ML Email Categorization Engine for Adaptive Threat Neutralization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing solutions for credential harvesting and spam emails are inadequate in detecting and neutralizing sophisticated threats due to their reactive nature and reliance on known threat signatures, failing to keep pace with evolving malicious tactics.
Innovation Solution
A system and method utilizing a machine learning model in a categorization engine to analyze and categorize email messages as malicious or non-malicious, applying metadata for sorting and modifying hyperlinks, with feedback loops for model refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional spam filtering methods are used, then known threat signatures can be detected, but sophisticated evolving threats cannot be detected
Solution Approach 1:
The system transitions from static threat signatures to dynamic machine learning models that continuously adapt to new threat patterns. The categorization engine uses trained models that can learn from new data, enabling detection of evolving threats while maintaining reliability through continuous improvement of detection accuracy.
Solution Approach 2:
The system incorporates feedback loops where misclassified messages are fed back into the machine learning model for retraining. This feedback mechanism allows the model to learn from errors and improve its detection accuracy over time, simultaneously enhancing both reliability and adaptability to new threat types.
2Reliability
If machine learning model is used for categorization, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The system divides the email processing workflow into distinct segments: a coarse filter for simple threats, a machine learning categorization engine for sophisticated threats, and a feedback loop for model improvement. This segmentation allows the complex ML model to be applied only where needed, managing overall system complexity while maintaining high detection accuracy.
Solution Approach 2:
The categorization engine acts as an intermediary between the coarse filter and the final message delivery. It receives messages from the coarse filter, applies machine learning analysis, and then passes processed messages to the email exchange application, thereby managing complexity through a structured intermediate layer.
3Reliability
If all messages are processed through detailed analysis, then detection accuracy improves, but processing time increases
Solution Approach 1:
The system applies partial analysis by first using a coarse filter that performs simple, quick checks on all messages. Only messages that pass this initial filter undergo detailed machine learning analysis. This partial action approach reduces processing time for the majority of messages while maintaining high detection accuracy for sophisticated threats.
Solution Approach 2:
The coarse filter performs preliminary action by quickly identifying and removing obviously malicious messages before they reach the machine learning categorization engine. This preliminary filtering reduces the workload on the more time-consuming ML analysis, thereby reducing overall processing time while maintaining accuracy.
Data Source
AI summary
Systems, computer program products, and methods are described herein for electronic message data analysis and neutralization via machine learning. The present disclosure includes receiving message data at a gateway server, analyzing, using a coarse message data filter, the message data, receiving, upon a condition where the message data does not meet the at least one coarse message data filtering condition, the message data in a categorization engine, scoring, using the categorization engine, the message data based on contents of the message data, categorizing the message data in a category of malicious or non-malicious, applying metadata to the message data based on the category, transmitting the message data to an endpoint device having an email exchange application with a malicious email filtering plug-in, and displaying the message data in a predetermined folder of the email exchange application based on the category.