Detecting and Reporting IoT Attacks using AI Models

The rapid advancement of techniques utilizing machines learning and deep learning in the last few years has made it feasible to detect and mitigate cyberattacks in real time. This study presents a novel technique for cyberattack detection by employing artificial generative networks (GANs) to generate real network attack data. After that, classification algorithms based on machine learning are taught to make accurate predictions. We compare the performance of GANs with more traditional models such as deeper belief networks (DBNs) and Automated Encoders, highlighting the accuracy of GANs in generating realistic attack information while also achieving good classification performance. The discriminator and generator networks comprise the model architecture. While the generator produces false attack data, the discriminator separates real data from fake. A regression classifier taught on every dataset performs better when the synthetic data generated by the GAN is added to real-world attack data. The efficacy of the model is evaluated using a number of classification metrics, such as preciseness, the matrix of disarray and classification reports. Our results show that the GAN- based approach performs better than Autoencoders and DBNs, providing an accurate and scalable solution for cyberattack detection and classification. This study paves the way for future research into improving synthetic data synthesis for cybersecurity anomaly detection.