A Survey on Intrusion Detection System Based on Deep Learning

Authors- Saddam Hussain, Professor Santosh Nagar, Professor Anurag Shrivastava

Abstract-Intrusion Detection Systems (IDS) play a critical role in safeguarding computer networks against malicious activities and security breaches. With the advent of deep learning techniques, IDS have achieved remarkable advancements in accuracy, adaptability, and real-time detection. This survey explores the state-of-the-art deep learning-based IDS, categorizing them by architectures such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and hybrid models. Key challenges, including handling imbalanced datasets, scalability, and adversarial attacks, are critically analyzed. Emerging trends, such as transfer learning and explainable AI, are also discussed to highlight future research directions. The survey aims to provide researchers and practitioners with a comprehensive understanding of leveraging deep learning techniques for building robust and efficient IDS in dynamic network environments.

DOI: /10.61463/ijset.vol.13.issue1.173