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The evolution in cybersecurity requires a shift in Intrusion Detection Systems to fight increased threat levels. Traditional methods fail in cases with imbalanced datasets, leading to missed detections. Introducing Particle Swarm Optimized Generative Adversarial Network (PSO-GAN) which addresses these issues, improving detection precision and credibility. With the help of NSL-KDD dataset, PSO-GAN has established the high standard for GAN-based methods compared to the others. It enhances system robustness without affecting computational efficiency, making it an essential tool for network security against emerging threats. Experimental results confirm PSO-GAN’s efficiency, making it an efficient approach in intrusion detection for cybersecurity.

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A Novel Particle Swarm Optimized GAN Framework for Network Intrusion Detection

  • 会议论文

  • 发表日期：

    2025年

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  • Bhavya Gupta,
  • Arunima Jaiswal,
  • Gaurav Indra,
  • Yashika Behl,
  • Yukti Sharma

摘要

The evolution in cybersecurity requires a shift in Intrusion Detection Systems to fight increased threat levels. Traditional methods fail in cases with imbalanced datasets, leading to missed detections. Introducing Particle Swarm Optimized Generative Adversarial Network (PSO-GAN) which addresses these issues, improving detection precision and credibility. With the help of NSL-KDD dataset, PSO-GAN has established the high standard for GAN-based methods compared to the others. It enhances system robustness without affecting computational efficiency, making it an essential tool for network security against emerging threats. Experimental results confirm PSO-GAN’s efficiency, making it an efficient approach in intrusion detection for cybersecurity.