Artificial Intelligence-Driven Cybersecurity Framework for Enterprise Threat Detection: A Machine Learning Approach

Abstract

The increasing complexity of cyber threats has exposed the limitations of traditional signature-based intrusion detection systems, creating a need for intelligent and adaptive cybersecurity solutions. This study proposes an artificial intelligence-driven cybersecurity framework for enterprise threat detection using the CICIDS2017 benchmark dataset. The framework incorporates data preprocessing, feature engineering, and supervised machine learning to classify network traffic as benign or malicious. Seven machine learning algorithms, including Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, Extra Trees, LightGBM, and XGBoost, were evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. The results indicate that ensemble learning models outperform conventional classifiers, with XGBoost achieving the highest performance, recording 99.42% accuracy, 99.39% precision, 99.31% recall, 99.35% F1-score, and an AUC-ROC of 0.999. LightGBM also demonstrated excellent performance with lower computational time. The findings suggest that the proposed XGBoost-based framework provides an accurate, scalable, and efficient solution for real-time enterprise threat detection and can be effectivel