Traffic Anomaly Detection via Partial Packet Inspection
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Solution Overview
Problem
Service provider networks face challenges in detecting traffic anomalies in real-time due to high processing demands, which can lead to congestion and service disruptions, as existing techniques often degrade network performance and reduce throughput.
Innovation Solution
Implementing traffic steering and real-time analytics techniques using an analytics and reporting server to monitor network traffic across multiple layers of the OSI model, performing stateful packet inspection without examining payloads, allowing for anomaly detection without hindering network throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing techniques are used to detect traffic anomalies in real-time, then anomaly detection capability is improved, but network performance degrades and throughput is reduced
Solution Approach 1:
The patent extracts only the necessary packet information (headers and control data) for anomaly detection while leaving the packet payloads unexamined. This selective extraction approach enables real-time anomaly detection without the processing overhead of examining entire packet contents, thus maintaining network throughput while improving detection capability.
Solution Approach 2:
The patent applies partial inspection by analyzing only specific portions of network packets (such as headers and control fields) rather than performing complete packet inspection. This partial action suffices for anomaly detection purposes while significantly reducing processing requirements and preserving network performance.
2Measurement precision
If deep packet inspection is performed to identify anomalies, then measurement precision is improved, but processing capacity requirements increase and network performance degrades
Solution Approach 1:
The patent extracts and analyzes only the essential packet fields necessary for anomaly detection (such as headers and control data) while excluding payload examination. This targeted extraction maintains sufficient measurement precision for identifying traffic anomalies while dramatically reducing the processing capacity required compared to deep packet inspection.
Solution Approach 2:
The patent employs partial inspection of network packets, examining only the portions needed for anomaly detection rather than performing comprehensive deep packet inspection. This partial approach provides adequate measurement precision for anomaly identification while consuming significantly less processing capacity.
3Reliability
If real-time monitoring is implemented to detect traffic conditions, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent extracts and monitors only the critical packet fields necessary for detecting traffic conditions and anomalies, rather than implementing comprehensive monitoring of all packet contents. This selective extraction approach improves reliability for detecting important traffic conditions while keeping the monitoring system complexity manageable.
Solution Approach 2:
The patent implements real-time monitoring through partial packet inspection, focusing only on the essential fields needed for traffic condition detection. This partial monitoring approach achieves the required reliability for detecting traffic anomalies while avoiding the excessive complexity that would result from full-depth packet analysis.
Data Source
AI summary
A system, associated with a service provider network, is configured to monitor traffic, that is traveling to or from the service provider network, to obtain traffic metrics that correspond to a collection of network layers, where the network layers; process the traffic metrics with respect to each of the network layers to identify an anomaly, associated with the traffic, that corresponds to at least one of the network layers; send a request for packets associated with the traffic based on the identification of the anomaly; receive copies of the packets associated with the traffic; analyze the copies of the packets to obtain information associated with the anomaly; and send a notification that indicates that the anomaly has been identified, where the notification includes the traffic metrics associated with the traffic or the information associated with the anomaly.