Machine Learning Bot Detection for Low-False-Positive Web Requests
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Solution Overview
Problem
Current bot detection methods generate a high number of false positives, misidentifying legitimate users as bots due to the dynamic nature of bot IP addresses and user agents, making it difficult to accurately distinguish between human and bot web requests.
Innovation Solution
A system utilizing machine learning models, specifically ensemble models, to analyze web request logs, generate features, and determine whether a request is from a bot or a human, employing techniques like one-hot encoding, feature scaling, and autoencoders to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rules-based approaches are used to screen web traffic based on header information, then bot detection can be implemented, but a large number of false positives occur and legitimate users are misidentified as bots
Solution Approach 1:
The patent replaces rules-based detection mechanisms with machine learning models that automatically learn patterns from data. The system uses trained models to analyze web request features and distinguish bots from legitimate users, substituting static rule evaluation with dynamic, adaptive machine learning-based classification that reduces false positives while maintaining detection accuracy.
2Reliability
If machine learning models are used to improve bot detection accuracy, then false positives are reduced, but system complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models offline using historical web request data before deployment. Feature extraction methods, model architectures, and detection thresholds are established in advance through training processes, allowing the system to make accurate real-time predictions without complex runtime decision logic, thus managing system complexity while maintaining high reliability.
Data Source
AI summary
Methods, non-transitory computer readable media, security server devices, and security systems that perform bot detection using machine learning models are illustrated. With this technology, a request for a webpage from a client device to a server device is received. The request has one or more associated logs. A set of features is generated based on the one or more associated logs. The set of features are submitted as an input to a machine learning model. In response to an output of the machine learning model indicating the request for a webpage from the client device was initiated by a bot, the request is denied.