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Correction: A GAN-Based Approach for enhancing security in satellite based IoT networks using MPI enabled HPC.

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A hybrid AI based framework for enhancing security in satellite based IoT networks using high performance computing

Syed Zubair Ahmad1, Farhan Qamar2

  • 1Computer Engineering Department, UET Taxila, Rawalpindi, Punjab, 47050, Pakistan. szahmaduet@gmail.com.

Scientific Reports
|December 27, 2024
PubMed
Summary

A novel Embedded Hybrid Deep Learning (EHID) technique enhances Internet of Things (IoT) security for High-Performance Computing (HPC) clouds, especially over satellite links. This AI-driven approach improves intrusion detection accuracy and efficiency for connected IoT devices.

Keywords:
Deep learning (DL)Embedded hybrid deep learning-based intrusion detection technique (EHID)High-performance computing (HPC) cloudsIntrusion detection systems (IDS)IoTMachine learning (ML)Satellite communication

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Cloud Computing
  • Internet of Things (IoT)

Background:

  • Rapid expansion of IoT devices and cloud computing increases security vulnerabilities.
  • Satellite-based IoT-HPC cloud architectures introduce unique security challenges.
  • Traditional Intrusion Detection Systems (IDS) are often ineffective for resource-constrained IoT devices and large data volumes.

Purpose of the Study:

  • To introduce a novel Embedded Hybrid Deep Learning-based intrusion detection technique (EHID) for IoT devices connected to HPC clouds via satellite.
  • To address the limitations of existing IDS in detecting threats in complex IoT-HPC environments.
  • To improve the accuracy and efficiency of threat detection while minimizing computational overhead.

Main Methods:

  • Developed an Embedded Hybrid Deep Learning (EHID) technique integrating two Deep Learning (DL) algorithms.
  • Modified conventional IDS architecture to leverage the processing power of HPC cloud servers.
  • Trained and evaluated the EHID model on the Edge-IIoTset Cyber Security Dataset for IoT & IIoT.

Main Results:

  • The EHID technique effectively detects 14 types of threats with improved accuracy and reduced false positives.
  • Achieved efficient computational performance, considering processing time and trainable parameters.
  • Demonstrated the system's capability for real-time intrusion monitoring and detection in IoT-HPC environments.

Conclusions:

  • The proposed EHID offers a robust and efficient solution for securing satellite-based IoT-HPC cloud architectures.
  • Hybrid deep learning approach combined with HPC cloud resources enhances cybersecurity for IoT networks.
  • EHID provides a promising advancement in real-time threat detection for the evolving landscape of connected devices.