Work

Deep learning-driven methods for network-based intrusion detection systems: A systematic review

Year: 2025

Type: review

Abstract: This paper presents a systematic review of deep learning (DL) techniques for Network-based Intrusion Detection Systems (NIDS) based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses: (PRISMA2020) guidelines. It explores recent advancements in data preparation, DL architectures, and performance evaluation metrics for NIDS. The review provides insights into various datasets and tools used in the field, highlighting the effectiveness of DL in improving NIDS performance. Additionally, it discusses the applications of NIDS across different industries and identifies emerging research trends, offering a comprehensive resource for researchers and practitioners in cybersecurity.

Source: ICT Express

DOI: 10.1016/j.icte.2025.01.005

Authors: Ramya Chinnasamy, Malliga Subramanian, Sathishkumar Veerappampalayam Easwaramoorthy, Jaehyuk Cho

Institutions: Sunway University, Jeonbuk National University

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,500

Funders: Ministry of Environment, Korea Environmental Industry and Technology Institute