A Brain-Inspired Trust Management Model to Assure Security in a Cloud Based IoT Framework for Neuroscience Applications

Mahmud, Mufti; Kaiser, M. Shamim; Rahman, M. Mostafizur; Rahman, Muhammad Arifur; Shabut, Antesar M.; Mamun, Shamim Al; Hussain, Amir

doi:10.1007/s12559-018-9543-3

Cited by 160 publications

(55 citation statements)

References 50 publications

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“…An interoperable ecosystem consisting of diverse devices, applications, and back-end systems is essential for successful architectural design of an IoHT framework which will ensure undisrupted information flow for accurate and timely decision making [85], [86]. A conceptual overview of the proposed IoHT ecosystem is shown in Fig.…”

Section: Ecosystemmentioning

confidence: 99%

Toward a Heterogeneous Mist, Fog, and Cloud-Based Framework for the Internet of Healthcare Things

Asif-Ur-Rahman

Afsana

Mahmud

et al. 2019

IEEE Internet Things J.

Self Cite

180

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Rapid developments in the fields of information and communication technology and microelectronics allowed seamless interconnection among various devices letting them to communicate with each other. This technological integration opened up new possibilities in many disciplines including healthcare and well-being. With the aim of reducing healthcare costs and providing improved and reliable services, several healthcare frameworks based on Internet of Healthcare Things (IoHT) have been developed. However, due to the critical and heterogeneous nature of healthcare data, maintaining high quality of service (QoS) -in terms of faster responsiveness and data-specific complex analytics -has always been the main challenge in designing such systems. Addressing these issues, this paper proposes a five-layered heterogeneous mist, fog, and cloud based IoHT framework capable of efficiently handling and routing (near-)real-time as well as offline/batch mode data. Also, by employing software defined networking and link adaptation based load balancing, the framework ensures optimal resource allocation and efficient resource utilization. The results, obtained by simulating the framework, indicate that the designed network via its various components can achieve high QoS, with reduced end-to-end latency and packet drop rate, which is essential for developing next generation e-healthcare systems.

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Section: Ecosystemmentioning

confidence: 99%

Toward a Heterogeneous Mist, Fog, and Cloud-Based Framework for the Internet of Healthcare Things

Asif-Ur-Rahman

Afsana

Mahmud

et al. 2019

IEEE Internet Things J.

Self Cite

180

View full text Add to dashboard Cite

show abstract

“…The authors in [14] introduce a neuro-fuzzy based brain-inspired trust management model to secure IoT devices and relay nodes, and to ensure reliable data communication between devices. The method in [14] evaluates both node behavioral trust (entity trust) and data trust. The entity trusts are computed in a distributed manner, and the social relationships between IoT devices are taken into account.…”

Section: Hybrid Trust Managementmentioning

confidence: 99%

“…The entity trusts are computed in a distributed manner, and the social relationships between IoT devices are taken into account. However, unlike our proposed scheme in this paper, the method in [14] focuses on the brain data and neuroscience-related applications.…”

Section: Hybrid Trust Managementmentioning

confidence: 99%

A trust management scheme for IoT-enabled environmental health/accessibility monitoring services

Shayesteh

Hakami

Akbari

2019

Int. J. Inf. Secur.

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One rapidly growing application of Internet of Things (IoT) is the protection of public health and wellbeing through enabling environmental monitoring services. In particular, an IoT-enabled health/accessibility monitoring service (HAMS) can be consulted by its users to query about the status of different areas so as to optimize their trip throughout a geographic region. Given the high cost associated with a vast deployment of totally trusted information sources, the IoT-enabled monitoring services also subsist on citizen engagement and on (possibly untrusted) users sensing apparatus for data collection. However, trust management becomes a key factor in the success of such services because they might be misled by malicious users through altered or fake sensor data. In this paper, we consider a monitoring service, and propose a hybrid entity/data trust computation scheme which relies on Bayesian learning to score the users (as data reporters), and on Dempster-Shafer theory (DST) for data fusion and for the computation of the trustworthiness of the data itself. In order to provide resiliency against behavioral changes, the probability masses used in DST are dynamically updated using the freshly estimated user scores as well as the contextual properties associated with the reported data. We conduct simulation experiments to evaluate the performance of our scheme. Compared to prior work, the results demonstrate superior performance in terms of accuracy and resilience against malicious behavior Behshid Shayesteh

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“…Beside the aforementioned areas, several works proposed security solutions specially designed for the IoT or D2D context with regard to confidentiality, data reliability, privacy, access control, and authentication, which are mostly surveyed in previous studies. [22][23][24][25][26][27][28][29][30][31] More related to our paper, some other survey papers review existing works focusing especially on trust models in the IoT 6,9,10,[32][33][34] or D2D 11,35,36 context. In the following, we review these works, regardless of whether they use or not social-related information.…”

Section: Related Workmentioning

confidence: 99%

T‐D2D: A trust model for service offloading in device‐to‐device communication

Movahedi

Hosseini

2019

Trans Emerging Tel Tech

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With the advances in pervasive computing, Internet of things (IoT) has gained considerable attention from both research and industrial communities. While IoT devices are able to provide computational services to other devices via device‐to‐device (D2D) communications, they are not guaranteed to be honest and collaborative. In such a context, the trust model can help to detect malicious service providers. However, malicious nodes may perform trust‐distortion attacks to mislead the trust model. They may perform on‐off attacks to remain undetected or bad‐mouth about others to make it difficult to infer if a contradictory recommendation comes from the on‐off nature of the evaluated node or dishonesty of the recommender. To address these issues, we propose T‐D2D, a lightweight trust model capable to face simultaneous trust‐distortion attacks. T‐D2D evaluates a node's nature using both short‐term and long‐term evaluation intervals to detect different types of on‐off attacks. Moreover, it keeps track of marginal misbehaving over several successive intervals to recognize the nature of suspicious on‐off nodes with light misbehaving attitude. To face bad‐mouthing attackers, T‐D2D limits its dependence on recommendations to when the direct trust is not decisive. Moreover, it evaluates the honesty of a recommender based on the correctness of its recommendations over time. Simulation results prove that T‐D2D exhibits significantly better performance than other counterparts in terms of trust level, correctness of calculated trust, percentage of selected malicious providers, total wasted execution time, and energy consumption in presence of simultaneous trust‐distortion attacks.

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A Brain-Inspired Trust Management Model to Assure Security in a Cloud Based IoT Framework for Neuroscience Applications

Cited by 160 publications

References 50 publications

Toward a Heterogeneous Mist, Fog, and Cloud-Based Framework for the Internet of Healthcare Things

Toward a Heterogeneous Mist, Fog, and Cloud-Based Framework for the Internet of Healthcare Things

A trust management scheme for IoT-enabled environmental health/accessibility monitoring services

T‐D2D: A trust model for service offloading in device‐to‐device communication

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