P2P Network Flow Detection and Classification Modules
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Solution Overview
Problem
Existing systems lack effective methods to detect and classify peer-to-peer (P2P) network flows, which can be used by both legitimate applications and malicious software like botnets, posing challenges in distinguishing between benign and malicious traffic.
Innovation Solution
A system and method that monitor and analyze network traffic using flow detection and classification modules to identify P2P flows, employing modules for flow creation, detection, classification, clustering, and identification, and utilizing DNS reputation analysis and payload inspection to differentiate between legitimate and malicious P2P activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If network traffic monitoring is performed to detect P2P flows, then detection capability is improved, but system complexity increases
Solution Approach 1:
The system segments P2P flow detection into multiple independent modules: flow creation module, flow detection module, flow classification module, and flow clustering module. Each module performs a specific function in the detection pipeline, making the overall complex system manageable through functional segmentation.
Solution Approach 2:
The patent introduces flow representation data structures as intermediaries that capture essential P2P flow characteristics without requiring full packet inspection. This intermediary layer simplifies the detection process by working with summarized flow data rather than raw network packets.
2Measurement precision
If flow classification and clustering modules are added to distinguish P2P applications, then classification accuracy is improved, but processing time increases
Solution Approach 1:
The flow classification and clustering modules perform preliminary categorization of P2P flows based on extracted features before detailed analysis. By pre-grouping flows with similar characteristics, the system reduces the computational burden of subsequent deep inspection and accelerates overall processing.
Solution Approach 2:
The system transforms raw flow data into standardized feature representations that capture essential P2P application characteristics. This parameter transformation enables efficient comparison and classification while maintaining high accuracy in distinguishing different P2P applications.
3Measurement precision
If DNS reputation analysis and payload inspection are used to differentiate malicious traffic, then detection accuracy is improved, but computational load increases
Solution Approach 1:
The system applies DNS reputation analysis and payload inspection selectively to flows that exhibit suspicious characteristics identified by the flow detection module. Rather than applying these computationally intensive techniques to all traffic, the system focuses resources on locally relevant suspicious flows, reducing overall computational load while maintaining high detection accuracy.
Data Source
AI summary
A system and method comprising: receiving network flow data; identifying a peer to peer network flow within the network flow data comparing the peer to peer network flow to a known peer to peer application flow; labeling the peer to peer network flow as the known peer to peer application flow when the peer to peer network flow matches the known peer to peer application flow; and creating a data set to be associated with the labeled peer to flow.