Security Enhancement in Network Based Intrusion Detection Using Natural Language Processing with GAN concept and BEGAN Model

Cyber-attacks, particularly in recent times represent a major problem demanding novel measures consistent with modern digital structures. Some of the dangers that cybercrime entails encompass phishing, ransomware, DDoS assaults, as well as state-enabled cyber espionage. With growing digital infrastructures in countries and businesses moving to digital transformation, securing in the digital arena is more challenging. The number of lines of code nowadays generated daily makes it necessary to have enhanced vulnerability identification procedures. Cyber security can be advanced through the Natural Language Processing (NLP) subfield of Artificial Intelligence towards achieving computer-human language interaction. Cybercrime has seen a widespread utilization of GANs based on IDSs. Despite this, GAN-based IDS has still faced criticism. This consideration is based on real-time observation fueled by made strides cautioning and clarification of peculiarities utilizing GAN improved with NLP. Moreover, we suggest some future research works that will help resolve the deficiencies encountered by GANs based on IDS.