Out-of-Path DDoS Detection via Enriched Flow Data Analysis
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Solution Overview
Problem
Existing DDoS attack detection methods, particularly out-of-path (OOP) techniques, face inefficiencies and inaccuracies due to reliance on Flow data alone, leading to high false positives and negatives, and struggle to accurately identify entities and correlate attack traffic with network topology, limiting effective detection and mitigation in large-scale networks.
Innovation Solution
The proposed solution involves enriching Flow data with additional types of data from various sources, using machine learning techniques to analyze and categorize traffic patterns, and deploying an OOP detector that can dynamically create clusters of entities based on traffic behavior, enabling more accurate anomaly detection and entity-aware DDoS attack identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If out-of-path detection is used instead of inline detection, then deployment convenience is improved, but detection accuracy and response speed deteriorate
Solution Approach 1:
The patent combines multiple data sources (Flow data, NetFlow, telemetry data, entity identity information) into a unified detection framework. By merging these diverse data streams and enriching them with entity context, the system achieves both out-of-path deployment convenience and inline-level detection accuracy, resolving the contradiction between ease of operation and measurement precision.
2Device complexity
If Flow data only is used for detection, then data processing simplicity is improved, but detection accuracy deteriorates due to high false positives and negatives
Solution Approach 1:
The patent segments the detection process into multiple independent modules: Flow data collection, telemetry data collection, entity identity resolution, enrichment processing, and anomaly detection. Each module handles a specific aspect independently, maintaining processing simplicity while collectively achieving high detection accuracy through multi-dimensional analysis.
3Device complexity
If manual threshold configuration is used, then detection rule simplicity is improved, but detection accuracy deteriorates due to excessive false positives and negatives
Solution Approach 1:
The system implements feedback mechanisms where detection results and traffic patterns continuously inform threshold adjustments. Machine learning models analyze historical data and automatically optimize detection thresholds, providing both simplicity (automatic adjustment) and high accuracy (data-driven optimization) simultaneously.
4Device complexity
If IP address based telemetry is used for anomaly detection, then data collection simplicity is improved, but entity identification accuracy deteriorates
Solution Approach 1:
The patent introduces entity identity resolution as an intermediary layer between IP address telemetry and anomaly detection. This mediator resolves IP addresses to actual entity identities using multiple data sources, maintaining simple data collection while dramatically improving entity identification accuracy by bridging the gap between network addresses and real-world entities.
5Reliability
If existing anomaly detection solutions are applied to large-scale networks, then scalability is challenged, but detection coverage is improved
Solution Approach 1:
The patent adds the entity identity dimension to traditional IP-based detection. By enriching Flow data with entity context (organizing principle, application, device type), the system transforms the detection space from simple IP addresses to multi-dimensional entity profiles. This enables scalable detection in large networks by providing meaningful aggregation and correlation capabilities across hundreds of thousands of IPs.
Data Source
AI summary
A system and method for out-of-path detection of cyber-attacks are provided. The method includes receiving, by a detector, a plurality of data feeds from a plurality of data sources, wherein the detector is communicatively connected to the plurality of data sources; processing, by the detector, the plurality of received data feeds to generate enriched Flow data sets; analyzing the enriched Flow data sets to detect a potential cyber-attack; and upon detection of a potential cyber-attack, providing indication to each network entity of the network entities that is under attack.