Intrusion Detection in Cybersecurity Using Generative Adversarial Networks and Deep Learning

Networks require IDS systems for their cybersecurity protection to defend against unauthorized access and both malicious activities and cyberattacks. Traditional IDS systems face difficulties when detecting sophisticated and evolving threats because of increasing cyberattack complexity. The research focuses on combining Generative Adversarial Networks (GANs) and deep learning methods to boost cyber security intrusion detection functionality. The synthetic dataproducing mechanism of Generative Adversarial Networks utilizes generator-discriminator models which produce realistic intrusion pattern simulations for better detection model training. Through deep learning methodology patterns analysis the convolutional neural networks (CNNs) along with recurrent neural networks (RNNs) deliver reliable solutions for anomaly detection and classification. This proposed framework resolves issues associated with unbalanced datasets as well as it provides real-time identification capabilities and responds dynamically to changing cybersecurity threats. Public cybersecurity datasets reveal how GAN-augmented deep learning systems perform superior to classic IDS regarding zero-day attack detection while decreasing false alarm rates. This research introduces GANs with deep learning as an innovative intrusion detection solutions which delivers scalable, productive and proactive cybersecurity solutions. The work develops for intelligent IDS more advanced capabilities as well as establishes foundational framework for future automated IDS.

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