Topology-Aware Bot Detection Model for Click Activity
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Solution Overview
Problem
Current bot detection methods are inadequate in distinguishing between human and bot activity in click log data, leading to network congestion, security concerns, and inaccurate analysis of web traffic, as they rely on standard rules that fail to adapt to the diverse and evolving behaviors of bots.
Innovation Solution
The use of topology-aware machine learning models trained with a topological loss function to classify click activity data into human and bot classes, allowing for the filtration of bot activity and modification of user interfaces to optimize network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard rules-based bot detection methods are used, then the system is simple to implement, but the detection precision is insufficient to distinguish between human and bot activity
Solution Approach 1:
The patent replaces traditional rules-based mechanical detection systems with a machine learning model that uses topological loss functions to classify click activity. This substitution enables the system to automatically learn complex patterns in user behavior data, significantly improving bot detection precision while the model handles the complexity internally rather than requiring manual rule configuration.
Solution Approach 2:
The patent introduces topological loss functions as a new parameter framework for training the machine learning model. By changing the optimization parameters from standard loss functions to topology-aware loss functions, the system can better capture the structural characteristics of human versus bot click patterns, thereby improving detection precision without requiring proportional increases in system complexity.
2Loss of information
If all click activity data is processed and analyzed, then complete traffic analysis is achieved, but network resources are excessively consumed due to bot traffic
Solution Approach 1:
The patent extracts and separates bot-generated click activity from human-generated click activity using the trained machine learning model. By identifying and extracting bot traffic patterns through topological classification, the system can filter out malicious or unnecessary bot data before further analysis, reducing network resource consumption while preserving complete analysis of legitimate human traffic.
Solution Approach 2:
The patent performs preliminary classification of click activity as bot or human using the machine learning model before proceeding to detailed traffic analysis. This preliminary action filters out bot traffic early in the processing pipeline, preventing unnecessary consumption of network resources on analyzing malicious or irrelevant bot-generated clicks, while ensuring human traffic receives complete analysis.
3Measurement precision
If topology-aware machine learning models are used, then bot detection precision is improved, but the device complexity increases
Solution Approach 1:
The patent replaces manual system configuration and rule-based complexity with an automated machine learning model that learns topological patterns independently. The model's internal architecture handles the computational complexity of topological loss function calculations, while the external system benefits from improved precision without proportional increases in operational complexity.
Data Source
AI summary
In some embodiments, techniques for identifying bot activity are provided. For example, a process may involve receiving a plurality of samples, wherein each sample is a record of click activity; classifying the plurality of samples among a first class and a second class, using a machine learning model trained by a training process, to produce a corresponding plurality of classification predictions; filtering click activity data, based on information from the plurality of classification predictions, to produce filtered click activity data; and causing a user interface of a computing environment to be modified based on information from the filtered click activity data. The training process includes training the machine learning model to classify samples among the first and second classes, using a training set of samples of the first class, a training set of samples of the second class, and values of a topological loss function calculated based on the training sets.