Systematic literature review on intrusion detection systems: Research trends, algorithms, methods, datasets, and limitations

Abstract: Machine learning (ML) and deep learning (DL) techniques have demonstrated significant potential in the development of effective intrusion detection systems. This study presents a systematic review of the utilization of ML, DL, optimization algorithms, and datasets in intrusion detection research from 2018 to 2023. We devised a comprehensive search strategy to identify relevant studies from scientific databases. After screening 393 papers meeting the inclusion criteria, we extracted and analyzed key information using bibliometric analysis techniques. The findings reveal increasing publication trends in this research domain and identify frequently used algorithms, with convolutional neural networks, support vector machines, decision trees, and genetic algorithms emerging as the top methods. The review also discusses the challenges and limitations of current techniques, providing a structured synthesis of the state-of-the-art to guide future intrusion detection research.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Systematic literature review on intrusion detection systems: Research trends, algorithms, methods, datasets, and limitations ; volume:33 ; number:1 ; year:2024 ; extent:38 ; Journal of intelligent systems ; 33, Heft 1 (2024) (gesamt 38)

Contributors, Places and Time

• Creator: Issa, Melad Mohammed ; Aljanabi, Mohammad ; Muhialdeen, Hassan M.

Further information

• DOI: 10.1515/jisys-2023-0248
• URN: urn:nbn:de:101:1-2406051715112.207324261799
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:50 AM CEST