A Survey of Deep Learning Techniques for Misuse-Based Intrusion Detection Systems
Jan Lánský, Mokhtar Mohammadi, Adil Hussein Mohammed, Sarkhel H. Taher Karim, Shima Rashidi, Amir Masoud Rahmani, Mehdi Hosseinzadeh · Research Square · 2021
Abstract The ever-increasing complication and severity of the computer networks' security attacks have inspired security researchers to apply various machine learning methods to protect the organizations' data and reputation. Deep learning is one of the exciting techniques that recently have been widely used by intrusion detection systems (IDS) to secure computer networks and hosts' performance. This survey article focuses on the signature-based IDS using deep learning techniques and puts forward an in-depth survey and classification of these schemes. For this purpose, it first presents the essential background concepts about IDS architecture and various deep learning techniques. It then classifies these schemes according to the type of deep learning methods applied in each of them. It describes how deep learning networks are utilized in the misuse detection process to recognize intrusions accurately. Finally, a complete analysis of the investigated IDS frameworks is provided, and concluding remarks and future directions are highlighted.