Machine Learning for Anomaly Detection

More complex cyberattacks are popping up, thus we need better ways to spot suspicious behavior in network data. In order to discover both known and new infiltration attempts, this research investigates the use of Generative Adversarial Networks (GANs) to construct a dynamic learning environment. This adversarial system has a generator that mimics network traffic and possible assaults and a discriminator that tries to tell good things from bad. By continually adjusting to new attack patterns, this iterative process improves the system's threat detection capabilities. Extensive tests were carried out utilizing benchmark datasets to demonstrate that the GAN-based system outperformed traditional machine learning methods in identifying sophisticated and covert threats. Significant gains in detection rates, decreased false positives, and improved generalizability to previously undiscovered dangers are highlighted by the findings. A strong and scalable solution to safeguard contemporary network infrastructures from the dynamic cyber threat environment, the system's adaptive learning capabilities guarantees continuous development and resilience. In order to protect networks from ever-evolving cyberattacks, this study highlights the promise of GANs in this area, providing a solution that is both flexible and scalable.