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Related Concept Videos

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Machine Learning-Based Security Solutions for IoT Networks: A Comprehensive Survey.

Abdullah Alfahaid1, Easa Alalwany1, Abdulqader M Almars1

  • 1Department of Computer Science, College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia.

Sensors (Basel, Switzerland)
|September 19, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) enhances Internet of Things (IoT) security by detecting threats and mitigating risks. This survey reviews ML-driven solutions (2020-2024), identifying limitations and future research for robust IoT cybersecurity.

Keywords:
IoT securityadversarial attacksanomaly detectioncybersecuritydeep learning (DL)federated learning (FL)internet of things (IoT)intrusion detection systems (IDSs)machine learning (ML)privacy protection

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Area of Science:

  • Cybersecurity and Network Engineering
  • Artificial Intelligence and Machine Learning

Background:

  • The Internet of Things (IoT) offers transformative potential across industries but faces significant security challenges, including data breaches, privacy violations, and cyber threats.
  • Effective security mechanisms are crucial for IoT adoption, with machine learning (ML) showing promise for anomaly and intrusion detection.

Purpose of the Study:

  • To provide a comprehensive review of machine learning-driven Internet of Things (IoT) security solutions implemented between 2020 and 2024.
  • To systematically classify ML techniques for IoT security, analyze threat taxonomies, and evaluate current solutions' effectiveness, scalability, and privacy preservation.

Main Methods:

  • A systematic survey of recent literature (2020-2024) focusing on machine learning applications in IoT security.
  • Examination of supervised, unsupervised, and reinforcement learning, alongside advanced techniques like deep learning (DL), ensemble learning (EL), federated learning (FL), and transfer learning (TL).
  • Classification of ML techniques by application, threat taxonomy, and critical evaluation of existing solutions.

Main Results:

  • ML, including DL, EL, FL, and TL, demonstrates significant potential in enhancing IoT security through anomaly and intrusion detection.
  • Current ML solutions face limitations such as high computational costs, vulnerability to adversarial attacks, and challenges in interpretability.
  • A taxonomy of IoT security threats and a classification of ML techniques for security applications were developed.

Conclusions:

  • Machine learning is a critical tool for advancing IoT security, offering adaptive and intelligent threat mitigation strategies.
  • Future research should focus on privacy-preserving ML, explainable AI (XAI), and edge-based security frameworks to address current limitations.
  • Developing robust, intelligent, and adaptive cybersecurity models is essential for securing future IoT ecosystems against evolving threats.