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

Ramya Chinnasamy; Malliga Subramanian; Sathishkumar Veerappampalayam Easwaramoorthy; Jaehyuk Cho

doi:10.1016/j.icte.2025.01.005

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

Ramya Chinnasamy
, Malliga Subramanian
, Sathishkumar Veerappampalayam Easwaramoorthy
, Jaehyuk Cho^*

^*Corresponding author for this work

Department of Software Engineering

Kongu Engineering College
Sunway University

Research output: Contribution to journal › Review article › peer-review

76 Scopus citations

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.

Original language	English
Pages (from-to)	181-215
Number of pages	35
Journal	ICT Express
Volume	11
Issue number	1
DOIs	https://doi.org/10.1016/j.icte.2025.01.005
State	Published - 2025.02

Keywords

Cyber Security
Deep Learning
Network-based Intrusion Detection Systems (NIDS)
Systematic Review

Quacquarelli Symonds(QS) Subject Topics

Computer Science & Information Systems
Data Science

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10.1016/j.icte.2025.01.005

Cite this

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title = "Deep learning-driven methods for network-based intrusion detection systems: A systematic 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.",

keywords = "Cyber Security, Deep Learning, Network-based Intrusion Detection Systems (NIDS), Systematic Review",

author = "Ramya Chinnasamy and Malliga Subramanian and Easwaramoorthy, \{Sathishkumar Veerappampalayam\} and Jaehyuk Cho",

note = "Publisher Copyright: {\textcopyright} 2025 The Authors",

year = "2025",

month = feb,

doi = "10.1016/j.icte.2025.01.005",

language = "English",

volume = "11",

pages = "181--215",

journal = "ICT Express",

issn = "2405-9595",

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TY - JOUR

T1 - Deep learning-driven methods for network-based intrusion detection systems

T2 - A systematic review

AU - Chinnasamy, Ramya

AU - Subramanian, Malliga

AU - Easwaramoorthy, Sathishkumar Veerappampalayam

AU - Cho, Jaehyuk

PY - 2025/2

Y1 - 2025/2

N2 - 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.

AB - 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.

KW - Cyber Security

KW - Deep Learning

KW - Network-based Intrusion Detection Systems (NIDS)

KW - Systematic Review

UR - https://www.scopus.com/pages/publications/85215923922

U2 - 10.1016/j.icte.2025.01.005

DO - 10.1016/j.icte.2025.01.005

M3 - Review article

AN - SCOPUS:85215923922

SN - 2405-9595

VL - 11

SP - 181

EP - 215

JO - ICT Express

JF - ICT Express

IS - 1

ER -

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

Abstract

Keywords

Quacquarelli Symonds(QS) Subject Topics

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