An overview of Intrusion Detection Based on Deep Learning Techniques
Fadheela Hussain, Mustafa Hammad · 2020 International Conference on Decision Aid Sciences and Application (DASA) · 2020
One of the main challenge in computer networks has become to keep up with patterns of threats that evolve and increase on a daily basis. There are many traditional mechanisms, such as firewalls, but they do not secure the detection of new types of attacks. Intrusion detection systems are tools to detect attacks, but they suffer from an inability to detect unknown attacks. Therefore, the method was devoted to methods of machine learning and data mining to increase the capability to predict new types of attacks. This study reviewed and analysed the research background for Intrusion Detection Systems (IDSs) based on Deep Learning (DL) or Machine Learning (ML) methods into a logical taxonomy and pinpoints the challenges and future opportunities in this vital study area. There are several techniques to aid IDS to identify and identified the changing behaviour of the system. However, some papers have lately proposed the idea of hybrid detection. This study analyses machine-learning techniques in IDS. Many related studies focused on machine learning techniques had been reviews in the period below 2000 to 2020. Associated studies include machine learning, deep learning, and Hybrid approaches.