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A novel and secure attacks detection framework for smart cities industrial internet of things - 2020

Research Area:  Internet of Things

Abstract:

New trend of smart cities has changed the life with more equipped and integrated systems. Various new technologies have adopted for sustainable and improved smart cities infrastructure. Internet of Thing (IoT) is a rapidly evolving technology for sustainable and improved smart cities infrastructure that is revealing its manifestations to facilitate mankind. Numerous privileges and easily adaptable nature of the IoT applications makes it a core component of smart cities. IoT is also implemented in the industrial sector referred to as the Industrial Internet of Things (IIoT) where various diverse services related to operation technologies, manufacturing, utilities, machines monitoring have been applied to connected devices. This phenomenon also makes it susceptible to a variety of crucial security concerns that need to be addressed. IPV6 based Routing Protocol for Low Power and Lossy Networks (RPL) is an ideal choice to ensures effective data communication in resource constraint IIoT environments. By using basic concepts of genetic programming, this paper proposes a novel and secure framework to detect the presence of security threats in RPL based IoT and IIoT networks. The proposed framework possesses the capability to detect HELLO-Flood attack, Version number attack, Sinkhole attack, and Black hole attack. The performance of proposed framework is evaluated at various performance parameters including attack detection accuracy, true positive rate, false-positive rate, throughput, and end-to-end delay. Favorable results appear to support the proposed framework and makes it a best choice for RPL based IIoT environments.

Author(s) Name:  Kashif Naseer Qureshi, Shahid Saeed Rana, Awais Ahmed, Gwanggil Jeon

Journal name:  Sustainable Cities and Society

Conferrence name:  

Publisher name:  ELSEVIER

DOI:  10.1016/j.scs.2020.102343

Volume Information:  Volume 61, October 2020, 102343