Email Pattern Clustering Using NLP Vectors for Threat Detection
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Solution Overview
Problem
Existing IT systems struggle to efficiently analyze large quantities of automatically collected emails to identify emerging trends, threats, or noteworthy activity due to the labor-intensive nature of clustering similar content, employed tactics, or contained threats, leading to inefficiencies in resource utilization and threat detection.
Innovation Solution
A pattern recognition management (PRM) system that processes and classifies emails using a natural language processing model to vectorize email content into multi-dimensional vectors, generates maliciousness scores, and clusters similar emails based on these vectors, enabling efficient identification of patterns and threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis methods are used to cluster similar emails, then analysis accuracy can be maintained, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated machine learning system that uses natural language processing models to vectorize email content and clustering algorithms to group similar emails. This substitution maintains analysis accuracy through sophisticated algorithms while eliminating the time-consuming manual labor of analysts reviewing and clustering emails individually.
Solution Approach 2:
The patent introduces an intermediary machine learning system that acts as a bridge between raw email data and actionable insights. The system uses natural language processing models and vectorization techniques as intermediate steps to transform unstructured email content into structured data that can be efficiently clustered and analyzed, thereby maintaining accuracy while reducing time loss.
2Productivity
If automated processing is implemented to reduce manual labor, then productivity increases, but the ability to accurately identify emerging trends and threats may deteriorate
Solution Approach 1:
The patent replaces manual threat analysis with automated machine learning systems that use natural language processing and clustering algorithms. These systems maintain high threat detection accuracy by learning from patterns in the data while significantly increasing productivity through automated processing of large email volumes, eliminating the trade-off between automation and accuracy.
Solution Approach 2:
The patent transforms the analysis process by changing parameters from manual human judgment to automated algorithmic processing. The system uses vectorization to convert text into numerical representations and applies clustering algorithms with adjustable parameters to identify patterns, maintaining detection accuracy while enabling high-speed automated processing of emails.
3Reliability
If all collected emails are analyzed in detail, then comprehensive threat detection is achieved, but resource utilization becomes inefficient
Solution Approach 1:
The patent extracts and processes only the most relevant features from email content using natural language processing models. By vectorizing emails and applying clustering algorithms, the system identifies and focuses on representative samples from each cluster, extracting key threat patterns without analyzing every single email in detail, thereby maintaining comprehensive detection while reducing resource consumption.
Solution Approach 2:
The patent segments the large volume of emails into clusters based on similarity using machine learning algorithms. This segmentation allows the system to analyze representative samples from each cluster rather than processing every email individually, maintaining comprehensive threat detection across all segments while significantly reducing the computational resources required for detailed analysis of the entire dataset.
Data Source
AI summary
A system and method of detecting malicious activity in emails using pattern recognition. The method includes maintaining a plurality of associations between a plurality of emails and a plurality of multi-dimensional (MD) vectors of the plurality of emails. Each association is between a respective email of the plurality of emails and a respective MD vector of the plurality of MD vectors that corresponds to the respective email. The method includes identifying, based on one or more keywords, a set of MD vectors of the plurality of MD vectors. The method includes selecting, based on the plurality of associations, a set of emails associated with the set of MD vectors. The method includes generating, by a processing device, based on the set of emails or the set of MD vectors, a set of clusters to represent patterns in the set of emails.