Phishing Detection via LSTM URL Pattern Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional phishing detection systems face challenges in identifying and blocking phishing URLs in real-time, as they often rely on blacklists that are not updated quickly enough, and webpage content analysis can be time-consuming, allowing phishing attacks to go undetected until after they have completed.
Innovation Solution
The use of a machine learning process, specifically a long short-term memory (LSTM) network-based phishing system that generates synthetic phishing URLs and adjusts detection systems to recognize patterns that allow phishing URLs to bypass traditional detection, enhancing both phishing attacks and detection capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional phishing detection systems use blacklists and webpage content analysis, then they can identify phishing URLs, but the detection process is too slow and allows phishing attacks to complete before detection
Solution Approach 1:
The patent extracts and analyzes only the URL string itself without requiring full webpage content analysis. By focusing detection on URL patterns, characteristics, and components rather than complete webpage rendering and analysis, the system achieves fast detection while maintaining accuracy in identifying phishing attempts
Solution Approach 2:
The system performs preliminary analysis of URL patterns and characteristics before phishing attacks can complete. By pre-establishing detection rules and analyzing URL structures in advance, the system can quickly identify and block phishing attempts before they cause harm, rather than waiting for attack completion
2Reliability
If phishing detection systems analyze webpage content in real-time, then they can detect phishing attempts, but the evaluation time is too long allowing attacks to go undetected
Solution Approach 1:
The patent extracts only the essential URL components for analysis rather than evaluating complete webpage content. This extraction approach maintains detection reliability by focusing on critical URL patterns while dramatically improving evaluation speed by avoiding full webpage rendering and content analysis
Solution Approach 2:
The system segments the phishing detection process into distinct phases: URL pattern analysis, characteristic identification, and threat assessment. This segmentation allows parallel processing of different URL components, improving overall evaluation speed while maintaining comprehensive detection reliability through multi-faceted analysis
Data Source
AI summary
Phishing enhancement and phishing detection enhancement technologies. The technologies can include determinations of an effectiveness rate of one or more phishing threat actors. The technologies can also include selection of effective URLs from at least one effective phishing threat actor. The technologies can also include generation or adjustment of a phishing system using a machine learning process to identify patterns in the selected effective URLs that enable the selected effective URLs to avoid detection by the phishing detection system. The technologies can also include generation of synthetic phishing URLs using the phishing system and the identified patterns. The technologies can also include adjustments or training of the phishing system or the phishing detection system according to the synthetic phishing URLs to enhance the systems.