ML Email Threat Detection via Baseline Deviation Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional email security software is inadequate in detecting sophisticated email-based threats due to their reliance on attack signatures, inability to handle personalized and unique attacks, and the rarity of sophisticated malicious emails, leading to delayed email delivery and insufficient data for machine learning models.
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 email-based threats in real-time, including email account compromise and business email compromise, by analyzing email content, context, and user actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional email security software uses attack signatures and pattern matching, then it can detect known malicious emails, but it fails to detect sophisticated personalized attacks that lack signatures
Solution Approach 1:
The system performs preliminary action by collecting and storing communication patterns from legitimate emails before attacks occur. It builds a baseline of normal behavior for each user, including typical communication partners, message structures, and timing patterns. This pre-established baseline enables the system to detect deviations caused by sophisticated attacks without requiring advance signatures.
Solution Approach 2:
Instead of searching for known malicious patterns (top-down approach), the system inverts the approach by first establishing normal behavior patterns and then detecting deviations from this baseline (bottom-up approach). This inversion allows the system to detect zero-day attacks and personalized phishing that lack traditional attack signatures.
2Reliability
If machine learning models are trained on rare sophisticated malicious emails, then the model can learn attack patterns, but insufficient data limits model effectiveness
Solution Approach 1:
The system creates synthetic training data by copying and transforming legitimate communication patterns. It generates artificial malicious examples by introducing controlled deviations from normal user behavior patterns, effectively multiplying the available training data. This synthetic data augmentation enables the model to learn attack detection without requiring extensive collections of actual malicious emails.
Solution Approach 2:
The system implements feedback mechanisms where detected anomalies are reviewed and used to refine the training data. User reports of phishing attempts and security team annotations provide feedback that labels borderline cases, improving the training dataset quality over time. This feedback loop continuously enhances model accuracy despite the rarity of actual attack samples.
3Reliability
If email security software delays delivery to analyze threats, then detection accuracy improves, but email delivery speed decreases
Solution Approach 1:
The system performs preliminary analysis of email patterns and establishes baseline behavior before actual threats arrive. By pre-collecting communication patterns and building user profiles in advance, the system can make rapid comparisons against established norms without requiring time-consuming real-time analysis, thus maintaining fast delivery while ensuring accurate detection.
Solution Approach 2:
The system skips detailed analysis for emails that match established normal patterns, allowing them to pass through quickly to the user inbox. Only emails that deviate from baseline behavior trigger deep analysis, reducing overall processing time while maintaining high detection accuracy for actual threats. This selective analysis approach rushes legitimate emails through while thoroughly examining suspicious ones.
Data Source
AI summary
Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.