Webpage Phishing Detection via Image Similarity and IP Comparison
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Solution Overview
Problem
Existing phishing detection systems are ineffective against evolving threats, particularly due to the rise of phishing kits and zero-day attacks, with high false-positive rates and low classification accuracy, and traditional methods like blacklisting are becoming less effective.
Innovation Solution
A system utilizing machine learning, natural language processing, and behavioral analysis to detect phishing websites by comparing captured images of websites with legitimate databases, analyzing IP addresses, and digital certificates, and generating alarms for potential phishing threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional blacklisting and static rules are used for phishing detection, then implementation is simple, but detection accuracy decreases and false positives increase
Solution Approach 1:
The patent replaces traditional mechanical filtering methods (blacklisting, static rules) with machine learning-based semantic analysis. The system uses trained models to perform dynamic classification of URLs and webpage content, substituting rigid mechanical decision-making with adaptive intelligent analysis that can recognize phishing patterns without relying on pre-defined signatures.
Solution Approach 2:
The system changes the parameters of detection by moving from static attributes (known phishing URLs, fixed rules) to dynamic semantic features. It analyzes multiple parameters including URL semantics, webpage content, structural features, and behavioral patterns simultaneously, allowing the detection system to adapt to new phishing techniques without requiring manual rule updates.
2Adaptability or versatility
If sophisticated programmers create phishing websites manually, then detection is easier, but with phishing kits anyone can create phishing sites increasing the number of threats
Solution Approach 1:
The patent detects phishing sites by identifying copied structures and content from legitimate websites. The system analyzes semantic similarities, structural patterns, and content plagiarism to recognize when phishing sites replicate legitimate site designs. This approach effectively counters the proliferation of phishing sites created through phishing kits by detecting the copied nature of their content and structure.
3Measurement precision
If visual content analysis is used for phishing detection, then false positives decrease, but processing time increases
Solution Approach 1:
The system performs preliminary analysis of URL semantics and structural features before conducting full visual content analysis. This multi-stage approach allows the system to quickly filter out obvious legitimate sites using lightweight checks, and only apply resource-intensive visual analysis when necessary, thereby reducing overall processing time while maintaining high accuracy.
Data Source
AI summary
Embodiments relate to systems and method for phishing webpage detection, the system comprising a weblink retrieval unit configured to obtain a weblink of a website of interest, a website capturing unit configured to capture a first image of the website of interest, a detection unit configured to detect a similarity between the first image of the website of interest and a list of pre-configured legitimate websites corresponding to a label in a database, an IP address grabbing unit configured to grab an IP address of the website of interest if the similarity is detected, a comparison unit configured to compare the IP address of the website of interest with a database of legitimate IP addresses in at least one of a static context and a dynamic context to identify a difference and an alarm unit configured to generate a phishing website alarm based on a presence the difference.