Managing security in IoT by applying the deep neural network-based security framework




Internet of things, security, artificial intelligence, fog computing, wireless sensors, security threats


Security 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

Author Biographies

Nabeel Mahdy Haddad, Misan University

PhD, Lecturer

Collage of Education

Hayder Sabah Salih, Iraqi Ministry of Higher Education and Scientific Research

PhD, Director/Head of the Scientific Affairs Section

Department of Private Education

Ban Salman Shukur, Economic Sciences University

PhD, Lecturer

Department of Computer Science

Baghdad College

Sura Khalil Abd, Universiti Tenaga Nasional; Dijlah University College

Doctor of Network and Communication Systems Engineering

Department of Computer Science and Information Technology

Department of Computer Engineering Techniques

Mohammed Hasan Ali, Imam Ja'afar Al-sadiq University

PhD, Lecturer

Department of Computer Systems and Software Engineering

Rami Qais Malik, Al-Mustaqbal University College

PhD, Lecturer

Department of Medical Instrumentation Techniques Engineering


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Managing security in IoT by applying the deep neural network-based security framework




How to Cite

Haddad, N. M., Salih, H. S., Shukur, B. S., Abd, S. K., Ali, M. H., & Malik, R. Q. (2022). Managing security in IoT by applying the deep neural network-based security framework. Eastern-European Journal of Enterprise Technologies, 6(9 (120), 38–50.



Information and controlling system