Generative Artificial Intelligence (Generative AI) offers a paradigm shift to the way robots perceive and interact with data. Generative AI approaches aim to produce new data samples that closely match the original dataset, in contrast to standard AI models that concentrate on tasks such as categorisation or prediction. This ability has broad implications for a variety of fields, including network security. New generation of network intrusion defense brought forth by Generative AI is revolution using the area of network security. In this work, investigates the revolutionary potential of Generative AI in network intrusion detection, with the goal of developing a proactive and adaptable cyber protection mechanism. In this work the use of Generative AI use for identifying network intrusions, using the NSL-KDD dataset. The work begins with meticulous data preprocessing using handling missing values, encoding, feature scaling, and feature extraction to enhance the dataset and then focuses on using Generative AI for intrusion detection. In this work many models have been used like SVM, AE, DAE, and GANs, and in this GANs has the best accuracy with 91.12% compared with other models.
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