Malicious Message Classification Using ML Models
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Solution Overview
Problem
Conventional malicious message identification tools are ineffective due to their reliance on analyzing only the message content, which can be easily circumvented by sophisticated nefarious actors, and manual human expert services are inefficient and often delayed, failing to promptly alert users to potential threats.
Innovation Solution
A classifier model is trained using a supervised approach with a dataset containing message categorization, user transaction records, and message attributes to determine whether incoming messages are malicious, leveraging user-specific knowledge to improve identification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional tools use rules based on message content analysis, then the system is simple to implement, but the identification accuracy deteriorates as nefarious actors circumvent rules
Solution Approach 1:
The patent replaces rule-based mechanical filtering with a machine learning classifier model that processes message content, user profile data, and transaction history. This substitution enables the system to learn complex patterns and adapt to evolving malicious messaging techniques, thereby improving identification accuracy while maintaining implementation feasibility through automated training and deployment processes.
2Reliability
If manual human expert services are used, then the system can provide thorough analysis, but the response time deteriorates causing users to be defrauded due to delays
Solution Approach 1:
The system performs self-service by automatically analyzing incoming messages against trained classifier models that incorporate user-specific profiles and transaction history. This automated self-analysis eliminates the need for manual expert intervention, providing immediate response times while maintaining thorough analysis through comprehensive feature extraction and pattern recognition algorithms.
Solution Approach 2:
The system performs preliminary action by pre-training classifier models on extensive datasets and pre-loading user profiles and transaction history into the analysis system. This preparation enables the system to immediately and accurately classify new incoming messages without requiring real-time human expert analysis, thus reducing response time while maintaining high analysis quality.
3Speed
If the system analyzes only incoming message content, then the processing is fast, but the identification accuracy deteriorates because sophisticated messages can evade detection
Solution Approach 1:
The patent merges multiple data sources including incoming message content, user profile information, and transaction history into a unified classification approach. The classifier model processes all these combined features simultaneously, enabling the system to maintain fast processing speeds while significantly improving identification accuracy by cross-referencing multiple data dimensions to detect sophisticated malicious messages that would evade single-source analysis.
Data Source
AI summary
Knowledge about a user is used to determine whether one or more messages received by the user are malicious. The knowledge about the user may be based on the user's financial history such as transaction records. Particularly, a classifier model is trained on a supervised approach using a dataset containing, for example, a categorization of incoming messages (e.g., password change message), the user's aggregated transaction records, message attributes, user attributes, and corresponding classification labels. After the training, the classifier model is deployed to determine whether an incoming message is malicious.