Message Abuse Detector Component for Spam Classification
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Solution Overview
Problem
Mobile messaging systems lack effective solutions to classify and manage abusive messages, leading to increased costs and inconvenience for users and service providers due to spam and malicious software-induced messaging issues, with conventional spam defenses being inadequate for the mobile environment.
Innovation Solution
A system and method employing a Message Abuse Detector Component (MADC) that analyzes historical data, abuse reports, and message content using hash algorithms to classify mobile messages as spam, subscription-related, or harassing, and automatically executes appropriate actions such as blocking or crediting users, while utilizing ratios and reputation evaluations to distinguish between types of abusive messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If users have limited messaging plans to control costs, then billing costs are reduced, but users incur additional fees for unwanted spam messages
Solution Approach 1:
The system performs preliminary classification of messages using multiple ratios (abuse-report-hash ratio, abuse-report-sender ratio, unique ID to overall-mobile-message ratio) before messages reach the user. This allows the network to identify and block spam messages proactively, preventing users from being charged for unwanted messages while maintaining legitimate messaging services.
2Measurement precision
If service providers manually review and process spam complaints, then message classification accuracy is improved, but service provider costs and time consumption increase
Solution Approach 1:
The system enables automated self-service classification by having the network itself analyze messages using multiple ratios and make classification decisions without requiring manual intervention from service providers. The Message Abuse Detector Component automatically processes complaints, evaluates ratios, and executes appropriate actions, freeing service providers from manual review while maintaining high classification accuracy.
Solution Approach 2:
The system uses feedback from abuse reports and honeypot data to continuously refine classification accuracy. By analyzing patterns from reported abusive messages and adjusting the ratios and thresholds accordingly, the system improves its ability to distinguish between spam and legitimate messages over time without requiring manual retraining of service providers.
3Device complexity
If conventional email spam defenses are applied to mobile messaging, then implementation simplicity is maintained, but spam detection effectiveness deteriorates
Solution Approach 1:
The system adapts spam detection parameters specifically for mobile messaging by developing mobile-appropriate ratios such as the abuse-report-hash ratio (comparing hash values of message content), abuse-report-sender ratio (comparing abuse reports to total messages from a sender), and unique ID to overall-mobile-message ratio. These parameter changes reflect the unique characteristics of mobile messaging traffic patterns and user behavior, significantly improving detection effectiveness while remaining implementable through standard network processing.
Data Source
AI summary
Systems, methods, and devices that classify mobile messages and associated mobile message senders are presented. A communication network can comprise a message abuse detector component (MADC) that evaluates a mobile message, for example, labeled as abusive in an abuse report. The MADC can evaluate information relating to the abuse report, wherein the evaluations can include an abuse-report-hash-ratio, an abuse-report-sender-ratio, correlation of mobile message content to known spam content or non-spam content, and/or evaluation of reputation of the mobile message sender or abuse report sender, etc. Based at least in part on the evaluation, the MADC can classify mobile message as spam, subscription related, or as another type of mobile message, and can identify, select, and execute (e.g., automatically) a desired abuse management action(s) based at least in part on the evaluation and predefined message abuse criteria.