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AI-Driven Cybersecurity: Leveraging Machine Learning Algorithms for Advanced Threat Detection and Mitigation

by Md. Aminur Rahman, Manjur Ahammed, Mohammad Mizanur Rahaman, Alvi Amin Khan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 69
Year of Publication: 2025
Authors: Md. Aminur Rahman, Manjur Ahammed, Mohammad Mizanur Rahaman, Alvi Amin Khan
10.5120/ijca2025924526

Md. Aminur Rahman, Manjur Ahammed, Mohammad Mizanur Rahaman, Alvi Amin Khan . AI-Driven Cybersecurity: Leveraging Machine Learning Algorithms for Advanced Threat Detection and Mitigation. International Journal of Computer Applications. 186, 69 ( Mar 2025), 50-60. DOI=10.5120/ijca2025924526

@article{ 10.5120/ijca2025924526,
author = { Md. Aminur Rahman, Manjur Ahammed, Mohammad Mizanur Rahaman, Alvi Amin Khan },
title = { AI-Driven Cybersecurity: Leveraging Machine Learning Algorithms for Advanced Threat Detection and Mitigation },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2025 },
volume = { 186 },
number = { 69 },
month = { Mar },
year = { 2025 },
issn = { 0975-8887 },
pages = { 50-60 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number69/ai-driven-cybersecurityleveraging-machine-learning-algorithms-for-advanced-threat-detection-and-mitigation/ },
doi = { 10.5120/ijca2025924526 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-03-01T12:38:53.223842+05:30
%A Md. Aminur Rahman
%A Manjur Ahammed
%A Mohammad Mizanur Rahaman
%A Alvi Amin Khan
%T AI-Driven Cybersecurity: Leveraging Machine Learning Algorithms for Advanced Threat Detection and Mitigation
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 69
%P 50-60
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The rapid evolution of cyber threats necessitates advanced solutions, and Artificial Intelligence (AI) has emerged as a transformative tool in cybersecurity. This study aims to evaluate the effectiveness of AI-driven machine learning algorithms—Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), and Support Vector Machines (SVM)—in enhancing threat detection and mitigation. Leveraging the KDD Cup 99 dataset, the research employs a rigorous experimental setup, including data preprocessing, feature selection, and algorithm evaluation using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The results reveal that CNN outperformed other models, achieving a 96.5% accuracy and demonstrating superior capability in identifying complex attack patterns. ANN and SVM also performed well, with accuracies of 94.8% and 92.1%, respectively. These findings underscore the potential of AI to bolster cybersecurity frameworks, offering improved detection rates and reduced false positives. The study contributes to the growing field of AI-driven cybersecurity by providing actionable insights for integrating machine learning models into practical applications. Future research is encouraged to explore hybrid models, real-time threat intelligence, and broader datasets to further enhance the adaptability and efficacy of AI-driven solutions in combating the dynamic landscape of cyber threats.

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Index Terms

Computer Science
Information Sciences

Keywords

Machine Learning ANN CNN cyber security Cyber Threat Deep Learning Detection

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