Click Spam Detection via Normal User Behavioral Patterns
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Solution Overview
Problem
Current methods for detecting click spam in communication systems are inadequate, as they rely on identifying malicious individuals and become increasingly difficult as click spamming techniques become more sophisticated, leading to unnecessary charges for companies.
Innovation Solution
The system detects click spam by identifying normal user behavioral patterns, tracking user activities such as image loading, cookie age, JavaScript usage, browser type, and visit intervals to determine a click rate, and compares this to the overall user population to identify spammed advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current methods rely on identifying malicious individuals to detect click spam, then detection capability is maintained at basic level, but detection effectiveness deteriorates as click spamming techniques become more sophisticated
Solution Approach 1:
Instead of trying to identify malicious click spammers directly, the patent inverts the approach by identifying normal users through behavioral pattern analysis and then detecting anomalies. The system establishes baseline behavioral patterns for legitimate users and flags deviations from these patterns, making detection more effective against sophisticated spamming techniques that attempt to mimic human behavior.
Solution Approach 2:
The patent transforms the detection problem from identifying malicious actors to analyzing behavioral parameters and patterns. By monitoring multiple behavioral parameters (click timing, navigation patterns, session duration, interaction sequences) and comparing them against established norms, the system can detect click spam through parameter deviations rather than attempting to directly identify spammers.
2Measurement precision
If the system tracks detailed user activities to distinguish normal users from spammers, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent implements a multi-functional behavioral analysis system that uses a single framework to perform multiple detection functions. The same behavioral pattern matching engine that identifies normal users also detects anomalies, and the system simultaneously monitors various behavioral parameters (click patterns, navigation, timing) through a unified analysis mechanism, reducing overall system complexity despite comprehensive tracking.
Solution Approach 2:
The system automatically establishes baseline behavioral patterns from observed user activities and uses these self-generated patterns for subsequent detection. The behavioral analysis framework learns and adapts to normal user behavior autonomously, reducing the need for manual configuration and complex rule-setting while maintaining high detection accuracy.
Data Source
AI summary
A system detects spamming. The system identifies normal users visiting a web site and determines an occurrence of spamming on the web site based at least in part on the identified normal users.