Network Anomaly Detection via Dynamic Baseline Adjustment
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Solution Overview
Problem
Traditional network anomaly detection methods rely on historical baselines and upfront assumptions, which become outdated quickly due to dynamic network changes, leading to high false positive rates and inefficiencies in detecting DoS and DDoS attacks.
Innovation Solution
A novel approach that computes trends in network traffic characteristics using multi-dimensional analysis and fuzzy logic to classify anomalies in real-time, eliminating the need for historical baselines and reducing false positives by assessing multiple characteristics simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional baseline anomaly detection methods are used, then historical patterns can be identified, but false positive rates increase and detection reliability decreases due to outdated baselines
Solution Approach 1:
The patent implements dynamic baseline adjustment by continuously updating traffic patterns as new data becomes available. The system transitions from static historical baselines to dynamic adaptive baselines that evolve with network conditions, using mechanisms like baseline drift detection and automatic recalibration to maintain accuracy without generating false positives from outdated patterns.
Solution Approach 2:
The system changes detection parameters adaptively by adjusting sensitivity thresholds, time window sizes, and aggregation levels based on current network conditions. This allows the anomaly detection mechanism to optimize its parameters in real-time, improving measurement precision while maintaining reliability across varying traffic conditions.
2Reliability
If multiple network traffic characteristics are analyzed simultaneously, then anomaly detection confidence increases, but system complexity increases
Solution Approach 1:
The patent segments the anomaly detection system into independent modular components: traffic characteristic analyzers, baseline comparison modules, confidence calculation units, and anomaly classification components. Each module handles specific aspects of multi-dimensional analysis independently, reducing overall system complexity while maintaining high detection confidence through coordinated operation of specialized sub-systems.
Solution Approach 2:
The system implements universal data structures and processing pipelines that can handle multiple network traffic characteristics (packet rates, byte counts, flow durations, protocol distributions) through a single unified anomaly detection framework. This multi-functional approach allows simultaneous analysis of diverse traffic metrics without requiring separate complex systems for each characteristic.
3Loss of time
If real-time anomaly detection is implemented, then timely alerting is achieved, but processing speed requirements increase system resource demands
Solution Approach 1:
The patent implements periodic sampling and batch processing mechanisms where traffic characteristics are analyzed at optimized intervals rather than continuously. The system uses configurable time windows and aggregation periods to balance real-time detection requirements with resource constraints, achieving timely alerting through periodic updates rather than continuous heavy processing.
Solution Approach 2:
The system applies partial analysis by focusing computational resources on the most critical traffic characteristics and high-risk network segments. Instead of fully processing all traffic data at maximum depth, the system performs selective analysis on suspicious patterns and escalates only when confidence thresholds are met, reducing overall resource consumption while maintaining detection speed.
Data Source
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AI summary
The present invention relates to systems and methods for detecting anomalies in computer network traffic with fewer false positives and without the need for time-consuming and unreliable historical baselines. Upon detection, traffic anomalies can be processed to determine valuable network insights, including health of interfaces, devices and network services, as well as to provide timely alerts in the event of attack.