Scam Detection System Using Pre-filtered Web Page Pattern Analysis
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Solution Overview
Problem
Current systems are ineffective in detecting emerging scam campaigns in real time, as they often remain behind the curve in adapting to the latest online trends, leading to unsuspecting users being tricked into providing sensitive information through fake websites that use search engine optimization techniques to appear at the top of search results.
Innovation Solution
A computer-implemented method that identifies pre-filtered web pages based on predetermined criteria, performs pattern detection using natural language processing and image analysis, and compares the detected patterns to user inputs to generate real-time notifications of potential scams, utilizing a database repository of common scam techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current detection systems are used to identify scam websites, then detection capability is limited, but real-time detection of emerging scam campaigns cannot be achieved
Solution Approach 1:
The system performs preliminary actions by proactively crawling and analyzing web pages before they are exploited by scammers. It pre-identifies potential scam patterns and builds a repository of suspicious web pages in advance, enabling real-time detection when users access these pages without requiring the system to react after scams emerge
Solution Approach 2:
The system dynamically adapts its detection capabilities by continuously crawling new web pages, updating pattern repositories, and adjusting analysis parameters based on emerging scam techniques. The dynamic nature allows the system to evolve with changing scam methodologies while maintaining reliable detection
2Measurement precision
If comprehensive analysis of web pages is performed to detect scams, then detection accuracy is improved, but processing time and system complexity increase
Solution Approach 1:
The system segments the web page analysis into distinct modules: crawling module, natural language processing module, image analysis module, and pattern matching module. Each module handles specific aspects of analysis independently, improving detection accuracy through comprehensive examination while managing complexity through modular architecture
Solution Approach 2:
The system performs preliminary filtering and analysis on web pages during the crawling phase, pre-processing content to extract key features before user interaction. This preliminary action reduces the complexity of real-time analysis by preparing data in advance, maintaining both accuracy and efficiency
3Measurement precision
If multiple analysis methods are used on web pages, then pattern detection accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The system applies different analysis methods selectively based on local characteristics of web pages. Natural language processing is applied to text content, image analysis to visual elements, and pattern matching to structural features. This localized application of analysis methods improves detection accuracy while optimizing computational resource usage by avoiding unnecessary processing
Solution Approach 2:
The system performs preliminary analysis during the crawling phase, extracting and storing key features of web pages in advance. This pre-processing reduces the computational burden during user interactions, as the heavy lifting of content analysis has already been completed, balancing accuracy with resource efficiency
Data Source
AI summary
A computer-implemented method for detecting scam campaigns is described. A plurality of web pages that are pre-filtered according to predetermined criteria is identified. Pattern detection is performed on the pre-filtered web pages. A pattern is detected among the pre-filtered web pages. The detected pattern is compared to a user input.