Network Traffic Anomaly Detection Using Holt-Winters Forecasting
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Solution Overview
Problem
Managing network traffic anomalies is challenging due to high costs and inefficiencies in detecting deviations from normal traffic patterns, leading to increased business costs and complexity in maintaining network performance.
Innovation Solution
A system and method using a Holt-Winters model to forecast network traffic with daily and weekly seasonality, generating prediction intervals, and identifying outliers by comparing observed traffic to these intervals, which allows for real-time detection of anomalies and efficient adaptation to changing network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network traffic monitoring methods are used, then network management can be performed, but false positives increase and detection accuracy decreases
Solution Approach 1:
The system dynamically adapts to changing network conditions by continuously updating the forecasting model with new data, allowing the anomaly detection thresholds to evolve with normal traffic patterns rather than using static baselines
Solution Approach 2:
The system changes the parameters used for anomaly detection by incorporating multiple seasonality components (daily and weekly patterns) and adjusting the prediction interval based on observed error distributions, improving detection accuracy while reducing false positives
2Measurement precision
If complex anomaly detection models are implemented, then detection accuracy improves, but computational cost increases
Solution Approach 1:
The system segments the anomaly detection process into distinct components: forecasting using Holt-Winters model, calculating prediction intervals based on error distributions, and comparing observed traffic against these intervals, allowing for efficient computation at each stage
Solution Approach 2:
The system uses the network traffic data itself to generate the forecasting model and prediction intervals, eliminating the need for external training datasets or complex parameter tuning while maintaining high detection accuracy
Data Source
AI summary
According to an aspect of the invention, a system and method is configured to detect time series outliers in network traffic.