AI Meets Cyber Defense: Enhancing Network Security with GAN-Driven NIDS

The field of cybersecurity has witnessed a growth in the breadth and intricacy of cyber threats due to the advancement of communication technology, which calls for the implementation of strong network intrusion detection systems (NIDS). Regrettably, a wide range of potential hazards might put computer users at risk when using the Internet. Users stand to lose a great deal, including control over machines, private information, and potentially even identity theft. The ongoing problem of inconsistent and changing threat data persists despite notable improvements in NIDS models. As more and more research has realized the need for improved detection skills, artificial intelligence (AI) has become a viable answer. In order to meet the challenges presented by emerging cyber threats, this study presents a thorough investigation into the integration of AI, specifically generative adversarial networks (GANs), into NIDS. GANs are a machine learning breakthrough that provides a fresh method for creating new data instances that closely mimic real data. This skill is especially useful in the context of network intrusion detection systems (NIDS), where identifying anomalies necessitates a sophisticated comprehension of intricate and dynamic threat patterns. Our study expands on earlier research in the field that mostly concentrated on deep learning methods like autoencoders, convolutional neural networks (CNN), and long short-term neural networks (LSTM), as well as conventional machine learning models like decision trees (DT) and support vector machines (SVM). Although these methods have shown potential, there hasn’t been much of a practical use for them in actual NIDS systems. A promising approach to improving NIDS performance is provided by the suggested NIDS makes use of GANs, especially when it comes to anomaly detection. Through the creation of artificial data instances that closely resemble real-world threats, GANs can enhance the resilience and flexibility of NIDS in dynamic threat settings.