A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects

Shakeel, Tanzeela; Habib, Shaista; Boulila, ‪Wadii; Koubâa, Anis; Javed, Abdul Rehman; Rizwan, Muhammad; Gadekallu, Thippa Reddy; Sufiyan, Mahmood

doi:10.1007/s40747-022-00767-w

Cited by 42 publications

(22 citation statements)

References 139 publications

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“…However, if CAIDA is used, large data set cannot be stored in the system thus high case external attacks is not prevented. In [19][20][21][22][23], data detection in industrial applications is analysed as data in entire segment inside the industry must be protected in reaching external users. Thus, the protection is provided using machine learning algorithm with two directional data flow procedures.…”

Section: Research Gap and Motivationmentioning

confidence: 99%

Construal Attacks on Wireless Data Storage Applications and Unraveling Using Machine Learning Algorithm

Kshirsagar

Manoharan²,

Alterazi

et al. 2022

Journal of Sensors

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Cloud services are a popular concept used to describe how internet-based services are delivered and maintained. The computer technology environment is being restructured with respect to information preservation. Data protection is of critical importance when storing huge volumes of information. In today’s cyber world, an intrusion is a significant security problem. Services, information, and services are all vulnerable to attack in the cloud due to its distributed structure of the cloud. Inappropriate behavior in the connection and in the host is detected using intrusion detection systems (IDS) in the cloud. DDoS attacks are difficult to protect against since they produce massive volumes of harmful information on the network. This assault forces the cloud services to become unavailable to target consumers, which depletes computer resources and leaves the provider exposed to massive financial and reputational losses. Cyber-analyst data mining techniques may assist in intrusion detection. Machine learning techniques are used to create many strategies. Attribute selection techniques are also vital in keeping the dataset’s dimensionality low. In this study, one method is provided, and the dataset is taken from the NSL-KDD dataset. In the first strategy, a filtering method called learning vector quantization (LVQ) is used, and in the second strategy, a dimensionality-simplifying method called PCA. The selected attributes from each technique are used for categorization before being tested against a DoS attack. This recent study shows that an LVQ-based SVM performs better than the competition in detecting threats.

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Section: Research Gap and Motivationmentioning

confidence: 99%

Construal Attacks on Wireless Data Storage Applications and Unraveling Using Machine Learning Algorithm

Kshirsagar

Manoharan²,

Alterazi

et al. 2022

Journal of Sensors

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show abstract

“…There are many researchers who have provided good overviews in the arena of IoMT. Shamsoshoara et al [ 18 ] provided security measures using PUF; Fernández-Caramés et al [ 19 ] showed the challenges of security measures; Alwarafy et al [ 20 ] presented intrusion detection using edge computing; Shakeel et al [ 21 ] showed security systems in limited scale; Al-Garadi et al [ 22 ], Arora et al [ 23 ], and Rbah et al [ 24 ] presented deep-learning based intrusion detection system; and only blockchain-based measures are presented by Sengupta et al [ 17 ], Sawadi et al [ 25 ], and Khor et al [ 26 ]. This paper presents not only the issues and challenges of IoMT application that are highlighted, but security and privacy risks with both centralized and decentralized solutions are presented.…”

Section: Contributionsmentioning

confidence: 99%

Prospect of Internet of Medical Things: A Review on Security Requirements and Solutions

Sadhu

Yanambaka

Abdelgawad

et al. 2022

Sensors

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With the widespread and increasing use of Internet-of-Things (IoT) devices in all aspects of daily life, a hopeful future for people, data, and processes is emerging. Extensive spans allow for an integrated life cycle to be created from home to enterprise. The Internet of Medical things (IoMT) forms a flourishing surface that incorporates the sensitive information of human life being sent to doctors or hospitals. These open an enormous space for hackers to utilize flaws of the IoMT network to make a profit. This creates a demand for standardizing regulations and a secure system. Though many authorities are making standards, there are some lacking in the system which makes the product vulnerable. Although many established mechanisms are present for the IoT network, there are a number of obstacles preventing its general implementation in the IoMT network. One of the adoption challenges is the IoMT devices itself, because many IoMT networks consist of battery-powered devices with constrained processing capability. A general overview of the different security integrations with IoT applications has been presented in several papers. Therefore, this paper aims to provide an overview of the IoMT ecosystem, regulations, challenges of standards, security mechanisms using cryptographic solutions, physical unclonable functions (PUF)-based solutions, blockchain, and named data networking (NDN) as well, with pros and cons.

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“…Segmentation is achieved by operating as parameters with some functions. This approach provides good accuracy, robust quality, and accuracy [ 39 ].…”

Section: Related Workmentioning

confidence: 99%

Entropy and Gaussian Filter-Based Adaptive Active Contour for Segmentation of Skin Lesions

Mustafa

Iqbal

Rana

et al. 2022

Computational Intelligence and Neuroscience

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Malignant melanoma is considered one of the deadliest skin diseases if ignored without treatment. The mortality rate caused by melanoma is more than two times that of other skin malignancy diseases. These facts encourage computer scientists to find automated methods to discover skin cancers. Nowadays, the analysis of skin images is widely used by assistant physicians to discover the first stage of the disease automatically. One of the challenges the computer science researchers faced when developing such a system is the un-clarity of the existing images, such as noise like shadows, low contrast, hairs, and specular reflections, which complicates detecting the skin lesions in that images. This paper proposes the solution to the problem mentioned earlier using the active contour method. Still, seed selection in the dynamic contour method has the main drawback of where it should start the segmentation process. This paper uses Gaussian filter-based maximum entropy and morphological processing methods to find automatic seed points for active contour. By incorporating this, it can segment the lesion from dermoscopic images automatically. Our proposed methodology tested quantitative and qualitative measures on standard dataset dermis and used to test the proposed method’s reliability which shows encouraging results.

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A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects

Cited by 42 publications

References 139 publications

Construal Attacks on Wireless Data Storage Applications and Unraveling Using Machine Learning Algorithm

Construal Attacks on Wireless Data Storage Applications and Unraveling Using Machine Learning Algorithm

Prospect of Internet of Medical Things: A Review on Security Requirements and Solutions

Entropy and Gaussian Filter-Based Adaptive Active Contour for Segmentation of Skin Lesions

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