VIPNANO: Monitoring of Virtual Private Cloud Networks for Automated Anomaly Detection

Abstract

Anomaly detection in enterprise networks is crucial for cybersecurity, system monitoring, and identifying outages. Despite extensive academic research, practical deployment of proposed mechanisms remains rare. The VIPNANO project investigates key shortcomings in academic approaches, focusing on two major obstacles: (1) reliance on unrealistic datasets that fail to reflect real-world complexity, and (2) overly complex machine learning models with impractical computational overhead. Additionally, we highlight a critical gap – the lack of rigorous real-world validation. Through systematic analysis, we emphasize the need to prioritize realistic data, scalability, and verifiable solutions to bridge the gap between theory and deployment.