Distributed Email Threat Detection with Local Model Training
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Solution Overview
Problem
Current e-mail filtering techniques are slow and reactionary, often allowing malicious e-mails to reach users before they are identified, and are ineffective in targeting specific entities due to one-size-fits-all approaches, leading to improper labeling of emails as malicious or safe.
Innovation Solution
The implementation of distributed machine learning techniques that allow for local training of models by different entities, using feedback to update or generate new models that can identify malicious emails specific to each entity's threats without sharing email data, enabling more efficient and targeted identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If e-mail gateways and threat intelligence data feeds are used to filter malicious e-mails, then malicious e-mail content can be identified, but the system is slow and reactionary, allowing malicious e-mails to reach users before they are identified
Solution Approach 1:
The system performs preliminary actions by distributing models to multiple entities in advance, enabling them to train locally on their own data before threats emerge. This allows the system to proactively build detection capabilities across the network rather than waiting for threats to be discovered and then distributing updates.
Solution Approach 2:
The system implements feedback mechanisms where trained models are shared back to the central server, which then distributes updated models to all entities. This continuous feedback loop enables the system to learn from each entity's local data and improve detection accuracy across the entire network over time.
2Ease of manufacture
If one-size-fits-all filtering rules are applied to all entities, then implementation is simplified, but the system cannot effectively target specific entities, leading to improper labeling of emails
Solution Approach 1:
The system applies local quality by allowing each entity to train models on its own local data, creating entity-specific detection patterns. Each entity's model is customized to its unique threat landscape and email characteristics, rather than applying uniform rules to all entities.
Solution Approach 2:
The system segments the filtering approach by dividing the model training process into entity-specific instances. Each entity maintains its own trained model instance that is customized to its needs, while the overall system architecture remains unified and coordinated through the central server.
3Loss of information
If centralized threat intelligence data feeds are used, then information can be distributed to network infrastructure, but delays occur in sharing information, creating windows of vulnerability
Solution Approach 1:
The system performs preliminary action by distributing model instances to all entities in advance, enabling them to begin training immediately on their local data. This eliminates waiting time for threat intelligence updates, as each entity continuously learns from its own data while the system evolves.
Solution Approach 2:
The system uses feedback loops where each entity's trained model is shared back to the central server, which then redistributes aggregated learnings to all entities. This continuous feedback mechanism ensures that threat intelligence is shared rapidly across the network without centralized bottlenecks.
4Measurement precision
If e-mail data is shared across entities to improve detection, then comprehensive threat identification can be achieved, but data privacy is compromised
Solution Approach 1:
The system extracts only the essential model parameters and training patterns from each entity's local data, rather than sharing the actual email content. This allows comprehensive threat detection to be achieved by aggregating learned patterns while keeping sensitive email data localized and private to each entity.
Solution Approach 2:
The central server acts as an intermediary that coordinates model distribution and aggregation without directly accessing or storing entity-specific email data. Each entity trains models locally and shares only model parameters through the intermediary, maintaining data privacy while enabling comprehensive threat detection across the network.
Data Source
AI summary
Embodiments of the present disclosure provide centralized and coordinate learning techniques for identifying malicious e-mails while maintaining privacy of the analyzed e-mails of different organizations. One or more models may be generated and configured to construct feature sets that may be used to characterize e-mails as malicious or safe. Feedback associated with one or more models trained by a first organization (and other organizations) may be shared with a modelling device to modify parameters of the one or more models, where the modified parameters are configured to improve identification of malicious e-mail threats. The feedback provided by the first organization may not include e-mails received by the first organization, thereby enabling the privacy of the e-mails received by the first organization to be maintained in an confidential manner even though the updated parameters may be shared with a second organization.