Automated Phishing Detection via Multi-Module Threat Scoring
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Solution Overview
Problem
Current methods are inadequate in automatically identifying and distinguishing phishing, phony, and malicious websites, as they often require significant user expertise and may not effectively protect users from rogue sites that engage in identity theft, virus propagation, or display undesirable content.
Innovation Solution
A computer-implemented method and system that receives URL inputs, extracts data from webpages, and calculates a threat score using multiple software modules to assess safety, incorporating feedback and reputation information, thereby classifying websites as safe or hazardous and providing users with customizable safety reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated identification systems are implemented to detect phishing and malicious websites, then user protection and safety identification improve, but system complexity and computational requirements increase
Solution Approach 1:
The system segments website analysis into multiple independent modules: code-centric analysis module, content analysis module, spatio-temporal analysis module, reputation module, and graph-based analysis module. Each module focuses on specific aspects of website evaluation, making the overall complex system manageable and maintainable while achieving comprehensive security assessment
Solution Approach 2:
The system creates a universal website evaluation framework that handles multiple types of threats (phishing, malware, spam, fraudulent content) through a single integrated platform. The threat scoring mechanism and safety classification apply across diverse website types and threat scenarios, reducing the need for separate specialized tools
2Measurement precision
If comprehensive data extraction and analysis are performed on webpages to calculate threat scores, then measurement precision of website safety improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing baseline reputation scores, pre-compiling safety rules and threat indicators, and pre-establishing evaluation criteria before actual website assessment. This allows the system to quickly evaluate websites by comparing against pre-prepared standards rather than creating analysis frameworks from scratch
Solution Approach 2:
The system implements a multi-tiered analysis approach where not all websites receive full comprehensive analysis. Low-risk websites receive basic evaluation using fewer resources, while high-risk or suspicious websites trigger more intensive analysis. This partial action strategy maintains high detection precision for threats while reducing average processing time and computational overhead
3Difficulty of detecting and measuring
If multiple analysis modules are used to evaluate websites from different perspectives, then detection capability of malicious sites improves, but ease of operation decreases
Solution Approach 1:
The system merges results from multiple complex analysis modules (code analysis, content analysis, reputation checks, graph-based analysis) into a single unified threat score and safety classification. Users interact with one simple interface that displays overall website safety status rather than navigating multiple separate analysis tools and interpreting individual module results
Solution Approach 2:
The system introduces an intermediary threat scoring mechanism that translates complex multi-module analysis results into simplified safety classifications. This intermediary layer processes the outputs from various sophisticated detection modules and presents them as straightforward safety recommendations to users, bridging the gap between complex detection capabilities and simple user interaction
Data Source
AI summary
A method and system for automated identification of phishing, phony, and malicious web sites are disclosed. According to one embodiment, a computer implemented method, comprises receiving a first input, the first input including a universal resource locator (URL) for a webpage. A second input is received, the second input including feedback information related to the webpage, the feedback information including an indication designating the webpage as safe or unsafe. A third input is received from a database, the third input including reputation information related to the webpage. Data is extracted from the webpage. A safety status is determined for the webpage, including whether the webpage is hazardous by using a threat score for the webpage and the second input, wherein calculating the threat score includes analyzing the extracted data from the webpage. The safety status for the webpage is reported.