Multi-Domain ML for Malicious Traffic Detection
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Solution Overview
Problem
Existing systems are inadequate in detecting and preventing malicious network traffic across multiple domains, as they often lack sufficient labeled data for effective model training, and there is a risk of exposing sensitive information when sharing data for cross-domain learning.
Innovation Solution
The implementation of multi-domain machine learning and cross-domain training methods that leverage labeled data from one domain to improve model performance in another without disclosing personally identifiable or restricted information, using embeddings to create a common vector space for traffic analysis across domains like cyber and advertising.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from multiple domains is shared for cross-domain learning, then model performance in detecting malicious traffic is improved, but sensitive information and personally identifiable data may be exposed
Solution Approach 1:
The patent introduces an intermediary mechanism (embedding layer) that transforms domain-specific data into a common vector space representation. This embedding acts as a mediator that enables cross-domain learning while preventing direct exposure of sensitive information between domains. The embedding layer processes data from multiple domains and creates unified representations that capture malicious behavior patterns without preserving identifiable information from individual domains.
Solution Approach 2:
The patent creates a simplified copy of domain data in the form of embeddings that capture essential features for malicious traffic detection without copying the original sensitive data. These embedding representations serve as substitutes that retain the useful patterns needed for cross-domain detection while eliminating the risk of exposing personally identifiable information and sensitive domain data.
2Measurement precision
If labeled data from one domain is used to train models in another domain, then detection accuracy is improved, but the complexity of data processing and model training increases
Solution Approach 1:
The patent creates a universal embedding space that serves multiple domains simultaneously. The same embedding infrastructure and common vector space can process data from different domains (cybersecurity, advertising, etc.) using unified mechanisms. This multi-functional approach enables cross-domain training while reducing complexity by using a single shared representation framework rather than separate domain-specific processing pipelines.
Solution Approach 2:
The patent transforms data from multiple domains into a common parameter space (vector space) through embedding. This parameter transformation consolidates diverse data formats and structures into a unified representation that can be processed using consistent algorithms. The embedding process changes the representation parameters of data from various domains, enabling simplified cross-domain processing and training.
3Adaptability or versatility
If domain-specific models are trained separately, then each model can be optimized for its specific domain, but the overall detection capability across multiple domains is limited
Solution Approach 1:
The patent merges multiple domain-specific models into a unified multi-domain detection system through the common embedding space. By combining models that process data from different domains (cybersecurity, advertising, etc.) within a single integrated framework, the system achieves both domain-specific optimization and cross-domain versatility. The merged architecture allows knowledge transfer and pattern recognition across domains while maintaining the ability to handle domain-specific characteristics.
Data Source
AI summary
System and methods for cross-domain training and updating of models to perform classification and scoring of network data/traffic are described. Information used to build deep machine learning models about traffic in one domain is used to improve the modeling in another domain. By using cross-domain learning, labeled data from another domain can be used to improve the detection rate and false positive rate of an analytic model in another domain. Because of the construction of the models, and because the models, and not the data are transferred, there is no disclosure of personally identifiable or otherwise restricted information.