Enhancing intrusion detection recursive feature elimination with resampling in WSN

Ashwini B. Abhale () and Jayaram Reddy Avulapalli ()
Additional contact information
Ashwini B. Abhale: Vellore Institute of Technology
Jayaram Reddy Avulapalli: Vellore Institute of Technology

International Journal of System Assurance Engineering and Management, 2023, vol. 14, issue 6, No 46, 2642-2660

Abstract: Abstract With the proliferation of technologies such as the Internet of Things, Cloud computing, and Social Networking, large quantities of network traffic and data are generated, necessitating the use of effective Intrusion Detection Systems (IDS). IDS play an essential role in detecting hazards within a system and generating alerts for potential attacks. This paper examines the use of machine learning algorithms for intrusion detection with the NSL KDD dataset. However, not all features contribute equally to performance enhancement in large datasets. Therefore, it becomes essential to reduce the feature set to a subset that improves both speed and accuracy. Within the IDS framework, we employ machine learning algorithms such as Random Forest, Support Vector Machine, K Nearest Neighbour, and Decision Tree through rigorous experimentation. Recursive Feature Elimination and Resampling techniques are utilised to select and evaluate the impact of feature reduction. The comparative evaluation of the model’s performance is demonstrated, emphasising the performance enhancements attained through feature selection.

Keywords: Wireless sensor network; Intrusion detection system (IDS); NSL KDD dataset; Machine learning; Random forest; Support vector machine; K nearest neighbor; Decision tree algorithms (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-023-02128-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:14:y:2023:i:6:d:10.1007_s13198-023-02128-3

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-023-02128-3

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().