Local Fraudulent Website Detection With Lightweight Browser AI
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Solution Overview
Problem
Current systems are inadequate in detecting and preventing technologically sophisticated fraudulent websites that quickly attack users and vanish before detection, leading to widespread victimization.
Innovation Solution
A fraudulent website detection system using a lightweight machine learning model executed locally on client devices to analyze website images and data in real time, employing filters and threat assessment models to identify and block fraudulent sites, with collaboration for aggregated reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional remote detection systems are used to identify fraudulent websites, then detection capability is provided, but detection speed is too slow allowing scammers to attack and vanish within hours
Solution Approach 1:
The patent introduces a lightweight machine learning model as an intermediary component that bridges the gap between remote detection systems and real-time local analysis. This model enables client devices to perform autonomous fraud detection while maintaining the ability to collaborate with centralized threat intelligence platforms, thus achieving both speed and reliability.
Solution Approach 2:
The detection system is segmented into multiple components: a centralized threat detection service that aggregates data and trains models, and distributed lightweight models deployed on client devices that perform local real-time detection. This segmentation allows the system to combine the reliability of centralized analysis with the speed of local execution.
2Measurement precision
If heavy machine learning models are deployed for accurate fraud detection, then detection accuracy is improved, but computational burden increases making real-time execution difficult
Solution Approach 1:
The patent employs lightweight machine learning models that are computationally inexpensive and can be executed on resource-constrained client devices. These simplified models trade some complexity for speed and efficiency, enabling real-time detection without requiring heavy computational resources. The models are designed to be deployed widely across many devices rather than being centralized on powerful servers.
Solution Approach 2:
The patent transforms the detection problem by changing the parameters of the machine learning model - using smaller model sizes, reduced feature sets, and simplified algorithms that maintain adequate accuracy while dramatically reducing computational requirements. This allows the model to execute efficiently on client devices with limited processing power.
3Productivity
If local detection is implemented on client devices, then real-time response is achieved, but device resources are consumed
Solution Approach 1:
The patent implements a hybrid detection approach where the lightweight local model performs partial detection autonomously, and only cases requiring more sophisticated analysis are escalated to the centralized threat detection service. This partial action approach enables real-time response for common cases while conserving device resources by avoiding full computational overhead for every detection task.
Data Source
AI summary
This disclosure describes a fraudulent website detection system that provides a framework for locally detecting fraudulent websites on a client device. For example, using a local lightweight machine learning model, the fraudulent website detection system can detect and respond to fraudulent websites in real time. In some examples, the fraudulent website detection system is integrated into a web browser to promptly identify fraudulent websites. Moreover, the fraudulent website detection system, operating on multiple client devices, can collaborate with an online threat detection system to quickly notify other client devices about fraudulent websites and to utilize aggregated reports to improve the lightweight machine learning model.