A Multi-Factor Access Control and Ownership Transfer Framework for Future Generation Healthcare Systems

Aski, Vidyadhar; Dhaka, Vijaypal Singh; Kumar, Sunil; Parashar, Anubha; Ladagi, Akshata

doi:10.1109/pdgc50313.2020.9315840

Cited by 4 publications

(5 citation statements)

References 20 publications

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“…The T-CAD DDoS defense system has outperformed many existing thresholds and entropy-based DDoS detection approaches in simulation testing. In the research introduced by [27,28], DDoS attack's various models, as well as a timeline of defense measures and improvements to counter them, are all reviewed. Using the MapReduce programming architecture, a new DDoS attack detection mechanism has been built.…”

Section: Related Workmentioning

confidence: 99%

Detection of Distributed Denial of Service (DDoS) Attacks in IOT Based Monitoring System of Banking Sector Using Machine Learning Models

et al. 2022

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Cyberattacks can trigger power outages, military equipment problems, and breaches of confidential information, i.e., medical records could be stolen if they get into the wrong hands. Due to the great monetary worth of the data it holds, the banking industry is particularly at risk. As the number of digital footprints of banks grows, so does the attack surface that hackers can exploit. This paper aims to detect distributed denial-of-service (DDOS) attacks on financial organizations using the Banking Dataset. In this research, we have used multiple classification models for the prediction of DDOS attacks. We have added some complexity to the architecture of generic models to enable them to perform well. We have further applied a support vector machine (SVM), K-Nearest Neighbors (KNN) and random forest algorithms (RF). The SVM shows an accuracy of 99.5%, while KNN and RF scored an accuracy of 97.5% and 98.74%, respectively, for the detection of (DDoS) attacks. Upon comparison, it has been concluded that the SVM is more robust as compared to KNN, RF and existing machine learning (ML) and deep learning (DL) approaches.

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Section: Related Workmentioning

confidence: 99%

Detection of Distributed Denial of Service (DDoS) Attacks in IOT Based Monitoring System of Banking Sector Using Machine Learning Models

et al. 2022

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“…Services : [ 26 , 33 , 35 , 37 , 38 , 39 , 40 , 41 , 44 , 46 , 48 , 52 , 57 , 65 , 66 , 69 , 72 , 81 , 84 , 85 , 86 , 87 , 93 , 97 , 98 , 103 , 106 , 107 , 111 , 113 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 ]…”

Section: Taxonomy and Trendsunclassified

“…A security framework for edge-computing [ 118 ] has been connected to healthcare systems. It utilizes multi-factor access control and ownership transfer mechanism to create an authentication system.…”

Section: Details On Goal-based Taxonomy Perspectivesmentioning

confidence: 99%

“…To summarize, we identified 57 U2M approaches [ 11 , 14 , 18 , 20 , 22 , 24 , 26 , 29 , 31 , 32 , 35 , 39 , 40 , 42 , 44 , 46 , 48 , 50 , 51 , 59 , 60 , 62 , 64 , 68 , 70 , 73 , 75 , 76 , 77 , 78 , 80 , 82 , 83 , 85 , 92 , 93 , 94 , 98 , 100 , 102 , 104 , 105 , 106 , 107 , 112 , 118 , 119 , 122 , 126 , 128 , 132 , 133 , 135 , 136 , 137 …”

Section: Details On Goal-based Taxonomy Perspectivesunclassified

“…However, it is still difficult to predict which ideas might be adopted widely. Although a significant amount of research is focused on adoption of existing technologies with 63 of identified publications [ 15 , 16 , 20 , 21 , 22 , 23 , 26 , 28 , 30 , 32 , 35 , 39 , 40 , 43 , 45 , 49 , 50 , 51 , 55 , 57 , 60 , 62 , 63 , 66 , 67 , 69 , 71 , 72 , 73 , 76 , 78 , 82 , 84 , 92 , 94 , 95 , 96 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 107 , 112 , 113 , 116 , 118 , 120 , 121 , 123 <...>…”

Section: Details On Goal-based Taxonomy Perspectivesmentioning

confidence: 99%

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Systematic Review of Authentication and Authorization Advancements for the Internet of Things

Trnka

Abdelfattah

Shrestha

et al. 2022

Sensors

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Technologies for the Internet of Things (IoT) are maturing, yet no common standards dictate their direction, leaving space for a plethora of research directions and opportunities. Among the most important IoT topics is security. When we design a robust system, it is important to know the available options for facing common tasks related to access control, authentication, and authorization. In this review, we systematically analyze 1622 peer-reviewed publications from October 2017 to December 2020 to find the taxonomy of security solutions. In addition, we assess and categorize current practices related to IoT security solutions, commonly involved technologies, and standards applied in recent research. This manuscript provides a practical road map to recent research, guiding the reader and providing an overview of recent research efforts.

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SOK: Evaluating Privacy and Security Vulnerabilities of Patients’ Data in Healthcare

Tazi

Dykstra²,

Rajivan

et al. 2022

Lecture Notes in Computer Science

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Sensitive information is intrinsically tied to interactions in healthcare, and its protection is of paramount importance for achieving high-quality patient outcomes. Research in healthcare privacy and security is predominantly focused on understanding the factors that increase the susceptibility of users to privacy and security breaches. To understand further, we systematically review 26 research papers in this domain to explore the existing user studies in healthcare privacy and security. Following the review, we conducted a card-sorting exercise, allowing us to identify 12 themes integral to this subject such as "Data Sharing," "Risk Awareness," and "Privacy." Further to the identification of these themes, we performed an in-depth analysis of the 26 research papers report on the insights into the discourse within the research community about healthcare privacy and security, particularly from the user perspective. MotivationSecurity and privacy integration in the healthcare domain is essential to protect patients' data [12], considering medical records include sensitive health and personal information. The healthcare industry is often a prime target for cybercriminals considering that these data sets could contain a plethora of sensitive information such as social security numbers, birth dates, employment information, emergency contacts, and insurance and billing data; these data are also notoriously difficult to monitor or safeguard after a breach [23]. Furthermore, healthcare data are lucrative on the black market. Sahi et al. noted

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A Multi-Factor Access Control and Ownership Transfer Framework for Future Generation Healthcare Systems

Cited by 4 publications

References 20 publications

Detection of Distributed Denial of Service (DDoS) Attacks in IOT Based Monitoring System of Banking Sector Using Machine Learning Models

Detection of Distributed Denial of Service (DDoS) Attacks in IOT Based Monitoring System of Banking Sector Using Machine Learning Models

Systematic Review of Authentication and Authorization Advancements for the Internet of Things

SOK: Evaluating Privacy and Security Vulnerabilities of Patients’ Data in Healthcare

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