Neural Networks and Cyber Resilience: Deep Insights into AI Architectures for Robust Security Framework

Abstract

With the goal of applying artificial intelligence architectures to strengthen security frameworks, this paper explores the integration of Neural Networks into cybersecurity. The review of the literature highlights the advancements made in the use of neural networks for a range of cyber defense uses, with a focus on spam filtering and intrusion detection. Notable obstacles highlight the changing cybersecurity scene, such as the interpretability of AI and the need for explainable models.The study paper that goes along with it offers helpful advice on how to put AI architectures for strong security frameworks into practice. It emphasizes the methodical fusion of various AI techniques to strengthen defenses against the ever-changing array of cyberthreats. Tabulated performance ratings of several Neural Network types provide a comprehensive grasp of their strengths and capabilities across a range of metrics. These evaluations show that hybrid RCNN-ML performs very well. The potential and difficulties presented by the changing cybersecurity landscape are explored in discussions of AI technologies and their effects, with a focus on how traditional defenses must evolve in order to effectively use AI-driven solutions. At the end this work offers a nuanced viewpoint on the use of neural networks to improve cybersecurity resilience. Understanding the nuances of various Neural Network types is crucial for creating flexible and reliable security frameworks in the face of constantly evolving cyber threats as AI continues to advance.