Message Classification via Clustering and Hashing
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Solution Overview
Problem
Existing spam filters struggle to recognize and filter unusual spam campaigns that use randomized or varying content, sender credentials, and IP addresses, leading to compromised user security and inbox clutter, as they rely on learning from sufficient spam votes after harm has been done.
Innovation Solution
A message classification system that transforms messages into bags of words, uses hash descriptions, and clusters them based on features like subject lines, content, and user actions, creating tables to identify message features and classify messages as spam or not, using a machine learning classifier to differentiate between spam and other types of messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional spam filters are used that rely on learning from spam votes, then they can recognize known spam campaigns, but they cannot detect unusual spam attacks until after harm has been done
Solution Approach 1:
The system performs preliminary clustering and analysis of message features before spam votes are accumulated. By pre-processing messages into bags of words, computing hash descriptions, and organizing them into clusters with computed features, the system is ready to rapidly classify new spam campaigns as they emerge, eliminating the time delay inherent in traditional vote-based learning systems.
Solution Approach 2:
The patent replaces the mechanical vote-counting mechanism with a computational clustering system. Instead of waiting for human users to vote on messages, the system automatically computes message features, clusters similar messages, and identifies spam campaigns through algorithmic analysis of cluster characteristics, enabling faster and more automated detection.
2Measurement precision
If spam filters wait for sufficient spam votes to learn a bad reputation, then classification accuracy improves, but user security is compromised during the learning period
Solution Approach 1:
The system computes all necessary message features, clusters, and cluster features in advance, before any spam classification decision is required. This preliminary processing creates a ready-to-use feature set that can be immediately applied to new messages, eliminating the need to wait for spam votes and allowing immediate security protection from the outset.
Solution Approach 2:
The system performs self-service by automatically analyzing message content, computing features, and identifying spam patterns without requiring human user input or spam votes. The automated feature computation and clustering enable the system to protect users independently, removing the security vulnerability period that exists when traditional systems wait for user feedback.
3Reliability
If the system clusters all messages into multiple clusters with multiple features, then spam detection capability improves, but computational complexity increases
Solution Approach 1:
The patent segments the message analysis process into distinct components: transforming messages into bags of words, computing hash descriptions, organizing into clusters, and computing cluster features. This segmentation allows each component to be processed independently and efficiently, managing computational complexity through modular organization while maintaining comprehensive spam detection capability.
Solution Approach 2:
The system transforms messages through multiple parameter changes: from raw text to bags of words, from bags of words to hash descriptions, and from individual messages to cluster-level features. These parameter transformations simplify the data at each stage, reducing computational complexity while preserving the information necessary for reliable spam detection.
Data Source
AI summary
One or more computing devices, systems, and/or methods for message classification are provided. For example, a set of messages is clustered into a set of clusters. A cluster comprises messages with similar features (e.g., similar subject lines, message body content, sender information, recipient information, structure, user action such as reading or deleting, spam vote information, etc.). Cluster features are computed for the clusters based upon features of messages within such clusters. A first table, comprising cluster entries corresponding cluster features of clusters, and a second table, comprising message entries corresponding to clusters to which messages are assigned, are created. Message features of a message are created, using the first table and second table, based upon features of the message and cluster features of clusters to which the message is assigned. A message classifier is used to classify the message (e.g., spam, safe, a threat, etc.) based upon the message features.