Web Traffic Classification via User-Agent Segmentation
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Solution Overview
Problem
Web servers are vulnerable to automated attacks as existing security measures cannot differentiate between real web browsers and forged traffic, allowing malicious tools to deceive web services by mimicking legitimate User-Agent headers.
Innovation Solution
Implement unsupervised web traffic classification by monitoring and analyzing attribute data points from client interactions, grouping clients based on similarity, and flagging malicious activity when a client is included in multiple groups, thereby passively detecting and preventing malicious automation tool usage without modifying web pages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If attack tools forge User-Agent headers to mimic legitimate web browsers, then the ability to deceive web services is improved, but the capability to differentiate between real browsers and malicious tools deteriorates
Solution Approach 1:
The patent segments the User-Agent header into multiple distinct attributes (browser name, version, platform, engine) and analyzes them individually. By examining each component separately and checking for consistency across all segments, the system can identify forged headers that may have passed simple whole-string matching. This segmentation approach resolves the contradiction by maintaining high measurement precision while allowing the harmful deception capability to exist.
Solution Approach 2:
The patent introduces an intermediary verification process that acts as a mediator between the raw User-Agent header and the web service authentication. This intermediary layer performs consistency checks across multiple attributes and cross-references against known browser databases before allowing traffic to proceed. This intermediary mechanism enables the system to maintain both the ability to process legitimate traffic efficiently and the precision to detect sophisticated forgeries.
2Reliability
If web services implement sophisticated attack prevention techniques, then security against automated attacks is improved, but the complexity of the system increases
Solution Approach 1:
The patent implements preliminary action by pre-compiling databases of legitimate User-Agent strings and their attributes before runtime. During operation, the system only needs to perform simple lookups and consistency checks against these pre-prepared data structures, rather than performing complex analysis in real-time. This preliminary preparation maintains high security reliability while keeping the runtime system complexity low.
Solution Approach 2:
The system employs self-service mechanisms by automatically updating its database of legitimate User-Agent patterns and learning from new browser versions without requiring manual intervention. The consistency checking logic automatically identifies suspicious patterns and flags them for further analysis, reducing the need for complex manual security configuration and maintenance.
3Measurement precision
If web services monitor and analyze all client traffic in detail, then detection accuracy is improved, but the processor and network load increases
Solution Approach 1:
The patent applies partial action by selectively analyzing only specific attributes of the User-Agent header based on risk assessment. Rather than examining every single characteristic of every client connection in equal detail, the system performs shallow analysis on low-risk traffic and deeper attribute-by-attribute verification only on suspicious connections. This partial analysis approach maintains high detection accuracy for malicious traffic while significantly reducing overall processor and network load.
Data Source
AI summary
Techniques to facilitate web traffic classification are disclosed herein. In at least one implementation, web traffic between a plurality of clients and at least one web server is monitored, and the web traffic is analyzed to determine attribute data points associated with each individual client of the plurality of clients. The attribute data points associated with each individual client are compared to define a plurality of client groups based on similarities in the attribute data points among each individual client. A client of the plurality of clients is identified as malicious when the client is included in more than one of the client groups.