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Machine learning and deep learning approaches in IoT.

Abqa Javed1, Muhammad Awais1, Muhammad Shoaib1

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Summary
This summary is machine-generated.

This study reviews current Internet of Things (IoT) security trends, focusing on the Internet of Medical Things (IoMT) and Internet of Vehicles (IoV). It highlights the need for intrusion prevention systems (IPS) and machine learning to secure these critical IoT applications.

Keywords:
Deep learningIPS (Intrusion Prevention System)IoMT (Internet of Medical Things)IoT (Internet of Things)IoV (Internet of Vehicles)Machine learning

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Network Security

Background:

  • The proliferation of Internet of Things (IoT) devices, particularly in critical sectors like healthcare (Internet of Medical Things - IoMT) and transportation (Internet of Vehicles - IoV), has led to an alarming increase in security threats.
  • Ensuring authentication, authorization, and data privacy for IoMT and IoV devices is paramount due to their role in real-time monitoring for individual safety.
  • The growing sophistication of security assaults necessitates advanced solutions, such as intrusion prevention systems (IPS), to safeguard these interconnected systems.

Purpose of the Study:

  • To investigate and analyze current research trends in Internet of Things (IoT) security.
  • To identify key challenges and propose future research directions in securing IoT ecosystems, with a specific focus on IoMT and IoV.

Main Methods:

  • A systematic literature review (SLR) was conducted, involving a search for relevant papers in the IoT security domain.
  • The top 50 papers were selected for in-depth analysis, and research questions were formulated based on identified problems.
  • Data was retrieved from digital archives, and a taxonomy of IoT subdomains was developed based on the review findings.

Main Results:

  • The study identified machine learning and deep learning technologies as crucial for detecting and mitigating security threats in IoMT and IoV devices.
  • A comprehensive taxonomy of IoT security subdomains was established, providing a structured overview of the research landscape.
  • Key challenges and areas requiring further investigation within IoT security were pinpointed.

Conclusions:

  • Machine learning and deep learning are essential for enhancing the security of critical IoT applications like IoMT and IoV.
  • The systematic review provides a valuable foundation for understanding the current state of IoT security research and guides future endeavors.
  • Further research is recommended to address identified security challenges and advance the robustness of IoT systems.