Email Threat Detection Models for Real-Time Attack Characterization
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Solution Overview
Problem
Conventional email security software struggles to detect sophisticated malicious emails that bypass existing protective measures, often due to their personalization, lack of attack signatures, and the rarity of such threats, leading to delayed email delivery and inadequate protection against email-based attacks like account compromise and business email compromise.
Innovation Solution
A threat detection platform employing a multi-tiered ensemble model with machine learning, heuristics, and human-in-the-loop feedback to identify abnormal email behavior and facilitate real-time detection and remediation of sophisticated threats, including email account compromise and business email compromise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional email security software uses attack signatures and pattern matching to detect malicious emails, then it can identify known threat patterns, but it fails to detect sophisticated personalized attacks that lack signatures and use unique content
Solution Approach 1:
The system continuously learns from user actions and feedback to refine its detection models. When users interact with emails (opening, deleting, responding), this feedback is used to update the machine learning models, enabling the system to adapt to new attack patterns and improve detection accuracy over time without requiring manual signature updates
Solution Approach 2:
The system transitions from static signature-based detection to dynamic behavior-based detection. It monitors changes in email interaction patterns, sender-receiver relationships, and temporal patterns, using these parameter changes to identify anomalies that indicate malicious activity even when no known signatures exist
2Reliability
If conventional email security software indefinitely delays email delivery to ensure thorough security checking, then it can detect threats, but it causes significant time delay and disrupts email flow
Solution Approach 1:
The system performs preliminary security checks using machine learning models that can quickly assess email risk before full analysis. This allows most emails to pass through rapidly while only potentially malicious emails undergo deeper, more time-consuming inspection, reducing overall delivery delay while maintaining security
Solution Approach 2:
The system dynamically adjusts the level of inspection based on real-time risk assessment. High-risk emails receive intensive analysis with longer processing times, while low-risk emails receive minimal inspection and pass through quickly. This dynamic approach ensures security without imposing uniform time delays on all emails
3Reliability
If conventional email security software processes all emails through thorough analysis, then it can detect sophisticated threats, but it cannot keep up with the large volume of emails handled by enterprises
Solution Approach 1:
The system segments the email processing workload into multiple tiers: quick machine learning-based triage for most emails, followed by targeted deep analysis only for suspicious messages. This segmentation allows the system to maintain high throughput by avoiding exhaustive analysis of benign emails while preserving detection capability for threats
Solution Approach 2:
The system applies partial analysis to the majority of emails using efficient machine learning models, reserving excessive (thorough) analysis only for emails that trigger risk flags. This partial action approach processes large volumes of emails quickly while maintaining the ability to thoroughly investigate potentially malicious messages
4Measurement precision
If machine learning models are trained on limited data from rare sophisticated attacks, then the models can identify attack patterns, but they lack sufficient training data to accurately detect novel personalized attacks
Solution Approach 1:
The system performs self-service learning by automatically analyzing user interactions with emails and using this data to train and refine its models. This continuous self-training process enables the system to learn from real-world attack patterns without requiring external datasets, gradually building comprehensive knowledge of sophisticated attack techniques
Solution Approach 2:
The system uses feedback from user actions (opening, deleting, responding to emails) to continuously improve its detection models. This feedback loop provides abundant training data from actual user behavior patterns, enabling the system to learn sophisticated attack detection capabilities without requiring pre-existing large datasets of labeled attack emails
Data Source
AI summary
A message addressed to a user is received. A first model is applied to the message to produce a first output indicative of whether the message is representative of a non-malicious message. The first model is trained using past messages that have been verified as non-malicious messages. It is determined, based on the first output, that the message is potentially a malicious message. Responsive to determining that the message is potentially a malicious email based on the first output, apply a second model to the message to produce a second output indicative of whether the message is representative of a given type of attack. The second model is one of a plurality of models. At least one model included in the plurality of models is associated with characterizing a goal of the malicious message. An action is performed with respect to the message based on the second output.