Email Threat Detection Platform Using Behavioral Baselines
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Solution Overview
Problem
Conventional email security software struggles to detect and mitigate sophisticated email-based threats due to their personalized and unique nature, lack of attack signatures, high email volume, and limited data for machine learning models.
Innovation Solution
A threat detection platform that builds a model of normal email behavior and applies it to incoming emails to identify deviations, using machine learning, heuristics, and human feedback to detect and remediate various types of email-based threats.
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 communication data before attacks occur, building baseline models of normal behavior patterns. This allows the system to detect deviations from established norms without requiring pre-existing attack signatures, enabling detection of novel sophisticated threats.
Solution Approach 2:
The system incorporates feedback mechanisms where detected anomalies 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 evolving attack patterns and refine its ability to detect sophisticated personalized attacks over time.
2Reliability
If the system delays email delivery to analyze security threats, then detection accuracy improves, but email processing speed decreases
Solution Approach 1:
The system performs preliminary analysis actions in the background using machine learning models that continuously learn from historical data. This allows the system to maintain high detection accuracy without requiring real-time delays, as the heavy computational tasks are performed asynchronously using pre-trained models.
Solution Approach 2:
The system applies partial analysis actions by using lightweight rule-based filtering for obvious threats while reserving deep machine learning analysis for suspected emails. This selective approach maintains processing speed for the majority of emails while ensuring thorough analysis only when necessary, balancing speed and detection accuracy.
3Adaptability or versatility
If machine learning models are trained on limited data, then they can be customized to specific enterprises, but they lack the data volume needed for accurate pattern recognition
Solution Approach 1:
The system uses copying by creating synthetic training data that replicates real communication patterns. The machine learning models are trained on synthesized versions of enterprise communications that capture essential patterns without requiring actual sensitive data, enabling accurate pattern recognition while maintaining data privacy and security.
Solution Approach 2:
The system extracts key features and patterns from communication data without extracting actual sensitive content. By taking out only the structural and behavioral patterns (such as timing, formatting, and contextual relationships) while leaving out sensitive data, the system achieves accurate pattern recognition without compromising data security or requiring large volumes of real enterprise data.
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.