An Ensemble-Based Network Intrusion Detection System Using Random Forest Algorithm: A 2025 Perspective
Tanya Gupta
, Dr. Sunita Soni , Dr. Shweta Kharya
Network Intrusion Detection System, Random Forest, Ensemble Learning, UNSW-NB15, Cybersecurity, Machine Learning.
With the ever-evolving threat landscape in cyberspace, traditional intrusion detection systems (IDS) struggle to maintain efficacy against novel and complex attacks. This paper presents an ensemble-based Network Intrusion Detection System (NIDS) employing the Random Forest algorithm, evaluated using the UNSW-NB15 dataset. The ensemble approach combines the strengths of multiple base classifiers—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT)—with Random Forest to achieve robust and high-performing detection capabilities. Experimental results demonstrate improved accuracy, precision, recall, and F1-score compared to individual classifiers.
"An Ensemble-Based Network Intrusion Detection System Using Random Forest Algorithm: A 2025 Perspective", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 5, page no.a45-a48, May-2025, Available :https://ijsdr.org/papers/IJSDR2505008.pdf
Volume 10
Issue 5,
May-2025
Pages : a45-a48
Paper Reg. ID: IJSDR_302405
Published Paper Id: IJSDR2505008
Downloads: 000518
Research Area: Science and Technology
Country: Dhamtari , Chhattisgarh , India
DOI: https://doi.org/10.56975/ijsdr.v10i5.302405
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
