Computer Vision-Based Cybersecurity Threat Detection System with GAN-Enhanced Data Augmentation

The importance of establishing a strong and resilient cybersecurity threat detection system has become increasingly evident. In recent years, a multitude of methodologies have been developed to identify and mitigate security problems within computer networks. This study presents a novel methodology for categorizing security risks and effectively tackling these obstacles. Through the utilization of computer vision, network traffic data is converted into visual depictions, facilitating the discernment between secure traffic and possibly malevolent endeavors aimed at infiltrating a network. Furthermore, the integration of a Generative Adversarial Network (GAN) assumes a crucial function in enhancing data and reducing bias in the classification procedure. The focus of this study is around two critical classification components: binary classification, which involves deciding whether a given traffic instance is classified as safe or malicious, and multi-class classification, which involves identifying the specific sort of attack if the instance is truly classified as an attack. By utilizing advanced deep learning models, this study has produced notable outcomes, attaining a commendable level of precision of around 95% in both binary and multi-classification situations. The aforementioned results highlight the effectiveness and potential of the suggested methodology within the field of cybersecurity.