Unlocking Network Insights: Leveraging Statistics and AI for Anomaly and Trend Detection in Large-Scale Data

Abstract

The exponential growth of network traffic and the increasing sophistication of cyber threats necessitate advanced techniques for data analysis. This article explores how a combination of statistical methods and Artificial Intelligence can be effectively employed to derive critical insights from large-scale network data, such as DNS and HTTP requests. The data processing pipeline is examined from collection and storage in distributed architectures to the application of statistical rules for anomaly detection, the utilization of cloud monitoring services, and the power of AI in uncovering complex anomalies and evolving trends in network behavior. Real-world use cases, including DDoS detection and the identification of significant traffic spikes, illustrate the practical value of this integrated approach in enhancing network security and performance monitoring capabilities.