A Study on Artificial Intelligence-based Security Techniques for IoT-based Systems

Mustafa Al-Tahee^1,*, Marwa s. mahdi hussin², Mohammed Jameel Alsalhy³, Hussein Alaa Diame⁴, Noor Hanoon Haroon⁵, Salem Saleh Bafjaish⁶, Mohammed Nasser Al-Mhiqani⁷

¹Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad, Iraq

²Computer Technologies Engineering, Al-Turath University College, Baghdad,Iraq

³Department Of Medical Devices Engineering Technologies, National University Of Science And Technology, Dhi Qar, Nasiriyah, Liaq

⁴Technical Computer Engineering Department, Al-Kunooze University College, Basrah, Iraq

⁵Department Of Computer Technical Engineering, Technical Engineering College, Al-Ayen University, Thi- Qar, Iraq

⁶College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar

Keele University (KU), Keele, United Kingdom, Staffordshire, ST5 5AA,

Emails: Mustafa Al-Tahee@Uoalfarahidi.Edu.Iq; Marwa.Saad@Turath.Edu.Iq; Sahi@Nust.Edu.Iq;

Hussein. Alaa@ Kunoozu.Edu. Iq; Noor@Alayen.Edu.Iq; Salem.Bafjaish@gmail.com; Almohaiqny@gmail.com

Abstract

In a recent scenario, the Internet of Things (IoT) enables the Integration of disparate home automation systems into a unified network that can be managed from a single device, such as a smartphone. Connections to the Internet that aren't secure: A lack of security standards may make the Internet of Things devices vulnerable to assault, including hacking. Though current designs may address some security concerns inherent to the Internet of Things, most solutions suffer from two significant flaws. This only addresses a single threat at the level of IoT-edge architecture and cannot be expanded to deal with new threats as misunderstood obstacles. Second, its core operations are trustworthy and seldom require additional hardware to implement the advised security measures. The AI-SM-IoT framework is a three-tiered system incorporating security components based on AI motors into every IoT stack that communicates with the network's edge. AI motors were also added as a new transmission layer. This study suggests an AI-based security method for IoT environments (AI-SM-IoT system). This concept was based on the periphery of a network of AI-enabled security components for IoT disaster preparedness. The architecture recommends three main modules: cyber threat searching, intelligent firewalls for online applications, and cybercrime information. Based on the idea of the "cyberspace killing chain," the modules given detect, identify, and continue to identify the stage of an assault life cycle. It describes each long-term security in the suggested framework and demonstrates its usefulness in applications facing various risks. A distinct layer of AI-SM-IoT services is used to deliver artificial intelligence (AI) safety modules to address each risk in the boundaries layer. The architectural freedom from the project's essential regions and comparatively low latency, which offers safety as a service rather than an embedded network edge on the Internet of Things design, contrasted with the system framework's earlier designs. Based on the administration score of the IoT platform, throughput, security, and working time, it evaluated the proposed method

Keywords: Internet of Things; Security; Artificial Intelligence; Fog Computing; Sensors