International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 186 - Issue 69 |
Published: March 2025 |
Authors: Md. Aminur Rahman, Manjur Ahammed, Mohammad Mizanur Rahaman, Alvi Amin Khan |
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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 (March 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 }, year = { 2025 }, volume = { 186 }, number = { 69 }, pages = { 50-60 }, doi = { 10.5120/ijca2025924526 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2025 %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%T %J International Journal of Computer Applications %V 186 %N 69 %P 50-60 %R 10.5120/ijca2025924526 %I Foundation of Computer Science (FCS), NY, USA
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.