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Reseach Article

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
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