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MANAGING SECURITY IN IOT BY APPLYING THE DEEP NEURAL NETWORK-BASED SECURITY FRAMEWORK

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dc.contributor.authorHaddad N.M.en_US
dc.contributor.authorSalih H.S.en_US
dc.contributor.authorShukur B.S.en_US
dc.contributor.authorAbd S.K.en_US
dc.contributor.authorAli M.H.en_US
dc.contributor.authorMalik R.Q.en_US
dc.contributor.authorid58097036500en_US
dc.contributor.authorid57216502144en_US
dc.contributor.authorid56208747000en_US
dc.contributor.authorid56516784600en_US
dc.contributor.authorid57190250008en_US
dc.contributor.authorid58096872000en_US
dc.date.accessioned2023-05-29T09:38:47Z
dc.date.available2023-05-29T09:38:47Z
dc.date.issued2022
dc.description.abstractSecurity issues and Internet of Things (IoT) risks in several areas are growing steadily with the increased usage of IoT. The systems have developed weaknesses in computer and memory constraints in most IoT operating systems. IoT devices typically cannot operate complicated defense measures because of their poor processing capabilities. A shortage of IoT ecosystems is the most critical impediment to developing a secured IoT device. In addition, security issues create several problems, such as data access control, attacks, vulnerabilities, and privacy protection issues. These security issues lead to affect the originality of the data that cause to affects the data analysis. This research proposes an AI-based security method for the IoT environment (AI-SM-IoT) system to overcome security problems in IoT. This design was based on the edge of the network of AI-enabled security components for IoT emergency preparedness. The modules presented detect, identify and continue to identify the phase of an assault life span based on the concept of the cyberspace killing chain. It outlines each long-term security in the proposed framework and proves its effectiveness in practical applications across diverse threats. In addition, each risk in the borders layer is dealt with by integrating artificial intelligence (AI) safety modules into a separate layer of AI-SM-IoT delivered by services. It contrasted the system framework with the previous designs. It described the architectural freedom from the base areas of the project and its relatively low latency, which provides safety as a service rather than an embedded network edge on the internet of-things design. It assessed the proposed design based on the administration score of the IoT platform, throughput, security, and working time � 2022, Authors. This is an open access article under the Creative Commons CC BY licenseen_US
dc.description.natureFinalen_US
dc.identifier.doi10.15587/1729-4061.2022.269221
dc.identifier.epage50
dc.identifier.issue9-120
dc.identifier.scopus2-s2.0-85147779414
dc.identifier.spage38
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85147779414&doi=10.15587%2f1729-4061.2022.269221&partnerID=40&md5=0e23fbb40a043edef6368b2c9527fab5
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27023
dc.identifier.volume6
dc.publisherTechnology Centeren_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleEastern-European Journal of Enterprise Technologies
dc.titleMANAGING SECURITY IN IOT BY APPLYING THE DEEP NEURAL NETWORK-BASED SECURITY FRAMEWORKen_US
dc.typeArticleen_US
dspace.entity.typePublication
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