URL Risk Analysis Using Heuristics for Phishing Detection
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Solution Overview
Problem
Existing methods are inadequate for quickly, correctly, and efficiently identifying the risk of new, uncategorized Uniform Resource Locators (URLs) and detecting phishing sites that impersonate legitimate brands, leading to poor user experience or ineffective security.
Innovation Solution
A URL risk analyzer uses heuristics and scanning to classify URLs as benign, suspicious, or malicious based on extracted artifacts, and employs a Machine Learning model trained with Term Frequency–Inverse Document Frequency (TFIDF) and Logistic Regression to detect phishing URLs by analyzing code, metadata, and brand association.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all new, uncategorized sites are blocked, then security is improved, but user experience deteriorates due to blocking legitimate sites
Solution Approach 1:
The system changes the parameter of URL analysis from binary (block/allow) to a three-category classification (phishing/legitimate/uncategorized). This parameter change enables nuanced decision-making where only phishing sites are blocked while legitimate and uncategorized sites are allowed, resolving the contradiction between security and user experience.
Solution Approach 2:
The system segments the URL classification into distinct categories (phishing, legitimate, uncategorized) based on analysis results. This segmentation allows differentiated handling of different URL types, blocking only malicious ones while permitting others, thus maintaining both security and user experience.
2Measurement precision
If scanning and detailed analysis of new, uncategorized sites is performed, then detection accuracy is improved, but latency increases leading to poor user experience
Solution Approach 1:
The system performs preliminary action by pre-computing and storing brand associations and URL categorizations in databases before they are needed for analysis. When a URL needs to be analyzed, the system quickly retrieves pre-computed information rather than performing full analysis, thereby maintaining high detection accuracy while minimizing latency.
Solution Approach 2:
The system creates simplified copies of URL analysis results by storing categorization outcomes and brand associations in databases. These copies enable rapid lookup and decision-making without repeating the full analysis process, reducing latency while preserving detection accuracy.
3Ease of operation
If phishing protection is completely disabled, then user experience is improved, but security effectiveness deteriorates
Solution Approach 1:
The system implements self-service by providing users with informative warnings and categorization results that enable them to make informed decisions about whether to proceed to potentially risky sites. Instead of forcing complete blocking or complete disabling, the system serves users with information and lets them decide, balancing security and user experience.
Data Source
AI summary
Systems and methods include, responsive to starting a plurality of listener modules, receiving a Uniform Resource Locator (URL) for a site on the Internet into a database; loading the URL; receiving artifacts based on the loading; using the plurality of listener modules to run rules based on the received artifacts; scoring the URL based on the rules and the received artifacts; and determining whether the URL is one of benign, suspicious, or malicious based on the scoring. The steps can include any of blocking the URL, allowing the URL, further analyzing the URL, adding the URL to a whitelist or blacklist, and providing a notification, based on whether the URL is benign, suspicious, or malicious.