Email Sequence Detection for Multi-Segment Malicious Attacks
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Solution Overview
Problem
Existing email security systems struggle to efficiently detect multi-stage malicious email attacks, such as BEC spam attacks, which are challenging due to the computational complexity of existing detection methods and the rapid evolution of attack techniques, leading to potential financial and information losses.
Innovation Solution
An email security system that stores previously determined maliciousness predictions for past emails in a record database, allowing for efficient retrieval and combination with incoming email predictions to determine an overall maliciousness verdict, thereby reducing the need for real-time complex computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing detection methods are used to identify multi-stage malicious email attacks, then detection accuracy can be maintained, but computational complexity increases and real-time detection efficiency decreases
Solution Approach 1:
The detection system segments the email analysis process into multiple independent stages: initial feature extraction, preliminary maliciousness assessment, sequential email pattern recognition, and final verdict determination. Each stage processes specific features independently, reducing overall computational complexity while maintaining comprehensive detection accuracy for multi-stage attacks
Solution Approach 2:
The system performs preliminary analysis by extracting key features and determining initial maliciousness predictions for each email before full sequence analysis. This preliminary action prepares data in advance, enabling faster real-time processing of multi-stage attack patterns without sacrificing detection accuracy
2Reliability
If comprehensive analysis of multiple emails is performed in real-time, then detection of multi-stage attacks improves, but processing time increases
Solution Approach 1:
The system extracts email features and determines preliminary maliciousness predictions before complete sequence analysis is performed. This advance preparation of data enables rapid real-time processing when multi-stage attack patterns are identified, reducing processing time while maintaining comprehensive detection effectiveness
Solution Approach 2:
The detection system operates in periodic cycles: initial feature extraction for incoming emails, sequential analysis when patterns are detected, and verdict determination. This periodic operation allows the system to maintain real-time responsiveness for most emails while performing comprehensive multi-stage analysis only when necessary
3Productivity
If traditional email security scanning is used, then individual malicious emails can be blocked, but multi-stage attack sequences are missed
Solution Approach 1:
The system performs multiple functions simultaneously: it processes individual emails for immediate delivery decisions while also analyzing sequences of emails from the same sender to identify multi-stage attack patterns. This multi-functionality enables the system to maintain high email processing speed while achieving complete attack detection through sequential pattern recognition
Data Source
AI summary
A method to perform the techniques described herein includes receiving a first email from a first sender to a first receiver. The method may include determining a first maliciousness prediction that indicates a first likelihood that the first email is malicious. The method may include determining that the first maliciousness prediction fails to satisfy a maliciousness pattern associated with malicious emails. The method may include receiving a second email from the first sender to the first receiver. The method may include determining that the first email and second email were received within a threshold period of time. The method may include determining an overall maliciousness prediction that indicates an overall likelihood that the first email and second email in combination are malicious. The method may include determining that the overall maliciousness prediction satisfies the maliciousness pattern.