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Smart Cybersecurity Framework for IoT-Empowered Drones: Machine Learning Perspective.

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This study introduces a hybrid machine learning approach to secure drone networks (NoD) against cyber threats. The proposed method enhances network security and privacy, ensuring reliable drone operations.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Robotics

Background:

  • Advancements in small-sized drones enable new applications via the Internet of Things (IoT).
  • Drone networks (NoD) face significant privacy and security risks due to inherent design flaws.
  • A secure network is crucial for the optimal performance and reliability of drone operations.

Purpose of the Study:

  • To investigate current privacy and security challenges in drone networks (NoD).
  • To propose a novel, AI-inspired technique for enhancing NoD cybersecurity.
  • To ensure drone networks are protected against interception and intrusion.

Main Methods:

  • A hybrid machine learning (ML) technique combining logistic regression and random forest was employed.
  • The ML model was utilized for classifying data instances to maximize efficacy.
  • The proposed technique integrates artificial intelligence to mitigate cybersecurity vulnerabilities.

Main Results:

  • The hybrid ML technique demonstrated enhanced performance in securing drone networks.
  • Temporal efficacy was recorded at 34.56 seconds.
  • Statistical measures showed high precision (97.68%), accuracy (98.58%), recall (98.59%), F-measure (99.01%), reliability (94.69%), and stability (0.73).

Conclusions:

  • The proposed AI-inspired technique effectively mitigates cybersecurity vulnerabilities in drone networks (NoD).
  • The method ensures a protected and secure network environment for drone operations.
  • The validated results confirm the technique's efficacy and robustness.