AI-Based Anomaly Detection for Enterprise Network Security and Cloud Threat Prevention Using Behavioral Analytics

Dr. Keon Myung Lee; Dr. Keon Myung Lee

doi:10.15662/ijetemr.2025.1006012

AI-Based Anomaly Detection for Enterprise Network Security and Cloud Threat Prevention Using Behavioral Analytics

Dr. Keon Myung Lee

Department of Computer Science and Engineering, Jeonbuk National University, Jeonju, South Korea

Abstract

The increasing sophistication of cyberattacks and the rapid expansion of cloud computing have created significant challenges for enterprise network security. Traditional security mechanisms, which primarily depend on predefined rules, signatures, and static thresholds, often struggle to identify unknown threats, advanced persistent attacks, and subtle deviations in user or system behavior. Artificial intelligence (AI)-based anomaly detection offers a proactive approach by analyzing behavioral patterns across network traffic, cloud workloads, user activities, and system events. This study examines an integrated security framework that combines machine learning, behavioral analytics, and real-time monitoring to detect suspicious activities and prevent cloud-based threats. The proposed approach incorporates supervised and unsupervised learning techniques to identify known attack patterns and previously unseen anomalies. Behavioral profiles are constructed using network flows, authentication events, access histories, resource utilization, and cloud audit logs. The methodology emphasizes data preprocessing, feature engineering, model training, anomaly scoring, and comparative evaluation using established cybersecurity metrics. Detection performance is assessed through precision, recall, F1-score, false-positive rate, and detection latency. The framework also considers automated response mechanisms, explainable AI, privacy protection, and adversarial resilience. By integrating contextual behavioral analysis with adaptive threat detection, the proposed framework aims to strengthen enterprise security monitoring, improve incident response, and reduce the operational impact of cyber threats. The study provides a methodological foundation for evaluating AI-driven anomaly detection in increasingly distributed, dynamic, and complex enterprise network and cloud environments

Article Information

Journal	International Journal of Emerging Trends in Engineering and Management Research
Volume (Issue)	Vol. 10 No. 6 (2025): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)
DOI	https://doi.org/10.15662/ijetemr.2025.1006012
Pages	18984-18993
Published	December 15, 2025
Copyright	All rights reserved
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Dr. Keon Myung Lee (%2025). AI-Based Anomaly Detection for Enterprise Network Security and Cloud Threat Prevention Using Behavioral Analytics. International Journal of Emerging Trends in Engineering and Management Research , Vol. 10 No. 6 (2025): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 18984-18993. https://doi.org/10.15662/ijetemr.2025.1006012

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