Message Content Redaction for Malicious URL Detection
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Solution Overview
Problem
Existing wireless communication systems face challenges in ensuring the security and safety of URLs and other activatable content in messages, particularly against malicious content that can compromise user devices and networks.
Innovation Solution
A content analysis system intercepts and analyzes messages for activatable content, generating cryptographic hash digests and comparing them against a data store to identify potential malicious URLs, and modifies or redacts them to prevent activation, using machine learning and external databases for verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a content analysis system intercepts and analyzes messages for activatable content, then message content security is improved, but system complexity increases
Solution Approach 1:
A content analysis system is introduced as an intermediary component between message sources and user devices. This system intercepts messages, analyzes activatable content (URLs, etc.), determines security status, and modifies or forwards messages accordingly. The intermediary approach centralizes security analysis functionality, allowing user devices to remain simpler while maintaining enhanced security through network-level or gateway-level processing.
2Measurement precision
If cryptographic hash digests are generated and compared against data stores, then detection precision of malicious URLs is improved, but processing time increases
Solution Approach 1:
Cryptographic hash digests of known malicious URLs are pre-computed and stored in a data store before actual message analysis occurs. When a message is received, the system only needs to compute the hash of the suspected URL and compare it against the pre-computed hashes, rather than performing complex analysis from scratch. This preliminary preparation enables fast, precise detection while minimizing real-time processing requirements.
3Reliability
If machine learning and external databases are used for verification, then security accuracy is improved, but device complexity increases
Solution Approach 1:
External databases and machine learning models are integrated into the content analysis system as intermediary services. Rather than requiring each user device to host these complex verification systems, the centralized analysis system queries external databases and applies ML algorithms to determine URL safety. This approach improves security accuracy through sophisticated verification while keeping individual user devices simpler.
Data Source
AI summary
Systems and methods for redacting malicious URLs and other activatable content from messages are disclosed. A content analysis system may text messages and multimedia messages to determine whether a URL is contained in such messages. If so, the content analysis system may determine the maliciousness of the URL using various techniques, redacting the URL from the message before providing it to the user device if the URL is determined to be malicious. The original message may be stored for user access if desired.