Multi-Stage URL Analysis System for Malicious Detection

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Solution Overview

Problem

Current URL analysis systems face challenges in identifying malicious URLs due to their dynamic and transient nature, leading to inefficiencies in maintaining blacklists, resource-intensive content analysis, and difficulty in scaling to emerging threats.

Innovation Solution

A multi-stage URL analysis system that employs ensemble lexical analysis, third-party malicious URL detection results, and URL metadata analysis, with adaptive learning techniques to enhance classification accuracy and reduce computational intensity.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If content analysis is performed in real-time to identify malicious URLs, then detection accuracy is improved, but time consumption and resource usage increase significantly

Engineering Contradiction:

Improvemalicious URL detection accuracyVSAvoidanalysis time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The analysis system is divided into multiple independent stages: URL lexical ensemble analysis, third-party detection results analysis, URL metadata analysis, and content analysis. Each stage processes specific features independently and produces classification scores, allowing the system to avoid full content analysis for all URLs while maintaining high detection accuracy through selective multi-stage processing.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary analysis using lightweight methods (lexical analysis, metadata analysis, third-party results) before proceeding to more resource-intensive content analysis. URLs are pre-filtered and classified in earlier stages, so only uncertain cases require full content analysis, significantly reducing overall time consumption while maintaining detection accuracy.

Inventive Principle:

Principle #10Preliminary action

2Adaptability or versatility

If traditional URL analysis methods are used, then implementation simplicity is maintained, but adaptability to emerging threats deteriorates

Engineering Contradiction:

Improveadaptability to new threatsVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system incorporates dynamic machine learning models that continuously adapt to new threats. The lexical ensemble analyzers, third-party detection analyzers, and metadata analyzers use trained models that can be updated with new threat patterns. This dynamic adaptation allows the system to respond to emerging threats without requiring complete system redesign, balancing adaptability with manageable complexity through modular architecture.

Inventive Principle:

Principle #15Dynamics

3Reliability

If blacklists and whitelists are maintained to identify known malicious URLs, then detection of known threats is improved, but maintenance time and completeness deteriorate due to the transient nature of malicious URLs

Engineering Contradiction:

Improvedetection of known threatsVSAvoidmaintenance time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system introduces third-party malicious URL detection results as an intermediary data source. Instead of maintaining internal blacklists that require manual updates, the system leverages externally maintained detection databases that are continuously updated by specialized services. This intermediary approach provides reliable detection of known threats while eliminating the maintenance burden, as the third-party services handle the ongoing updates and completions of threat databases.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Productivity

If human-crafted rules are used for non-content URL analysis, then implementation speed is improved, but scalability and agility to new threats deteriorate

Engineering Contradiction:

Improveanalysis speedVSAvoidagility to emerging threats

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The system replaces manual human-crafted rule systems with automated machine learning-based lexical ensemble analyzers. These analyzers use trained models that automatically learn threat patterns from data, eliminating the need for manual rule creation and updating. The substitution maintains high analysis speed through automated processing while dramatically improving adaptability to emerging threats, as the machine learning models can be retrained on new threat data without human intervention in rule formulation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS10218716B2Technologies for analyzing uniform resource locators

Publication Date: 2019.02.26 INTEL CORP

AI summary

Technologies for analyzing a Uniform Resource Locator (URL) include a multi-stage URL analysis system. The multi-stage URL analysis system analyzes the URL using a multi-stage analysis. In the first stage, the multi-stage URL analysis system analyzes the URL using an ensemble lexical analysis. In the second stage, the multi-stage URL analysis system analyzes the URL based on third-party detection results. In the third stage, the multi-stage URL analysis system analyzes the URL based on metadata related to the URL. The multi-stage URL analysis system advances the stages of analysis if a malicious classification score determined by each stage does not satisfy a confidence threshold. The URL may also be selected for additional rigorous analysis using selection criteria not used in by the analysis stages.