Automated Email Campaign Detection Engine
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional cybersecurity systems face challenges in detecting large-scale malicious email campaigns efficiently, relying heavily on human analysis which is prone to inefficiencies and errors, making it difficult to identify and triage repetitive cybersecurity attacks effectively.
Innovation Solution
A cybersecurity system with an email campaign detection engine that automatically identifies malicious email campaigns by extracting features from email messages, performing pre-processing, and using analytic logic to determine correlations, thereby classifying messages as part of a campaign without human interaction, and issuing alerts to administrators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional cybersecurity systems rely on human analysis to detect malicious email campaigns, then the ability to identify and triage attacks is improved, but efficiency and accuracy deteriorate due to inefficiencies and errors in manual analysis
Solution Approach 1:
The patent replaces manual human analysis with an automated email campaign detection engine that uses machine learning models and analytics to identify malicious email campaigns. The system automatically extracts features from email messages, correlates them using analytics logic, and generates alerts without human intervention, thereby eliminating the inefficiencies and errors associated with manual analysis while maintaining high detection accuracy.
2Reliability
If human analysts manually analyze email messages to detect campaigns, then detailed inspection is possible, but time consumption and resource requirements increase significantly
Solution Approach 1:
The system implements self-service automation where the email campaign detection engine independently performs feature extraction, correlation analysis, and campaign identification without requiring human analyst intervention. The machine learning models automatically process email messages, correlate features, and generate alerts, enabling the system to serve itself and eliminate time-consuming manual analysis while maintaining reliable detection through automated validation mechanisms.
3Ease of operation
If conventional systems analyze each email message individually, then detailed examination of each message is achieved, but the ability to detect large-scale repetitive attacks deteriorates
Solution Approach 1:
The patent merges individual email message analysis with campaign-level pattern recognition by implementing a detection engine that extracts features from each message and then correlates these features across multiple messages using analytics logic. This combination allows the system to maintain detailed examination capabilities for individual messages while simultaneously detecting large-scale repetitive attacks by identifying patterns and correlations across the email corpus.
4Measurement precision
If manual analysis methods are used to identify email campaigns, then human expertise can be applied, but scalability and automation capability are limited
Solution Approach 1:
The patent replaces manual expert analysis with automated machine learning models that replicate and scale human expertise. The detection engine uses trained models to extract and correlate features from email messages, automatically identifying campaigns with expert-level precision. This substitution enables full automation of the detection process while maintaining or exceeding the quality of expert human analysis, and allows the system to scale to handle large volumes of emails without additional human resources.
Data Source
AI summary
According to one embodiment, a system for detecting an email campaign includes feature extraction logic, pre-processing logic, campaign analysis logic and a reporting engine. The feature extraction logic obtains features from each of a plurality of malicious email messages received for analysis while the pre-processing logic generates a plurality of email representations that are arranged in an ordered sequence and correspond to the plurality of malicious email message. The campaign analysis logic determines the presence of an email campaign in response to a prescribed number of successive email representations being correlated to each other, where the results of the email campaign detection are provided to a security administrator via the reporting engine.