DEEP LEARNING FOR INTRUSION DETECTION SYSTEMS

Abstract

In recent years, computer networks have faced a rapid and continuous rise in diverse and sophisticated cyberattacks, posing significant challenges to data integrity and system reliability. Intrusion detection therefore remains a critical research area in network security. The present work examines the implementation of deep learning (DL) methodologies aimed at optimizing the accuracy and adaptability of Intrusion Detection Systems (IDS), alongside a performance comparison among different models feature representation, and benchmark datasets to highlight the advantages of DL-based methods in accurately identifying emerging threats. In the 2024–2025 context, deep learning continues to advance IDS capabilities through automated feature extraction, improved generalization to unseen attacks, and real-time detection across dynamic network environments.