Abstract

With the exponential growth of networked systems and digital assets, cybersecurity threats have become increasingly sophisticated, posing significant risks to organizations and individuals alike. Traditional intrusion detection systems (IDS) struggle to keep pace with evolving attack vectors and the sheer volume of network traffic. Artificial Intelligence (AI), particularly deep learning, offers powerful tools for automating and enhancing intrusion detection. This paper surveys the design, implementation, and effectiveness of AI-powered intrusion detection systems using deep learning in cybersecurity. We discuss popular deep learning architectures, datasets, implementation strategies, challenges, and future directions, supported by recent research and empirical results.