Author
Dr. Maria Kostopoulos
Associate Professor, Department of Information Systems and Analytics,
University of Athens, Greece
Abstract
Enterprise networks are vital infrastructures that need to be continuously watched after to guarantee excellent performance and availability. Conventional defect detection techniques frequently depend on reactive monitoring or manual intervention, which can result in extended outages and decreased productivity. In order to proactively discover and address network abnormalities, this article suggests an intelligent fault detection system that makes use of predictive analytics and Python-based automation. The suggested approach facilitates improved network resilience and quicker fault resolution by combining automation frameworks, real-time data processing, and machine learning algorithms. When compared to traditional methods, our methodology dramatically lowers mean time to detection (MTTD) and mean time to resolution (MTTR), according to experimental results from simulated business environments.
Keywords
Fault Detection Enterprise Networks Predictive Analytics Network Monitoring Python Automation Machine Learning Anomaly Detection Proactive Maintenance
How to Cite This Article
APA Citation
Kostopoulos, M. (2025). Intelligent Fault Detection in Enterprise Networks Using Python-based Automation and Predictive Analytics. International Journal of Economics and Management Intellectuals, 1(2), 18-27.
Conclusion
This study demonstrates the significant potential of an intelligent fault detection system that integrates predictive analytics with Python-based automation for enterprise networks. The proposed solution outperforms traditional rule-based systems by enabling earlier fault identification, reduced downtime, and automated remediation.
By combining machine learning models (Random Forest, LSTM, Isolation Forest) with powerful automation tools (Nornir, Netmiko, Paramiko), the system achieves substantial improvements in MTTD and MTTR while enhancing overall network reliability and operational efficiency.
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