A Review of Deep Learning Techniques for Network Intrusions Detection towards Efficient Model Developments
Olufunso Dayo Alowolodu, Adebayọ Olusọla Adetunmbi, Jacob Olorunshogo Mebawondu, Olamatanmi Josephine Mebawondu · 2022 IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development (NIGERCON) · 2022
The enormous upsurge in cyber-attack cases on computer networks in recent times necessitated the need for an effective Intrusion detection system than ever before. Deep learning techniques have been established to be more effective in the detection of intrusions in computer networks than classical machine learning or rule-based algorithms. This is an attempt to appraise existing deep learning paradigms applied to intrusion detection in terms of architecture, methodologies, and challenges to assist other researchers in the choice of a deep learning technique. The deep learning techniques showing their architecture are presented to enable other researchers to decide easily on variations of the techniques for a better choice network intrusion detection system model. The review proposes a better model of developments of network intrusion detection for researchers.