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Area of Science:

  • Computer Science
  • Network Security
  • Data Analytics

Background:

  • The Internet of Things (IoT) connects smart devices, creating vast networks but facing significant big data and security challenges.
  • Malicious nodes in IoT networks compromise data integrity and user security by transmitting inaccurate information.
  • Existing IoT architectures require robust data analytics and security models to ensure reliable communication.

Purpose of the Study:

  • To propose a novel data analytics model and a self-organizing architecture for IoT networks.
  • To develop an integrated security model for IoT, focusing on authentication, detection, and prediction mechanisms.
  • To enhance the security and performance of smart environmental monitoring systems within IoT frameworks.

Main Methods:

  • Development of a data analytics model tailored for IoT environments.
  • Design of a self-organizing architecture to manage complex IoT layers and processes.
  • Implementation of a security model incorporating authentication, detection, and prediction algorithms.
  • Evaluation using machine learning algorithms to assess accuracy, sensitivity, and specificity.

Main Results:

  • The proposed model effectively addresses big data handling and security issues in IoT networks.
  • Enhanced protection against Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks.
  • Improved data reliability and network security for smart environmental monitoring applications.
  • Demonstrated high accuracy, sensitivity, and specificity in model evaluations.

Conclusions:

  • The integrated data analytics and security model provides a robust solution for IoT challenges.
  • The proposed architecture and security mechanisms significantly enhance the resilience of IoT networks.
  • This research contributes to the secure and efficient operation of smart environmental monitoring systems.