best solution is selected when the search agent's position is modified using Eq (15). The inertia weight is changed in the next stage using Eq (13). The next stage involves updating the position and velocity parameters in the APSO-CNN using Eqs (11) and (12).…”

Section: As Illustrated Inmentioning

confidence: 99%

“…Following the spread of COVID-19, it was found that 71% of security specialists believe that security vulnerabilities in IoT networks are increasing [10,11]. Some studies [12][13][14] describe the attacks and security vulnerabilities of each IoT layer and suggest ways to protect privacy [15][16][17]. Attacks on Internet of Things devices have increased and several studies have developed intrusion detection systems [18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…Store each particle's minimal fitness value, the global minimum, and compute the average. The inertia weight is changed in the third stage using the Eq (13). The fourth stage involves updating the position and velocity parameters in the APSO-CNN using the Eqs (11) and (12).…”

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confidence: 99%

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A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach

et al. 2022

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Due to the huge number of connected Internet of Things (IoT) devices within a network, denial of service and flooding attacks on networks are on the rise. IoT devices are disrupted and denied service because of these attacks. In this study, we proposed a novel hybrid meta-heuristic adaptive particle swarm optimization–whale optimizer algorithm (APSO-WOA) for optimization of the hyperparameters of a convolutional neural network (APSO-WOA-CNN). The APSO–WOA optimization algorithm’s fitness value is defined as the validation set’s cross-entropy loss function during CNN model training. In this study, we compare our optimization algorithm with other optimization algorithms, such as the APSO algorithm, for optimization of the hyperparameters of CNN. In model training, the APSO–WOA–CNN algorithm achieved the best performance compared to the FNN algorithm, which used manual parameter settings. We evaluated the APSO–WOA–CNN algorithm against APSO–CNN, SVM, and FNN. The simulation results suggest that APSO–WOA–CNf[N is effective and can reliably detect multi-type IoT network attacks. The results show that the APSO–WOA–CNN algorithm improves accuracy by 1.25%, average precision by 1%, the kappa coefficient by 11%, Hamming loss by 1.2%, and the Jaccard similarity coefficient by 2%, as compared to the APSO–CNN algorithm, and the APSO–CNN algorithm achieves the best performance, as compared to other algorithms.

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Section: As Illustrated Inmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach

et al. 2022

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“…The ransomware attacks are rapidly growing and are threats to individuals, businesses, and government and private sectors, where the attackers infect their deployed infrastructure and demand large amounts of ransom [ 8 , 9 , 10 ]. Due to an excess of such attacks, newer versions of the ransomware attacks appear frequently and have been responsible for millions of dollars of losses annually [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

An Effective Self-Configurable Ransomware Prevention Technique for IoMT

Tariq

Ullah

Uddin

et al. 2022

Sensors

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Remote healthcare systems and applications are being enabled via the Internet of Medical Things (IoMT), which is an automated system that facilitates the critical and emergency healthcare services in urban areas, in addition to, bridges the isolated rural communities for various healthcare services. Researchers and developers are, to date, considering the majority of the technological aspects and critical issues around the IoMT, e.g., security vulnerabilities and other cybercrimes. One of such major challenges IoMT has to face is widespread ransomware attacks; a malicious malware that encrypts the patients’ critical data, restricts access to IoMT devices or entirely disable IoMT devices, or uses several combinations to compromise the overall system functionality, mainly for ransom. These ransomware attacks would have several devastating consequences, such as loss of life-threatening data and system functionality, ceasing emergency and life-saving services, wastage of several vital resources etc. This paper presents a ransomware analysis and identification architecture with the objective to detect and validate the ransomware attacks and to evaluate its accuracy using a comprehensive verification process. We first develop a comprehensive experimental environment, to simulate a real-time IoMT network, for experimenting various types of ransomware attacks. Following, we construct a comprehensive set of ransomware attacks and analyze their effects over an IoMT network devices. Furthermore, we develop an effective detection filter for detecting various ransomware attacks (e.g., static and dynamic attacks) and evaluate the degree of damages caused to the IoMT network devices. In addition, we develop a defense system to block the ransomware attacks and notify the backend control system. To evaluate the effectiveness of the proposed framework, we experimented our architecture with 194 various samples of malware and 46 variants, with a duration of sixty minutes for each sample, and thoroughly examined the network traffic data for malicious behaviors. The evaluation results show more than 95% of accuracy of detecting various ransomware attacks.

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“…Specifically, IoMT can assist with the requirement of more genericity and scalability [1]. Many healthcare practitioners use IoMT applications to improve therapy, disease control, failure reduction, drug prescription management, and cost savings [3]. Furthermore, IoMT tremendously advances healthcare systems by expediting procedures that enable the adoption of cutting-edge diagnostic and therapeutic techniques via connected wearable sensor devices and real-time monitoring data available from IoT technologies [4].…”

Section: Introductionmentioning

confidence: 99%

A Survey: To Govern, Protect, and Detect Security Principles on Internet of Medical Things (IoMT)

Alhaj

Abdulla²,

Iderss³

et al. 2022

IEEE Access

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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.

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Security and Privacy Management in Internet of Medical Things (IoMT): A Synthesis

Cited by 43 publications

References 80 publications

A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach

A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach

An Effective Self-Configurable Ransomware Prevention Technique for IoMT

A Survey: To Govern, Protect, and Detect Security Principles on Internet of Medical Things (IoMT)

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