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Intrusion Detection in the Era of Machine Learning: A Critical Survey of Algorithms and Evaluation Practices

by Bhavika, Neelam Duhan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 21
Year of Publication: 2025
Authors: Bhavika, Neelam Duhan
10.5120/ijca2025925297

Bhavika, Neelam Duhan . Intrusion Detection in the Era of Machine Learning: A Critical Survey of Algorithms and Evaluation Practices. International Journal of Computer Applications. 187, 21 ( Jul 2025), 50-57. DOI=10.5120/ijca2025925297

@article{ 10.5120/ijca2025925297,
author = { Bhavika, Neelam Duhan },
title = { Intrusion Detection in the Era of Machine Learning: A Critical Survey of Algorithms and Evaluation Practices },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2025 },
volume = { 187 },
number = { 21 },
month = { Jul },
year = { 2025 },
issn = { 0975-8887 },
pages = { 50-57 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number21/intrusion-detection-in-the-era-of-machine-learning-a-critical-survey-of-algorithms-and-evaluation-practices/ },
doi = { 10.5120/ijca2025925297 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-07-26T00:55:56.435646+05:30
%A Bhavika
%A Neelam Duhan
%T Intrusion Detection in the Era of Machine Learning: A Critical Survey of Algorithms and Evaluation Practices
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 21
%P 50-57
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the growing prominence and sophistication of cyber-attacks, IDS are now indispensable in securing computer networks. Traditional signature-based methods often fail to detect novel threats, prompting the adoption of ML and DL techniques into IDS. This review explores a range of ML algorithms: such as Decision Trees, Random Forest, Support Vector Machines, k-Nearest Neighbors, Naïve Bayes, and Logistic Regression—as well as DL models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). It explains their use in anomaly detection with established datasets like NSL-KDD and UNSW-NB15, and emphasizes importance of data preprocessing, feature selection, and evaluation measures (precision, accuracy, recall, F1-score). The survey emphasizes the strengths as well as constraints of every method, indicating that ensemble & deep learning methods show improved detection accuracy. Finally, it outlines key challenges and proposes future research avenues for developing robust & intelligent IDS solutions.

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Index Terms

Computer Science
Information Sciences

Keywords

Intrusion Detection System (IDS); Machine Learning (ML); Deep Learning (DL); Anomaly Detection; NSL-KDD; UNSW-NB15.

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