Developing an Intrusion Detection System using Generative Adversarial Networks (GANs) to Enhance Network Security

As the increasing reliance on digital networks and systems across various domains, cyberattacks have been a significant threat to system security and information integrity. Intrusion IDS is a critical tool in enhancing cyber security, but traditional systems have a propensity to struggle to handle new and complex patterns of attacks. This paper aims to develop an intrusion detection system based on generative adversarial networks (GANs) to enhance accuracy and efficiency of network threat detection. GANs is a novel deep learning method that contains two competing models: a generator, which generates imitated attack data mimicking Actual attacks, and a discriminator, which discriminates between actual and imitated data. With Competing interaction between the two models, very accurate attack data is produced and is used to improve the performance of IDSs. The research approach is based on building a GAN modeling with deep network layers to generate attack data that mimics actual attacks, while training the discriminator network to accurately detect this data. Such common data sets include NSL-KDD and CICIDS will be used to assess the effectiveness of the system, with performance evaluated with strict measures of precision, recall, and false alarm rate. The built the system should be able to detect dynamic and complex threats at a faster pace and superior to traditional systems. The research highlights the importance of providing a new solution to the intrusion detection problem leveraging the capabilities of artificial intelligence and deep learning, which aid to enhance network security and protection of them from modern cyber-attacks.