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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 20/2020

25.02.2020 | S.I. : Applying Artificial Intelligence to the Internet of Things

AI4SAFE-IoT: an AI-powered secure architecture for edge layer of Internet of things

verfasst von: Hamed HaddadPajouh, Raouf Khayami, Ali Dehghantanha, Kim-Kwang Raymond Choo, Reza M. Parizi

Erschienen in: Neural Computing and Applications | Ausgabe 20/2020

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Abstract

With the increasing use of the Internet of things (IoT) in diverse domains, security concerns and IoT threats are constantly rising. The computational and memory limitations of IoT devices have resulted in emerging vulnerabilities in most IoT-run environments. Due to the low processing ability, IoT devices are often not capable of running complex defensive mechanisms. Lack of an architecture for a safer IoT environment is referred to as the most important barrier in developing a secure IoT system. In this paper, we propose a secure architecture for IoT edge layer infrastructure, called AI4SAFE-IoT. This architecture is built upon AI-powered security modules at the edge layer for protecting IoT infrastructure. Cyber threat attribution, intelligent web application firewall, cyber threat hunting, and cyber threat intelligence are the main modules proposed in our architecture. The proposed modules detect, attribute, and further identify the stage of an attack life cycle based on the Cyber Kill Chain model. In the proposed architecture, we define each security module and show its functionality against different threats in real-world applications. Moreover, due to the integration of AI security modules in a different layer of AI4SAFE-IoT, each threat in the edge layer will be handled by its corresponding security module delivered by a service. We compared the proposed architecture with the existing models and discussed our architecture independence of the underlying IoT layer and its comparatively low overhead according to delivering security as service for the edge layer of IoT architecture instead of embed implementation. Overall, we evaluated our proposed architecture based on the IoT service management score. The proposed architecture obtained 84.7 out of 100 which is the highest score among peer IoT edge layer security architectures.
Vorheriger Artikel Deep neural network-based clustering technique for secure IIoT
Nächster Artikel A hybrid classifier combination for home automation using EEG signals

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Metadaten
Titel
AI4SAFE-IoT: an AI-powered secure architecture for edge layer of Internet of things
verfasst von
Hamed HaddadPajouh
Raouf Khayami
Ali Dehghantanha
Kim-Kwang Raymond Choo
Reza M. Parizi
Publikationsdatum
25.02.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 20/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-04772-3

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