Client Fingerprinting for Filtering Spoofed Internet Traffic
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Solution Overview
Problem
Existing internet traffic filtering methods struggle to effectively identify and distinguish between legitimate user devices and computer-implemented clients that spoof or hide their identities, particularly through techniques like VPNs and proxies, making it difficult to manage network traffic and ensure security.
Innovation Solution
A system and method that utilizes client fingerprinting based on lower-level network protocol data, such as TLS/SSL handshake packets, to generate a unique fingerprint for client classification, enabling accurate identification and management of client types, even through VPNs or proxies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cookies, browser plugins, or client-side software are used for identification, then client identification capability is improved, but device complexity and user burden increase
Solution Approach 1:
The patent extracts the identification capability from complex client-side components (cookies, plugins, software) and relocates it to the network infrastructure through passive observation of standard TCP/IP handshake packets. This allows client identification without adding complexity to client devices.
Solution Approach 2:
The patent introduces a network-based fingerprinting system as an intermediary that identifies clients through passive analysis of network traffic characteristics during standard TCP connections. This intermediary approach eliminates the need for complex client-side identification mechanisms.
2Productivity
If dynamic IP address allocation is used, then IP address utilization is improved, but reliability of connection identification deteriorates
Solution Approach 1:
The patent segments the identification process into multiple independent fingerprinting parameters (TCP window size, maximum segment size, round trip time, packet inter-arrival times, sequence number increments) rather than relying on a single static IP address. This segmentation allows reliable identification despite dynamic IP allocation.
Solution Approach 2:
The patent changes from using static IP addresses as identification parameters to using dynamic network behavior parameters that remain consistent for each client regardless of IP address changes. These parameters include TCP handshake characteristics and packet timing patterns that uniquely identify each client.
3Measurement precision
If detailed client information is collected for filtering, then filtering accuracy is improved, but loss of information and privacy concerns increase
Solution Approach 1:
The patent applies local quality by collecting only the specific network parameters needed for identification (TCP handshake characteristics, packet timing) rather than comprehensive client information. This localized data collection achieves filtering accuracy while minimizing privacy intrusion.
Solution Approach 2:
The patent converts potentially harmful detailed client monitoring into beneficial passive observation of publicly visible TCP/IP handshake characteristics. The system uses parameters that are already exposed during standard protocol execution, transforming what could be invasive monitoring into a privacy-preserving identification method.
Data Source
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AI summary
A system and method that includes receiving a client data packet from network traffic with a client device; extracting a set of packet components from the client data packet; generating a client fingerprint from the set of packet components; assigning a client type to the network traffic using the client fingerprint; and optionally filtering the network traffic of the client device based at least in part on the client type.