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

Syed Zubair Ahmad1, Farhan Qamar1, Hamdan Alshehri2

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This study introduces DLGAN, a Deep Learning-based Generative Adversarial Network, to enhance security for satellite Internet of Things (IoT) networks. DLGAN effectively detects various cyberattacks, improving data security in critical remote applications.

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Satellite Communications

Background:

  • Satellite Internet of Things (IoT) networks are crucial for critical applications but face security vulnerabilities due to diverse technologies and limited device capacity.
  • Secure data transmission is a primary concern when connecting IoT systems to High-Performance Computing (HPC) clouds via satellite links.

Purpose of the Study:

  • To propose a novel security framework, DLGAN (Deep Learning-based Generative Adversarial Network), specifically for satellite-based IoT environments.
  • To address the challenge of skewed datasets in cybersecurity by generating synthetic attack data using Generative Adversarial Networks (GANs).
  • To enable scalable parallel processing of large IoT data volumes on HPC systems using the Message Passing Interface (MPI).

Main Methods:

  • Utilized Convolutional Neural Networks (CNNs) for real-time anomaly detection.
  • Employed Generative Adversarial Networks (GANs) to create realistic synthetic attack data.
  • Implemented a generator/discriminator mechanism for classifying network traffic as benign or malicious.
  • Optimized the DLGAN model for HPC systems with AI-enabled GPUs for efficient parallel processing.

Main Results:

  • The DLGAN framework demonstrated enhanced detection accuracy for 14 different attack types.
  • Achieved significant reductions in model training time.
  • Showcased excellent scalability with large data volumes, suitable for real-time security operations.
  • Maintained low computational costs while providing fast and accurate threat detection.

Conclusions:

  • Integrating deep learning with HPC-based distributed environments offers an efficient and dynamic defense for IoT networks.
  • The DLGAN solution provides a scalable, efficient, and attack-resilient mechanism for securing satellite-based IoT infrastructures.
  • The framework effectively addresses the unique security challenges of satellite IoT networks.