A Deep Learning Framework and Algorithms for Automatic Cyber Attacks

Abstract

Cybersecurity in the digital age faces increasing challenges posed by novel and adaptive cyber threats. Traditional Intrusion Detection Systems (IDS) struggle to keep pace with evolving attack vectors. To address this issue, our research presents a comprehensive framework that leverages deep learning and federated learning principles for cyber attack detection. Our primary objective is the development of an integrated model capable of efficiently detecting a wide range of cyber threats while preserving data privacy. We rigorously evaluate the framework's performance, emphasizing accuracy, scalability, and adaptability. Despite challenges such as data privacy constraints and computational demands, the potential applications of our research span across critical sectors, including IoT security, cloud platforms, financial institutions, government agencies, healthcare, e-commerce, and education. This research aims to significantly advance cyberattack detection capabilities in an ever-changing digital landscape.