A Security Framework for Internet of Things

Bouij-Pasquier, Imane; Kalam, Anas Abou El; Ouahman, Abdellah Ait; Montfort, Mina De

doi:10.1007/978-3-319-26823-1_2

Cited by 31 publications

(13 citation statements)

References 18 publications

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“…There are a number of reference architectures for IoT applications (Babar et al, 2011;Bouij-Pasquier et al, 2015). It remains to be seen if general models of IoT applications can be developed.…”

Section: Discussionmentioning

confidence: 99%

“…There are many aspects of IT-Security in the Internet of Things, such as securing the transmission of data over the internet to provide confidentiality and integrity, or using authentication and authorization to provide integrity and availability (Babar et al 2011, Chethan et al 2016, Bouij-Pasquier et al 2015. This paper focuses on a different aspect: How can an application itself be made as secure as possible?…”

Section: Introductionmentioning

confidence: 99%

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An Architecture for Reliable Industry 4.0 Appliances

Hänisch¹

2018

AJTE

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Architecture for Reliable Industry 4.0 Appliances

Hänisch¹

2018

AJTE

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“…Secondly, in addition to the IoT world, it is also a typical case in the AI domain. Thirdly, it presents an active research field [47], and last but not least it is becoming a necessity in order to efficiently deal with the massive growth of urbanization that is estimated to reach 66% by 2050 [48].…”

Section: B the Algorithmmentioning

confidence: 99%

Machine Learning based Access Control Framework for the Internet of Things

Outchakoucht¹,

Kalam²,

Es-Samaali³

et al. 2020

IJACSA

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The main challenge facing the Internet of Things (IoT) in general, and IoT security in particular, is that humans have never handled such a huge amount of nodes and quantity of data. Fortunately, it turns out that Machine Learning (ML) systems are very effective in the presence of these two elements. However, can IoT devices support ML techniques? In this paper, we investigated this issue and proposed a twofold contribution: a thorough study of the IoT paradigm and its intersections with ML from a security perspective; then, we actually proposed a holistic ML-based framework for access control, which is the defense head of recent IT systems. In addition to learning techniques, this second pillar was based on the organization and attribute concepts to avoid role explosion problems and applied to a smart city case study to prove its effectiveness.

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“…Néanmoins, les noeuds limités de l'IdO ne supporte pas systématiquement les technologies utilisées par PolyOrBAC (par exemple, les services Web basés sur SOA). Pour remédier à cela, SmartOrBAC [22,23] a essayé de réajuster OrBAC afin de garantir les exigences de l'IdO. Pour ce faire, les auteurs se sont basés sur une architecture hybride et ont introduit une couche intermédiaire moins contrainte capable de gérer les objets par clusters ; sauf qu'en fin de compte, SmartOrBAC ne fournit pas d'outils ou de mécanismes légers pour réduire la complexité de l'OrBAC afin qu'il puisse être maintenu par les dispositifs contraints utilisés dans les environnements IdO.…”

Section: Contrôle D'accèsunclassified

Apprentissage par Renforcement et Blockchain : Nouvelle approche pour sécuriser l’IoT

Outchakoucht¹,

Es-Samaali²,

Kalam³

et al. 2019

IdO

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La sécurité est un des sérieux problèmes qui menace le développement de l'internet des objets. Cependant, cette mission devient plus complexe dans les environnements IoT vu qu'ils ont des exigences intrinsèques supplémentaires telles que l'hétérogénéité, les capacités limitées de stockage et de calcul ainsi que le grand nombre de dispositifs. Pour remédier à ce problème, cet article propose un processus inspiré du concept de l'émergence visant à tirer profit de ce grand nombre d'objets intelligents et à en extraire les caractéristiques significatives que nous ne pouvons pas capter dans les systèmes avec un petit nombre. Le papier propose ensuite un framework de contrôle d'accès dédié aux environnements IoT basé sur trois notions de base : Réseaux de Blockchain, systèmes de réputation et algorithmes d'apprentissage par renforcement. ABSTRACT. Securing the IoT world in not a luxury task; it is even a matter of urgency given this exponential growth of IoT market. In fact, one can easily imagine the catastrophic damages of an attack in the field of e-Health or in the smart cities and critical infrastructures management. That being said, serious problems derived from these constrained environments block the proposal of pertinent solutions. This paper is a contribution step in this direction. To address these problems, we expose a global framework inspired from the concept of emergence in order to take advantage of this large number of devices and extract the "emergent" characteristics that are nonexistent in smaller systems. The framework is built on top of two pillars: Blockchain as architecture and Reinforcement Learning as processing toolkit.

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A Security Framework for Internet of Things

Cited by 31 publications

References 18 publications

An Architecture for Reliable Industry 4.0 Appliances

An Architecture for Reliable Industry 4.0 Appliances

Machine Learning based Access Control Framework for the Internet of Things

Apprentissage par Renforcement et Blockchain : Nouvelle approche pour sécuriser l’IoT

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