Email Threat Detection via Behavioral Deviation Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional email security software is inadequate in detecting sophisticated email-based threats due to their personalized nature, lack of attack signatures, high email volume, time sensitivity, and rarity of sophisticated attacks, which leads to delays and inefficiencies in threat detection and mitigation.
Innovation Solution
A threat detection platform that builds a model of normal email behavior to identify deviations, using machine learning, heuristics, and human feedback to detect and remediate threats such as email account compromise and business email compromise in real-time, by analyzing email attributes and content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional email security software uses attack signatures and pattern matching, then it can detect known threats, but it fails to detect sophisticated personalized attacks that lack signatures
Solution Approach 1:
The system performs preliminary actions by collecting and storing email usage data and behavioral patterns before attacks occur. It builds models of normal email behavior and establishes baselines for detection, enabling identification of sophisticated attacks that deviate from established patterns without requiring pre-existing attack signatures.
Solution Approach 2:
The system incorporates feedback mechanisms where security team annotations and user reports are fed back into the machine learning models to continuously improve detection accuracy. This feedback loop enables the system to adapt to new attack variants and refine its understanding of sophisticated threats over time.
2Reliability
If the system delays email delivery to analyze security threats, then detection accuracy improves, but email processing speed and productivity deteriorate
Solution Approach 1:
The system segments the email analysis process into multiple stages with different levels of scrutiny. Most emails undergo rapid initial assessment and are processed quickly, while only suspicious emails trigger deeper analysis. This segmentation allows the system to maintain high overall processing speed while ensuring thorough security checks for potential threats.
Solution Approach 2:
The system applies partial analysis to the majority of emails using lightweight detection rules and machine learning models, reserving more intensive analysis for only those emails that trigger suspicion thresholds. This partial action approach maintains productivity by avoiding exhaustive analysis of all emails while ensuring thorough investigation of potential threats.
3Reliability
If the system collects and analyzes extensive email data to improve detection models, then detection accuracy improves, but data processing complexity and system resource requirements increase
Solution Approach 1:
The system extracts and focuses analysis on specific high-value attributes and features from email data rather than processing all data uniformly. It identifies and analyzes key indicators such as sender behavior patterns, email metadata anomalies, and contextual relationships, reducing processing complexity while maintaining high detection accuracy through targeted feature extraction.
Solution Approach 2:
The system applies different levels of data processing and analysis quality to different parts of the email ecosystem. It collects and analyzes data at specific critical points in the email lifecycle (e.g., incoming mail gateway, before delivery) rather than uniformly across all email handling processes, optimizing the balance between detection accuracy and processing complexity.
Data Source
AI summary
Access to emails delivered to an employee of an enterprise is received. An incoming email addressed to the employee is acquired. A primary attribute is extracted from the incoming email by parsing at least one of: (1) content of the incoming email or (2) metadata associated with the incoming email. It is determined whether the incoming email deviates from past email activity, at least in part by determining, as a secondary attribute, a mismatch between a previous value for the primary attribute and a current value for the primary attribute, using a communication profile associated with the employee, and providing a measured deviation to at least one machine learning model.