Webpage Analysis System for Automated Content Injection Detection
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Solution Overview
Problem
Current methods for detecting content injection in webpages, such as those caused by 'Man in the Middle' or 'Man in the Browser' attacks, rely heavily on manual expert investigation and require ongoing maintenance with 'known good' and 'known bad' signature lists, which are inefficient due to constant attacker method changes.
Innovation Solution
A system and method that automatically classifies webpages by generating a baseline pool of representations, using unique identification parameters, and analyzing webpage elements to determine authenticity, reducing dependency on expert maintenance and using self-learning methods to detect malicious injections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual expert investigation is used to determine page modifications, then detection accuracy is improved, but productivity deteriorates due to ongoing maintenance requirements
Solution Approach 1:
The system performs self-learning by automatically analyzing webpage representations, comparing them against baseline pools, and updating classification models without requiring manual expert intervention. The analysis server autonomously maintains the baseline pool and detects content injections through machine learning algorithms, eliminating the need for experts to continuously update signature lists.
Solution Approach 2:
The patent replaces the mechanical process of manual expert investigation with an automated computational system. Instead of experts manually analyzing pages and updating signatures, the system uses algorithmic comparison of webpage representations against baseline pools, substituting human cognitive work with automated data processing and pattern recognition.
2Reliability
If manual expert investigation is used to build signature lists, then detection reliability is improved, but loss of time worsens due to constant updates required
Solution Approach 1:
The system operates continuously by automatically analyzing incoming webpage representations and comparing them against the baseline pool in real-time. The analysis server continuously updates classifications and maintains the baseline pool without interruption, eliminating the periodic downtime associated with manual signature list updates while maintaining constant detection capability.
Solution Approach 2:
The system performs preliminary analysis by pre-generating baseline pools from authentic webpage representations before actual detection occurs. These baseline pools are prepared in advance and stored for rapid comparison during live operation, enabling immediate detection without requiring real-time expert analysis or signature updates.
3Productivity
If automated classification is implemented, then productivity is improved, but device complexity worsens due to baseline pool generation requirements
Solution Approach 1:
The system divides the detection task into distinct functional modules: a baseline pool generation component that creates reference representations from authentic pages, an analysis server that performs classification by comparing incoming pages against the baseline, and a client component that collects and transmits webpage data. This segmentation allows each module to specialize in a specific function, improving overall productivity while managing complexity through modular architecture.
Data Source
AI summary
A system and method for classifying a webpage may include generating, by an analysis server, a first representation of a webpage. A system and method may include generating, by a unit installed in a user web browser, a second representation of the webpage and the method may comprise producing a classification of the webpage by relating the first representation to the second representation.