Sniffer Device Packet Collection for Network Anomaly Detection
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Solution Overview
Problem
Determining whether issues encountered by communicating entities in a network are caused by abnormal network conditions is challenging due to the large volume of network packets, requiring specialized expertise and time-consuming analysis.
Innovation Solution
A sniffer device connected to the network monitors communication and collects network-level packets into a capture data structure, such as a PCAP file, to efficiently identify and analyze abnormal conditions by comparing packet characteristics with predefined thresholds, allowing for real-time analysis and reduced data storage needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If network packets are collected for analysis to determine abnormal conditions, then analysis capability is improved, but data volume and storage requirements increase significantly
Solution Approach 1:
The patent extracts only the necessary packet information (headers and payload) related to abnormal conditions rather than collecting all network traffic data. This selective extraction reduces data volume while maintaining detection accuracy for abnormal conditions.
Solution Approach 2:
The patent segments packet collection by filtering packets based on specific criteria (such as protocol type, source/destination addresses, and abnormal condition indicators). This segmentation allows analysis of only relevant packets, reducing overall data volume while preserving measurement precision for abnormal condition detection.
2Measurement precision
If comprehensive network packet analysis is performed, then detection capability is improved, but analysis time and processing complexity increase
Solution Approach 1:
The patent performs preliminary filtering and classification of packets before detailed analysis. By pre-identifying packets that match abnormal condition criteria (such as unusual packet sizes, frequencies, or patterns), the system reduces analysis time while maintaining detection accuracy.
Solution Approach 2:
The patent implements feedback mechanisms where detected abnormal conditions trigger targeted analysis of related packets. This feedback loop allows the system to focus analysis resources on areas where abnormalities are detected, reducing overall analysis time while maintaining precision.
3Measurement precision
If specialized expertise is required for network packet analysis, then analysis accuracy is improved, but operational complexity and cost increase
Solution Approach 1:
The patent implements automated packet analysis and abnormal condition detection that performs self-service without requiring specialized expertise. The system automatically filters, analyzes, and reports on network packets, reducing operational complexity while maintaining accuracy through algorithmic decision-making.
Solution Approach 2:
The patent transforms complex packet analysis into simplified parameter-based evaluation. By converting network packet data into measurable parameters (such as packet count thresholds, time intervals, and pattern matches), the system reduces operational complexity while maintaining analysis accuracy through quantitative criteria.
4Reliability
If all network packets are collected for thorough analysis, then detection completeness is improved, but storage requirements and system resource consumption increase
Solution Approach 1:
The patent applies local quality by collecting and storing only packet data relevant to specific abnormal conditions or particular network segments. Rather than storing all network traffic, the system focuses storage resources on packets matching predefined criteria, reducing storage requirements while maintaining detection completeness for abnormal conditions.
Data Source
AI summary
A sniffer device determines whether an abnormal condition is present in a network communication. In response to determining that the abnormal condition is present, the sniffer device collects network-level packets into a data structure. The data structure containing the collected network-level packets can be analyzed for determining whether the abnormal condition caused an issue with a communicating entity.