Towards secure intrusion detection systems using deep learning techniques: Comprehensive analysis and review

Providing a high-performance Intrusion Detection System (IDS) can be very effective in controlling malicious behaviors and cyber-attacks. Regarding the ever-growing negative impacts of the security attacks on computer systems and networks, various Artificial Intelligence (AI)-based techniques have been used to introduce versatile IDS approaches. Deep learning is a branch of AI techniques, mainly based on multi-layer artificial neural networks. Recently, deep learning techniques have gained momentum in the intrusion detection domain and several IDS approaches are provided in the literature using various deep neural networks to deal with privacy concerns and security threats. For this purpose, this article focuses on the deep IDS approaches and investigates how deep learning networks are employed by different approaches in different steps of the intrusion detection process to achieve better results. It classifies the studied IDS schemes regarding the deep learning networks utilized in them and describes their main contributions and capabilities. Besides, in each category, their main features such as evaluated metrics, datasets, simulators, and environments are compared. Also, a comparison of the deep IDS approaches main properties are provided to illuminate the main techniques applied in them as well as the area less focused in the literature. Finally, the concluding remarks in the deep IDS context are provided and possible directions at the subsequent studies are listed.

Automated summary

This article focuses on the application of deep learning in intrusion detection systems, discussing how different methods utilize deep learning networks for better results. It classifies the studied IDS schemes and compares their features in different aspects. Finally, comparisons of deep IDS approaches and future research directions are provided.