Intrusion detection systems using deep learning aim to address the evolving challenges of cybersecurity by leveraging advanced machine learning techniques. By focusing on various aspects of intrusion detection, such as real-time monitoring, anomaly detection, and collaborative defense mechanisms, this research will contribute to the development of effective and adaptive security solutions.Deep learning has emerged as a powerful approach to enhance IDS by leveraging large volumes of data to learn complex representations and identify subtle indicators of intrusion. This series of PhD projects aims to develop innovative deep learning methodologies for building effective intrusion detection systems that can adapt to evolving threats and improve detection rates while minimizing false positives.