Designing Generative AI‐Envisioned Security Scheme for Server Attacks Protection

ABSTRACT Generative AI (Gen‐AI) refers to artificial intelligence (AI) models that are capable of generating original text, imagery, music, code, and even films on the basis of patterns identified in extensive datasets. Even though Generative AI (Gen‐AI) benefits many enterprises (i.e., business, healthcare, supply chain, and entertainment), it also introduces new concerns, particularly in the field of cybersecurity. Gen‐AI is a formidable tool for both hackers (i.e., Internet‐based attackers) and protectors (i.e., security experts), as its capacity to produce comprehensive, unique data patterns and mimic human behavior makes it extremely successful. Gen‐AI can be used to carry out sophisticated attacks, such as escaping detection systems using adaptive, randomized patterns that resemble real‐world activity. Improving intrusion detection systems, building adaptive firewalls, and establishing proactive defensive mechanisms capable of predicting and responding to attacks in real time can all help to improve security. This study investigates the dual nature of Gen‐AI for the domain of cybersecurity. It focuses on both its potential hazards and its defensive function in improving server security. In this paper, we propose a Gen‐AI‐envisioned bidirectional scheme for server attacks and protection. A practical implementation of the proposed scheme is also provided to assess its suitability in real‐world scenarios. Furthermore, a comparative performance analysis is provided, which identifies that the proposed scheme outperforms other similar competing schemes.