Host Classification via Multi-Dimensional Fingerprinting
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Solution Overview
Problem
In cloud-based networks, distinguishing between human-operated hosts and bot scripts sharing the same IP address is challenging, leading to inaccurate bot detection and potential blocking of human users, which is exacerbated by the difficulty in leveraging existing metadata for real-time differentiation.
Innovation Solution
A host classification system that evaluates data strings, including request parameters and metadata, using rules to differentiate between human users and bot scripts, and inhibits content delivery to detected bots, employing machine-learning techniques to improve accuracy and prevent fraudulent activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If multiple hosts share the same IP address to communicate with external servers, then network resource utilization is improved, but host differentiation and bot detection accuracy deteriorate
Solution Approach 1:
The patent segments host identification from IP address to multiple dimensions including device fingerprinting attributes (screen resolution, browser version, OS type), behavioral patterns (request timing, navigation paths), and metadata (cookies, headers). This segmentation allows unique identification of each host even when they share the same IP address, resolving the contradiction between network resource utilization and host differentiation accuracy.
2Difficulty of detecting and measuring
If device fingerprinting techniques are used to differentiate hosts, then host identification capability is improved, but classification collisions increase
Solution Approach 1:
The patent merges multiple fingerprinting attributes (device characteristics, behavioral patterns, metadata) into a composite host profile. By combining these diverse dimensions rather than relying on single attributes, the system reduces classification collisions and improves reliability of host differentiation, as the probability of multiple hosts having identical profiles across all dimensions is significantly reduced.
3Productivity
If real-time bot detection is implemented in data exchange systems, then fraudulent activity mitigation is improved, but detection accuracy deteriorates due to limited metadata availability
Solution Approach 1:
The patent performs preliminary classification of hosts during the data exchange process itself, using metadata that becomes available in real-time (request parameters, response patterns, timing information). By integrating bot detection into the existing data exchange workflow rather than as a separate pre-processing step, the system achieves both real-time detection speed and improved accuracy through contextual metadata.
Data Source
AI summary
Certain aspects and features of the present disclosure relate to systems and methods for automatically classifying hosts in real-time. For instance, classifying hosts as bots, and subsequently mitigating or blocking traffic from the hosts classified as bots can be advantageous in real-time data exchange systems. In a real-time data exchange system, data can be exchanged between a server and a target host in real-time when the target host accesses a webpage. Inhibiting data communication between servers and hosts operated by bot scripts can reduce fraudulent activity. In some implementations, hosts can be automatically classified into various groups based at least in part on the data included in requests received from the hosts.