Cookie Metadata Phishing Detection for Design-Around Evasion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing phishing detection systems are vulnerable to design arounds by phishing site designers, necessitating improved methods to identify and block fraudulent websites effectively.
Innovation Solution
A phishing detection system that analyzes webpage data, including visual features, cookie metadata, and URL information using machine learning models to predict fraudulent content and generate notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional phishing detection approaches are used, then phishing sites can be identified and blocked, but phishing site designers can create design arounds to avoid detection
Solution Approach 1:
The patent transitions from traditional 2D visual inspection of webpages to 3D spatial analysis by extracting cookie metadata that contains spatial information about webpage elements. This dimensional shift enables detection of phishing sites through their spatial fingerprints rather than just visual appearance, making it harder for attackers to evade detection through design arounds.
Solution Approach 2:
The patent introduces cookie metadata as an intermediary between the webpage visual structure and the detection system. This intermediary captures spatial relationships and structural information that directly reveals phishing characteristics, serving as a bridge that translates complex webpage designs into detectable patterns without requiring direct visual analysis of the phishing site itself.
2Measurement precision
If visual analysis of webpages is performed to detect phishing, then phishing content can be identified, but subtle differences between legitimate and fraudulent webpages remain difficult to detect
Solution Approach 1:
The patent extracts specific spatial metadata from cookie data that captures the precise positions, sizes, and relationships of webpage elements. This extraction isolates the critical spatial fingerprints that differentiate legitimate from fraudulent sites, separating the noise of visual complexity from the signal of phishing characteristics to enable precise detection of subtle differences.
Solution Approach 2:
The patent changes the detection parameters from traditional visual features (colors, layouts, text content) to spatial metadata parameters (positions, dimensions, spatial relationships). This parameter transformation enables precise measurement of subtle structural differences that are invisible to human observers but clearly distinguishable through quantitative spatial analysis.
Data Source
AI summary
In one embodiment, a method for detecting phishing activity by a webpage is provided. The method includes: receiving, by a processor, webpage data associated with the webpage; analyzing, by the processor, the webpage data to determine if at least one of a brand logo and credential entry box is present; in response to a determination that the brand logo is present or the credential entry box is present: extracting, by the processor, cookie feature data from the webpage data; determining, by the processor, cookie score data based on an analysis of the cookie feature data with a cookie model; predicting, by the processor, fraudulent content of the webpage based on the cookie score data and a prediction model; and generating, by the processor, notification data including an indication of the fraudulent content.