Email Classification Using Human Relationship Structures
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Solution Overview
Problem
Conventional email systems fail to effectively detect deceptive communication, such as spear phishing attacks, which pose a significant threat by disguising hostile requests for personal and organizational data, leading to identity theft and financial loss, due to their inability to accurately classify incoming emails from known parties.
Innovation Solution
The development of deep learning algorithms trained on human relationship-based communication classifiers to detect anomalies by correlating indications of association between senders and receivers with the content of electronic communications, using neural networks to identify deception and trigger defensive actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional email systems use traditional spam filtering methods, then they can identify obvious spam emails, but they fail to detect sophisticated deceptive communications such as spear phishing attacks from known parties
Solution Approach 1:
The patent introduces a new dimension for email analysis by incorporating human relationship structures and association models. Instead of relying solely on content-based filtering, the system analyzes the relationships between senders and receivers, their communication patterns, and contextual associations to detect deceptive communications. This multi-dimensional approach enables the system to identify spear phishing attacks that traditional single-dimension filters miss.
Solution Approach 2:
The patent employs neural networks as intermediary components that bridge the gap between raw email data and classification decisions. These neural networks process and interpret complex relationship patterns, communication behaviors, and contextual information, transforming them into actionable classification signals. The intermediary neural networks enable the system to detect subtle deceptive patterns that direct rule-based filtering cannot identify.
2Reliability
If email systems implement comprehensive deception detection algorithms, then they can identify phishing attempts, but they increase system complexity and processing requirements
Solution Approach 1:
The patent segments the deception detection system into distinct functional modules: association model construction, communication pattern analysis, neural network processing, and classification decision-making. Each module handles a specific aspect of the analysis, making the overall complex system more manageable and maintainable. The segmentation allows for independent optimization and debugging of each component while maintaining the integrity of the whole system.
Solution Approach 2:
Neural networks serve as intermediary processing layers that simplify the complexity by automatically learning and extracting relevant features from raw data. Instead of requiring explicit programming of complex detection rules, the neural networks intermediate between data input and decision output, automatically adapting to detect deception patterns. This intermediary approach reduces the burden of system configuration and maintenance.
3Productivity
If conventional systems classify emails based on content alone, then processing is fast, but they cannot accurately identify emails that appear to be from known parties but are actually deceptive
Solution Approach 1:
The patent performs preliminary analysis by pre-building association models that capture human relationship structures, communication patterns, and contextual associations before email classification is needed. These pre-computed models enable faster real-time processing while maintaining high accuracy, as the system doesn't need to analyze all relationship data from scratch for each email. The preliminary preparation of relationship contexts accelerates the classification process without sacrificing verification accuracy.
Solution Approach 2:
The patent applies different analysis depths and methods to different aspects of email processing. For high-risk emails or those involving critical relationships, the system performs more comprehensive analysis. For routine communications with established patterns, it uses streamlined processing. This localized quality adjustment optimizes the balance between processing speed and verification accuracy based on the specific context and risk level of each email.
Data Source
AI summary
Systems and methods for a computer system for detecting anomalies in incoming communication from a sender to a receiver. Accepting a relationship structure defining a trained association model between the sender and the receiver, and the incoming communication. Accessing neural networks trained to detect anomalies in the incoming communication and classify the anomalies by type, subject to correspondence between content of the incoming communication and the trained association model between the sender and the receiver. Compute an updated association model, based on sender and the receivers organizational indications using the content of the incoming communication. Execute the neural networks by submitting the incoming communication and the updated association model to produce a result of anomaly detection and anomaly classification type. Execute a single sequence of defensive actions to deliver the incoming communication to the receiver, when the single sequence of defensive actions is above a predetermined delivering threshold.