Engineering and Technology
| Open Access |
DOI: Enhancing Enterprise Security Management Using Hybrid Machine Learning and Large Language Model–Assisted Intrusion Detection
Mohammad Musa Mia , Master of Business Administration, International American University, Los Angeles, California Md Mohibur Rahman , Fred DeMatteis School of Engineering and Applied Science, Hofstra University, USA Md Abu Sayed , Department of Professional Security Studies, New Jersey City University, Jersey City, New Jersey, USA Rumana Akther Nipa , Master of Science in Engineering Management, College of Engineer & Technology, Westcliff University, Irvine, California Sonjoy Kumar Dey , McComish Department of Electrical Engineering and Computer Science, South Dakota State University, USA Kazi Abu Jahed , Master of Science in Business Intelligence and Analytics, Saint Joseph's University (SJU), USA Md Yassir Mottalib , Master of Science in Information System Technology, Wilmington University, USAAbstract
Enterprise security management faces increasing challenges due to the growing complexity of corporate networks and the sophistication of cyberattacks. Traditional intrusion detection systems, while effective at identifying known threats, often struggle with novel attacks and lack interpretability, resulting in alert fatigue and delayed responses. In this study, I propose a hybrid framework that combines ensemble-based machine learning intrusion detection with large language model–assisted contextual reasoning to enhance both detection accuracy and explain ability. Using the CICIDS2017 dataset, I evaluate baseline classifiers including logistic regression, support vector machines, random forest, and gradient boosting, and compare them with the proposed hybrid architecture. Experimental results demonstrate that the hybrid model outperforms traditional approaches, achieving the highest accuracy, precision, recall, F1-score, and area under the ROC curve. Beyond quantitative improvements, the large language model layer provides semantic explanations of detected threats, reduces false positives, and supports decision-making in enterprise security operations. This approach is particularly suitable for U.S. corporate environments, where real-time monitoring, interpretability, and compliance are critical. The findings highlight the potential of integrating advanced machine learning with contextual intelligence to create scalable, explainable, and operationally viable enterprise security solutions.
Keywords
Enterprise Security Management, Intrusion Detection, Large Language Models, Hybrid Machine Learning, CICIDS2017, Explainable AI, Cybersecurity Analytics
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Copyright (c) 2026 Mohammad Musa Mia, Md Mohibur Rahman, Md Abu Sayed, Rumana Akther Nipa, Sonjoy Kumar Dey, Kazi Abu Jahed, Md Yassir Mottalib
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