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Top-Down Machine Learning-Based Architecture for Cyberattacks Identification and Classification in IoT Communication

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A new architecture uses machine learning for Internet of Things (IoT) security, effectively detecting and classifying cyber-attacks. This system engineering approach enhances IoT network defense against evolving threats.

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Internet of Things (IoT)

Background:

  • The proliferation of smart, interconnected devices in the Internet of Things (IoT) has led to increased vulnerabilities and sophisticated cyber-attacks.
  • Attacks on IoT systems aim to compromise sensitive data, disrupt operations, and extort users, necessitating advanced security measures.

Approach:

  • A novel, generic top-down architecture for intrusion detection and classification in IoT networks is proposed.
  • The architecture integrates system engineering principles with machine learning, featuring distinct subsystems for feature engineering (FE), feature learning (FL), and detection and classification (DC).

Key Points:

  • The system employs deep learning models for high-accuracy detection and classification of both known and subtly mutated IoT cyber-attacks.
  • It is designed to be adaptable to various IoT cybersecurity datasets, including CICIDS and MQTT.
  • The architecture leverages system engineering (SE) techniques for a robust and systematic approach to IoT security.

Conclusions:

  • The proposed architecture offers an efficient and systematic solution for enhancing the cybersecurity of IoT networks.
  • This integrated approach, combining machine learning and system engineering, achieves high-performance trajectories in detecting and classifying IoT intrusions.