Towards a Gateway-based Context-Aware and Self-Adaptive Security Management Model for IoT-based eHealth Systems

Aman, Waqas; Kausar, Firdous

doi:10.14569/ijacsa.2019.0100137

Cited by 8 publications

(9 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many researchers in the last decade have addressed the literature concerning IoT privacy and security. It has been observed that addressing the security issues was primarily focused [11]; however, more attention needs to be paid to the protection of end-user privacy in modern electronic health care systems [12], [13]. In the modern era, the health care system is transformed into a new domain by incorporating advanced digital technologies such as IoT, high computing devices to store and process data, personal health records, and more.…”

Section: Security and Privacy Of Iomtmentioning

confidence: 99%

Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey

Ali¹,

Naeem²,

Tariq³

et al. 2022

Preprint

View full text Add to dashboard Cite

Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using internet of medical things (IoMT) devices. However, due to the centralized training approach of artificial intelligence (AI), mobile and wearable IoMT devices raise privacy issues concerning the information communicated between hospitals and end-users. The information conveyed by the IoMT devices is highly confidential and can be exposed to adversaries. In this regard, federated learning (FL), a distributive AI paradigm, has opened up new opportunities for privacy-preservation in IoMT without accessing the confidential data of the participants. Further, FL provides privacy to endusers as only gradients are shared during training. For these specific properties of FL, in this paper, we present privacyrelated issues in IoMT. Afterward, we present the role of FL in IoMT networks for privacy preservation and introduce some advanced FL architectures incorporating deep reinforcement learning (DRL), digital twin, and generative adversarial networks (GANs) for detecting privacy threats. Moreover, we present some practical opportunities of FL in IoMT. In the end, we conclude this survey by providing open research challenges for FL that can be used in future smart healthcare systems.

show abstract

Section: Security and Privacy Of Iomtmentioning

confidence: 99%

Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey

Ali¹,

Naeem²,

Tariq³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…1) The adaptation process: The adaption process in an adaptive S&P system can be typically divided into three main functionalities or phases [8,13,16,17]: Monitoring, Analysis, and Adaptation. They enable a system to adapt the S&P configurations based on the dynamic changes in the IoT ehealth infrastructure in an automated manner.…”

Section: B the Proposed Assessment Frameworkmentioning

confidence: 99%

“…Monitoring also observe external factors such as those related to the monitored devices, users, network and applications. Therefore, it attempts to collect data essential for a context-aware analysis and adaptation [8,16,17].…”

Section: B the Proposed Assessment Frameworkmentioning

confidence: 99%

“…Regardless of the availability of multiple studies and solutions on adaptive S&P, for instance, [8,9,10] emphasize on the need to assess their effectiveness. To validate the efficacy, it is vital to recognize and evaluate the essential factors www.ijacsa.thesai.org necessary for an adaptive S&P solutions and the extent to which they are employed as per adaptation and architectural needs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Assessment Framework for Evaluating Adaptive Security and Privacy Solutions for IoT e-Health Applications

Aman¹,

Mohammed²

2022

IJACSA

View full text Add to dashboard Cite

There exist numerous adaptive security and privacy (S&P) solutions to manage potential threats at runtime. However, there is a lack of a comprehensive assessment framework that can holistically validate their effectiveness. Existing Adaptive S&P assessment efforts either focus on privacy or security in general, or are focused on specific adaptive S&P attributes, e.g. authentication, and, at certain times, disregards the architecture in which they should be comprehended. In this paper, we propose a holistic assessment framework for evaluating adaptive S&P solutions for IoT e-health. The framework utilizes a proposed classification of essential attributes necessary to be recognized, evaluated, and incorporated for the effectiveness of adaptive S&P solutions for the most common IoT architectures, fog-based and cloud/server-based architectures. As opposed to the existing related work, the classification comprehensively covers all the major classes of essential attributes, such as S&P objectives, contextual factors, adaptation action aptitude, and the system's self-* properties. Using this classification, the framework assists to evaluate the existence of a given attribute with respect to the adaptation process and in the context of the architectural layers. Therefore, it stresses the importance of where an essential attribute should be realized in the adaptation phases and in the architecture for an adaptive S&P solution to be effective. We have also presented a comparison of the proposed assessment framework with existing related frameworks and have shown that it exhibits substantial completeness over the existing works to assess the feasibility of a given adaptive S&P solution.

show abstract

“…Context-aware solutions for multimodal data compression, in-network processing, and edge-event detection in multi-edge cloud-based systems [56] are promising. A context-aware and self-adaptive model serving as an IoT security management platform that can autonomously track, interpret, and respond to a host of security contexts are proposed in [124] . That model harnesses the benefits of fog computing and provides flexibility and open networking to handle any smart device.…”

Section: Smart and Connected Health: Review Classification And Comparisonmentioning

confidence: 99%

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

et al. 2021

View full text Add to dashboard Cite

Cardio Vascular Diseases (CVD) is the leading cause of death globally and is increasing at an alarming rate, according to the American Heart Association's Heart Attack and Stroke Statistics-2021. This increase has been further exacerbated because of the current coronavirus (COVID-19) pandemic, thereby increasing the pressure on existing healthcare resources. Smart and Connected Health (SCH) is a viable solution for the prevalent healthcare challenges. It can reshape the course of healthcare to be more strategic, preventive, and custom-designed, making it more effective with value-added services. This research endeavors to classify state-of-the-art SCH technologies via a thorough literature review and analysis to comprehensively define SCH features and identify the enabling technology-related challenges in SCH adoption. We also propose an architectural model that captures the technological aspect of the SCH solution, its environment, and its primary involved stakeholders. It serves as a reference model for SCH acceptance and implementation. We reflected the COVID-19 case study illustrating how some countries have tackled the pandemic differently in terms of leveraging the power of different SCH technologies, such as big data, cloud computing, Internet of Things, artificial intelligence, robotics, blockchain, and mobile applications. In combating the pandemic, SCH has been used efficiently at different stages such as disease diagnosis, virus detection, individual monitoring, tracking, controlling, and resource allocation. Furthermore, this review highlights the challenges to SCH acceptance, as well as the potential research directions for better patient-centric healthcare.

show abstract

Towards a Gateway-based Context-Aware and Self-Adaptive Security Management Model for IoT-based eHealth Systems

Cited by 8 publications

References 20 publications

Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey

Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey

A Comprehensive Assessment Framework for Evaluating Adaptive Security and Privacy Solutions for IoT e-Health Applications

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

Contact Info

Product

Resources

About