SSL Certificate Classification for Low-Latency Malicious Website Detection
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Solution Overview
Problem
Existing tools for detecting malicious websites are processing-intensive, require remote server analysis, consume large bandwidth, and suffer from latency and potential tampering, especially when only partial URLs or SSL certificates are available.
Innovation Solution
A machine learning model trained on SSL certificates of known legitimate and malicious websites is deployed on a computing device to process partial URLs, determining probabilities of malicious intent using character, word, structural, and entropy analysis, and integrating SSL certificate features to classify websites.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If remote server analysis is used for website detection, then detection capability is provided, but bandwidth consumption increases and latency increases
Solution Approach 1:
The patent extracts only the essential SSL certificate data from the website connection, omitting the need to transmit full URL pages or other data to remote servers. By taking out only the critical certificate information for local analysis, the system reduces bandwidth consumption while maintaining detection capability.
Solution Approach 2:
The patent performs preliminary action by pre-processing and analyzing SSL certificates locally on the device before any potential malicious activity occurs. The machine learning model is trained in advance to recognize malicious patterns, enabling immediate local detection without requiring continuous remote server communication, thus reducing ongoing bandwidth usage.
2Reliability
If remote server analysis is used for website detection, then detection capability is provided, but processing time increases
Solution Approach 1:
The patent extracts the SSL certificate as the key data element for analysis, removing the need to wait for full page loads or extensive URL data transmission to remote servers. By focusing only on the certificate which contains sufficient malicious website indicators, the system achieves rapid local analysis and reduces latency.
Solution Approach 2:
The patent implements self-service by enabling the computing device to perform autonomous detection using its own built-in machine learning model and SSL certificate parsing capabilities. The device serves its own security needs without relying on external servers, eliminating network-dependent processing delays and achieving immediate local response.
3Measurement precision
If full URL analysis is required for detection, then detection accuracy is improved, but processing complexity increases and bandwidth consumption increases
Solution Approach 1:
The patent extracts only the SSL certificate portion of the website data, which contains sufficient information for accurate malicious website detection. By taking out only this critical element rather than analyzing complete URLs and pages, the system maintains detection accuracy while significantly reducing processing complexity and data requirements.
Solution Approach 2:
The patent changes the analysis parameter from full URL string analysis to SSL certificate feature analysis. By transforming the detection focus to certificate properties (such as domain name, issuer, validity period, and cryptographic information), the system achieves accurate detection with simpler processing requirements and lower bandwidth consumption.
4Reliability
If remote server-based classification is used, then detection is provided, but the system becomes vulnerable to attacker tampering
Solution Approach 1:
The patent implements self-service by moving the detection intelligence to the local device, where it operates autonomously without external server dependency. The machine learning model runs locally on the computing device, using its own trained weights and decision logic, making the system immune to server-side tampering or blocking by attackers while maintaining detection capability.
Solution Approach 2:
The patent segments the detection function from the network infrastructure, isolating the classification logic within the local device's computing resources. By segmenting the intelligence away from remote servers, the system creates an independent detection layer that cannot be compromised by network-based attacks or server tampering.
Data Source
AI summary
A system and a method are disclosed for detecting a malicious website. In an embodiment, a computing device detects a uniform resource locator (URL) referencing an unknown website; pre-processes the URL to determine a first probability that the unknown website is malicious; and inputs the first probability into a machine learning model to receive a second probability that the unknown website is malicious. The machine learning model is trained using Secure Sockets Layer (SSL) certificates of known legitimate websites and known malicious websites. The computing device further determines whether the second probability is associated with at least a threshold risk. Responsive to the second probability being associated with at least the threshold risk, the computing device causes a graphical user interface of a client device to display a notification indicating a level of risk associated with the unknown website.