User-Agent Anomaly Detection via Sentence Embedding
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Solution Overview
Problem
Detecting fraudulent user-agents in computer networks is challenging due to the lack of effective methods for extracting features from user-agent strings and the limited availability of labeled datasets, making traditional rule-based and machine learning approaches impractical.
Innovation Solution
The use of sentence embedding algorithms, such as FastText, to convert user-agent strings into numerical data vectors, combined with a Gaussian Mixture Model for anomaly detection, allows for the calculation of predictive scores to determine fraudulent activity based on patterns learned from historic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional rule-based methods are used for user-agent detection, then implementation is simple, but detection accuracy is insufficient due to lack of effective feature extraction from user-agent strings
Solution Approach 1:
The patent replaces traditional rule-based detection mechanisms with sentence embedding algorithms (FastText, BERT) that automatically extract features from user-agent strings. These algorithms transform textual user-agent data into numerical vector representations, enabling machine learning models to detect fraudulent patterns without manual feature engineering, thus improving detection accuracy while managing complexity through automated processing.
2Reliability
If machine learning models are trained with limited labeled datasets, then model training is feasible, but detection reliability deteriorates due to insufficient training data
Solution Approach 1:
The patent applies preliminary action by using unsupervised learning approaches and anomaly detection techniques that can identify fraudulent user-agents without requiring extensive labeled training data. The system learns normal user-agent patterns from unlabeled historical data and detects deviations as anomalies, enabling reliable detection even with limited labeled examples.
3Measurement precision
If sentence embedding algorithms are used to convert user-agent strings into numerical vectors, then feature extraction effectiveness is improved, but computational complexity increases
Solution Approach 1:
The patent manages computational complexity by adjusting parameters of sentence embedding algorithms, such as vector dimensionality and processing batch sizes. The system optimizes the balance between feature extraction quality and computational resources by tuning these parameters based on available infrastructure and performance requirements.
4Adaptability or versatility
If anomaly detection models are trained on historic user-agent data, then ability to detect novel fraudulent patterns is improved, but processing time increases due to comprehensive pattern analysis
Solution Approach 1:
The patent implements partial action by using a two-stage detection process: first applying lightweight rule-based filtering for obvious cases, then applying comprehensive anomaly detection only to suspicious cases that pass the initial filter. This approach maintains high adaptability to novel fraud patterns while reducing average processing time by avoiding full analysis on all user-agents.
Data Source
AI summary
Systems and methods for user-agent anomaly detection are disclosed. In one embodiment, a user-agent string may be embedded into a numerical data vector representation using a sentence embedding algorithm (e.g., FastText). A predictive score may be calculated based on the numerical data vector representation and using a probability distribution function model that models a likelihood of occurrence of the observed user-agent based on patterns learned from historic payload data (e.g., a Gaussian Mixture Model). The predictive score may be compared to a threshold and, based on the comparison, it may be determined whether the user-agent is fraudulent.