Web Decoder Learning Engine for Main Page Identification
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Solution Overview
Problem
Current network communication analysis tools face challenges in accurately reconstructing web sessions, particularly in complex cases where embedded elements are difficult to identify and distinguish from main pages, leading to information overload and false positives/negatives.
Innovation Solution
A system utilizing a learning engine with artificial intelligence, such as a decision tree or neural network, to process communication packets, identify data elements, and adjust its configuration based on human feedback to differentiate between important and unimportant elements, matching URLs and determining handling rules for embedded elements, even if they are not identical.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all identified data elements are displayed to the operator, then completeness of information is improved, but information overload and difficulty in identifying important elements worsens
Solution Approach 1:
The system displays data elements with confidence indicators and allows operators to provide feedback on classification accuracy. This feedback is used to continuously improve the classification algorithm, resolving the contradiction by enabling selective display based on improving detection accuracy while maintaining information completeness.
Solution Approach 2:
The system applies different display treatments to different data elements based on their classification confidence and importance. High-confidence main pages are displayed prominently, while low-confidence or embedded elements are displayed with different visual characteristics, allowing operators to quickly identify important elements without information overload.
2Measurement precision
If classification confidence threshold is increased to reduce false positives, then accuracy of main page identification is improved, but number of false negatives increases
Solution Approach 1:
The classification confidence threshold is not fixed but dynamically adjusted based on operating conditions, data characteristics, and operator feedback. The system can adapt thresholds to balance false positives and false negatives, resolving the contradiction by making the classification criterion flexible rather than static.
Solution Approach 2:
The system changes classification parameters including confidence thresholds and displays multiple classification results with different confidence levels. Operators can review elements that fall into gray areas, allowing the system to maintain high accuracy while minimizing false negatives through parameter adjustment.
3Measurement precision
If manual review of all data elements is performed to ensure accurate classification, then classification accuracy is improved, but processing time and operational complexity worsens
Solution Approach 1:
Instead of manually reviewing all data elements, the system performs partial manual review only on elements with intermediate confidence levels or ambiguous classifications. High-confidence classifications are accepted automatically, while uncertain cases receive operator attention, resolving the contradiction by applying manual review selectively rather than universally.
Solution Approach 2:
The classification system serves itself by using operator feedback on classified elements to automatically improve future classifications. This self-learning mechanism reduces the need for continuous manual review while maintaining or improving accuracy over time, resolving the contradiction between accuracy and processing time.
Data Source
AI summary
Web pages may be rendered from a main page data element and a plurality of embedded data elements, which are separately fetched by a browser. Herein is provided a web decoder which includes a learning engine adapted to receive human indications of data elements which are unimportant and accordingly to adjust the web decoder's procedures for determining which data elements are displayed to the user. The learning engine may receive human indications of important data elements and uses both types of indications in its further determinations. Optionally, rule generalizations are performed in a manner which searches for parameters which differentiate between important and unimportant data elements. The rule generalizations optionally concentrate on groups of data elements having at least a predetermined number of parameters having the same values for both important and unimportant data elements, reducing the chances that a generalization rule will find important data elements as unimportant.