Website Artifact Signatures for New Malicious Site Detection
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Solution Overview
Problem
Malicious websites often mimic legitimate sites, making it difficult for consumers to distinguish between safe and fraudulent websites, leading to potential financial loss, identity theft, and legal issues, with conventional solutions failing to prevent damage from newly created scams.
Innovation Solution
A method using repetitive data signatures is employed to identify malicious websites by creating datasets from known safe and malicious websites, identifying common artifacts, determining signature frequencies, and establishing a confidence level to flag new malicious sites, with security actions to protect users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional website safety detection methods are used, then existing safe websites can be identified, but newly created malicious websites that mimic legitimate sites cannot be detected
Solution Approach 1:
The system performs preliminary actions by creating datasets of known safe and malicious websites before new threats emerge. It identifies and stores artifacts (HTML templates, CSS styles, JavaScript code, images, logos) from previously detected malicious sites, establishing a reference library that enables rapid detection of new malicious websites that mimic legitimate ones.
Solution Approach 2:
The system copies and analyzes artifacts from known malicious websites to create signature patterns. By replicating and storing these malicious patterns in a database, the system can recognize and detect new malicious sites that use similar or identical artifacts, even when they present themselves as legitimate websites.
2Measurement precision
If website content is analyzed to determine legitimacy, then consumers can make informed decisions, but the complexity of analyzing multiple artifacts increases processing time
Solution Approach 1:
The system segments website analysis into distinct artifact categories (HTML templates, CSS styles, JavaScript code, images, logos, contact information). Each artifact type is analyzed separately and compared against corresponding signatures in the database, allowing parallel processing and reducing overall analysis time while maintaining comprehensive coverage.
Solution Approach 2:
The system changes the analysis parameter from examining individual website content to counting the frequency and variety of artifacts. By measuring how many different artifacts a website uses and comparing this against established thresholds, the system achieves fast, accurate detection without analyzing every detail of each artifact.
3Reliability
If multiple artifacts are collected from websites, then detection accuracy improves, but the volume of data to process increases computational requirements
Solution Approach 1:
The system extracts only the most significant and distinguishing artifacts from websites - specifically HTML templates, CSS styles, JavaScript code, images, logos, and contact information. By focusing on these key artifact types that are most commonly reused across malicious sites, the system reduces the overall data volume while maintaining high detection accuracy.
Solution Approach 2:
The system creates a universal artifact database that serves multiple detection functions simultaneously. The same collected artifacts are used to create signatures for detecting various types of malicious websites (phishing, malware distribution, fraud), making the system highly versatile without requiring separate analysis systems for different threat types.
Data Source
AI summary
Methods for protecting against malicious websites using repetitive data signatures are disclosed. Some embodiments may identify known malicious websites and known safe websites. A first dataset containing data from one or more artifacts within the known malicious websites and a second dataset containing data from the one or more artifacts within the known safe websites may be created. One or more signatures may be identified from the first dataset. A first frequency of signature repetition within the first dataset and a second frequency of signature repetition within the second dataset may be determined. A level of confidence may be determined based on the frequencies. If a rule establishment threshold for confidence is met or exceeded, a rule may be established that websites containing the one or more signatures are malicious. The rule may be applied to identify a new malicious website. A security action may also be performed.