Phishing URL Detection via SVM Probability Engine
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Solution Overview
Problem
Phishing URLs, designed to mimic legitimate websites, pose a significant threat as they can deceive even sophisticated users by resembling genuine websites in appearance and content, making it difficult to distinguish between legitimate and fraudulent URLs.
Innovation Solution
A method involving real-time analysis and exploration of URLs, utilizing databases and a phishing probability engine, which includes checking against known phishing URLs, domains, and legitimate URLs, as well as analyzing webpage content with features such as keywords, domain names, and user experience elements to determine the likelihood of a URL being phishing, employing a Support Vector Machine (SVM) classifier for pattern recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If phishing URLs are designed to closely resemble legitimate websites in appearance and content, then the deception effectiveness is improved, but the difficulty of detection increases
Solution Approach 1:
The system performs preliminary analysis of URLs and webpages before users interact with them. By pre-computing features such as URL structure, domain reputation, and webpage content characteristics, the system prepares detection data in advance, enabling rapid classification when a URL is encountered without requiring real-time deep analysis.
Solution Approach 2:
The patent introduces an intermediary classification system that acts as a mediator between the phishing URL and the user. This intermediary analyzes various features (URL patterns, domain information, webpage content) and provides a probability rating that indicates the likelihood of phishing, thereby facilitating detection without requiring direct human expertise in identifying subtle phishing indicators.
2Measurement precision
If comprehensive analysis of URL features and webpage content is performed, then the detection accuracy is improved, but the processing time increases
Solution Approach 1:
The detection process is segmented into multiple independent feature extraction components. Each component analyzes a specific aspect of the URL or webpage (e.g., URL structure, domain reputation, text content, HTML elements) separately. This segmentation allows parallel processing of features and enables the system to focus computational resources on the most discriminative features, improving both accuracy and efficiency.
Solution Approach 2:
The system dynamically adjusts analysis parameters based on the input characteristics. For example, if a URL shows high-risk patterns in preliminary checks, the system intensifies the analysis depth and activates more comprehensive feature extraction. Conversely, for low-risk URLs, the analysis is streamlined, reducing processing time while maintaining sufficient detection accuracy.
Data Source
AI summary
A method of determining a probability that a received email comprises a phishing attempt may comprise analyzing a link therein to determine whether the link comprises a phishing attempt. This determination may comprise comparing features of the link with records stored in a remote database to determine whether the link comprises a phishing attempt. It may be determined that the link comprises a phishing attempt if there is a match. If the compared features do not match the records stored in the remote database, a multi-dimensional input vector may be built from features of the link, which input vector may then be input into a phishing probability engine. The probability that the link comprises a phishing attempt may be computed by the phishing probability engine. Thereafter, the received email may be acted upon according to the computed probability that the link comprises a phishing attempt.