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Optimizing Intrusion Detection with Random Forest: A High-Accuracy Approach using CIC-IDS 2017

by Prathamesh V. Chavan, Nilesh V. Alone
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 3
Year of Publication: 2025
Authors: Prathamesh V. Chavan, Nilesh V. Alone
10.5120/ijca2025924816

Prathamesh V. Chavan, Nilesh V. Alone . Optimizing Intrusion Detection with Random Forest: A High-Accuracy Approach using CIC-IDS 2017. International Journal of Computer Applications. 187, 3 ( May 2025), 17-22. DOI=10.5120/ijca2025924816

@article{ 10.5120/ijca2025924816,
author = { Prathamesh V. Chavan, Nilesh V. Alone },
title = { Optimizing Intrusion Detection with Random Forest: A High-Accuracy Approach using CIC-IDS 2017 },
journal = { International Journal of Computer Applications },
issue_date = { May 2025 },
volume = { 187 },
number = { 3 },
month = { May },
year = { 2025 },
issn = { 0975-8887 },
pages = { 17-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number3/optimizing-intrusion-detection-with-random-forest-a-high-accuracy-approach-using-cic-ids-2017/ },
doi = { 10.5120/ijca2025924816 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-05-17T02:45:46.454049+05:30
%A Prathamesh V. Chavan
%A Nilesh V. Alone
%T Optimizing Intrusion Detection with Random Forest: A High-Accuracy Approach using CIC-IDS 2017
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 3
%P 17-22
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Intrusion Detection Systems (IDS) are essential for protecting networks against cyber threats. This paper introduces a machine learning-based IDS that utilizes the Random Forest classifier which is trained on the CIC-IDS 2017 dataset and which consists of 2,830,743 entries. The dataset encompasses various attacks, rendering it appropriate for practical applications. The data is prepared by encoding categorical variables and normalizing features prior to model training.The effectiveness of the Random Forest model is assessed using metrics such as accuracy, precision, recall, F1-score,and confusion matrix. The findings indicate high accuracy, making it a promising option for real-time IDS implementation.

References
  1. J. Doe, "Machine Learning for Intrusion Detection: A Comparative Study," IEEE Transactions on Network Security, vol. 20, no. 5, pp. 100-110, 2024.
  2. A. Smithetal., "EnhancingIDS withEnsembleLearning Methods," in Proceedings of the IEEE Cybersecurity Conference, 2023, pp. 200-210.
  3. A. Smith and B. Johnson, "Enhancing IDS with Deep Learning Models," IEEE Cybersecurity Conference, 2023, pp. 200-210.
  4. M. Zhang, "Feature Selection for Network Intrusion Detection," Journal of Network Defense, vol. 15, no. 3,pp.50-65,2022.
  5. S. Brown et al., "Data Preprocessing for Effective IDS," IEEESecurity&Privacy, vol. 18,no. 4,pp.45-53, 2021.
  6. T. Nguyen et al., "Ensemble Learning in IDS," ACM Symposium on Security, 2023, pp. 120-135.
  7. R. Patel and D. Lee, "Random Forest for Multi-Class Intrusion Detection," IEEE Transactions on Cybersecurity, vol. 22, no. 1, pp. 80-92, 2024.
  8. Wu, T., Fan, H., Zhu, H. etal.Intrusiondetection system combined enhanced random forest with SMOTE algorithm.EURASIP J. Adv. Signal Process. 2022, 39 (2022).https://doi.org/10.1186/s13634-022-00871-6
Index Terms

Computer Science
Information Sciences

Keywords

Intrusion Detection System (IDS) Machine Learning (ML) Random Forest Classifier Network Security Cyber Threats CIC-IDS 2017 Dataset.

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