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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