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ISSN Approved Journal No: 2320-2882 | Impact factor: 7.97 | ESTD Year: 2013
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Published Paper Details:
Paper Title
AI-DRIVEN NETWORK THREAT DETECTION: COMPARATIVE ANALYSIS OF RANDOM FOREST, XGBOOST, AND HYBRID ENSEMBLE MODEL ON NSL-KDD DATASET
Authors
Dr.S.Brindha, Ms.D.Priya, SharvanthVadivelan, Ragav Ananth S
Keywords
Network Security, Machine Learning, Artificial Intelligence, Random Forest, XGBoost, Hybrid Ensemble, NSL-KDD, Threat Detection.
Abstract
This paper presents a comparative analysis of three Artificial Intelligence (AI) based models -- Random Forest, XG Boost, and a Hybrid Ensemble Model (Random Forest + XG Boost) -- for detecting network threats. The NSL-KDD dataset was used to train and evaluate the models. The study aims to improve the accuracy, precision, and reliability of intrusion detection systems by combining ensemble learning methods. Each model was tested under the same preprocessing and training conditions, and performance metrics such as Accuracy, Precision, Recall, and F1-score were analyzed. Results showed that the Hybrid Ensemble model achieved the best performance with an accuracy of 99.45%, proving that combining Random Forest and XG Boost enhances overall detection capability and reduces false alarms.
IJCRT's Publication Details
Unique Identification Number - IJCRT2602106
Paper ID - 299049
Page Number(s) - a932-a936
Pubished in - Volume 14 | Issue 2 | February 2026
DOI (Digital Object Identifier) -
Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Dr.S.Brindha, Ms.D.Priya, SharvanthVadivelan, Ragav Ananth S, "AI-DRIVEN NETWORK THREAT DETECTION: COMPARATIVE ANALYSIS OF RANDOM FOREST, XGBOOST, AND HYBRID ENSEMBLE MODEL ON NSL-KDD DATASET", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.14, Issue 2, pp.a932-a936, February 2026, Available at :http://www.ijcrt.org/papers/IJCRT2602106.pdf
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Volume 14 | Issue 2
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Submit Manuscript Online Impact Factor: 7.97 Review Results : Within 02-03 Days Paper Publication : Within 02-03 Days
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
ISSN and 7.97 Impact Factor Details
ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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