Behavioral Web Bot Detection with Tiered Machine Learning
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Solution Overview
Problem
Existing web bot detection methods are ineffective against evolving malicious bots due to a technological arms race, leading to unwanted network traffic, financial losses, and fraudulent activities like click fraud and data breaches, with traditional solutions impacting user experience and being easily circumvented.
Innovation Solution
A tiered approach using a heuristics module, semi-supervised generative adversarial network (SGAN), and deep graph convolutional neural network (DGCNN) for behavioral analysis and machine learning to detect and mitigate web bots by analyzing client agent characteristics and behaviors, leveraging both labeled and unlabeled data to improve classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional bot detection methods are used, then implementation is simple, but detection accuracy deteriorates due to evolving malicious bots
Solution Approach 1:
The detection system is divided into multiple specialized components: a heuristics module for rule-based detection, a semi-supervised GAN for learning from labeled and unlabeled data, and a graph convolutional neural network for analyzing interaction patterns. Each component handles specific aspects of bot detection, improving overall accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system combines multiple detection approaches (heuristic rules, generative adversarial networks, graph neural networks) into a composite detection framework. This multi-layered composite approach leverages the strengths of each method to achieve high detection accuracy against evolving bots while distributing the computational complexity across different specialized components.
2Measurement precision
If multi-tier machine learning approach is implemented, then detection accuracy improves, but computational resources increase
Solution Approach 1:
The heuristics module performs preliminary filtering and classification of client agents using rule-based methods before passing suspicious cases to the more computationally intensive GAN and graph neural network components. This preliminary action reduces the volume of data requiring heavy computational processing, optimizing resource utilization while maintaining high classification accuracy.
Solution Approach 2:
The semi-supervised GAN learns from both labeled and unlabeled data, applying partial supervision where complete labeling is not necessary. This approach achieves high detection accuracy without requiring excessive computational resources for full supervised training on all data, balancing precision with resource efficiency.
3Reliability
If behavioral analysis is performed on all clients, then bot detection improves, but processing time increases
Solution Approach 1:
The system applies different levels of analysis depth to different client agents based on their characteristics and behavior patterns. High-risk clients suspected of being bots undergo comprehensive behavioral analysis with graph convolutional processing, while low-risk clients receive lighter processing. This local quality approach maintains high detection reliability for suspicious cases while reducing average processing time across all clients.
Data Source
AI summary
Techniques for web bot detection using behavioral analysis and machine learning are disclosed. In an example method, a processing device receives an indication of a network interaction by a client agent, from which behaviors of the client agent can be determined. A heuristics module may classify the client agent as in an unknown class based on the behaviors of the client agent. A trained adversarial neural network may also classify the client agent as in the unknown class. The processing device then generates a graph representation of the network interaction. A trained graph convolutional neural network may classify the client agent as in a bot class using the graph representation. Based on the classification of the client agent as a bot, the processing device executes a command to cause a bot countermeasure and generates a notification including information about the behaviors of the client agent.