Articles
| Open Access |
DOI:
https://doi.org/10.55640/ijcsis/Volume10Issue01-02
LEVERAGING AI AND MACHINE LEARNING FOR PREDICTING, DETECTING, AND MITIGATING CYBERSECURITY THREATS: A COMPARATIVE STUDY OF ADVANCED MODELS
S M Shadul Islam Rishad , Master Of Science in Information Technology, Westcliff University, USA Farhan Shakil , Master’s in Cybersecurity Operations, Webster University, Saint Louis, MO, USA Sanjida Akter Tisha , Master of Science in Information Technology, Washington University of Science and Technology, USA Sadia Afrin , Department of Computer & Information Science, Gannon University, USA Md Mehedi Hassan , Master of Science in Information Technology, Washington University of Science and Technology, USA Mashaeikh Zaman Md. Eftakhar Choudhury , Master of Social Science in Security Studies, Bangladesh University of Professional (BUP), Dhaka Nabila Rahman , Master’s in information technology management, Webster University, USAAbstract
This study investigates the use of artificial intelligence (AI) and machine learning (ML) models to predict, detect, and mitigate cybersecurity threats, including zero-day attacks, ransomware, and insider threats. Using a comprehensive dataset of network logs and attack signatures, we evaluated models such as Logistic Regression, Random Forest, XGBoost, CNN, and LSTMOur results demonstrate that deep learning models, particularly CNN (97.3% AUC-ROC) and LSTM (96.8% AUC-ROC), significantly outperform traditional methods, excelling in real-time threat detection and minimizing false positives. This study highlights the practical applicability of AI and ML in enhancing cybersecurity frameworks, paving the way for more efficient and scalable solutions against evolving threats.
Keywords
Cybersecurity, Artificial Intelligence, Machine Learning, Threat Detection
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Copyright (c) 2025 S M Shadul Islam Rishad, Farhan Shakil, Sanjida Akter Tisha, Sadia Afrin, Md Mehedi Hassan, Mashaeikh Zaman Md. Eftakhar Choudhury, Nabila Rahman
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