Federated Cybersecurity Agents for Privacy-Preserving Threat Detection
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Solution Overview
Problem
Cybersecurity technologies often focus on individual modalities, failing to address sophisticated, multi-modal threats, and centralized data analysis poses privacy risks, making them inadequate for modern cyber-attacks and highly regulated industries.
Innovation Solution
A cybersecurity platform incorporating multimodal data fusion, federated learning, and a Multi-Agent System (MAS) for real-time risk mitigation, using secure hardware-based processing and privacy-preserving techniques to analyze multiple data modalities while ensuring data privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized data analysis is used to detect cybersecurity threats, then detection capability is improved, but data privacy is compromised
Solution Approach 1:
The system segments the centralized analysis architecture into distributed local nodes, each performing threat detection locally on their own data. This segmentation allows each node to maintain data privacy while collectively achieving comprehensive threat detection through federated learning, resolving the contradiction between detection capability and data privacy.
Solution Approach 2:
The patent introduces federated learning as an intermediary mechanism that enables collaborative threat detection without direct data sharing. The federated learning framework acts as a mediator that aggregates insights from multiple nodes while preserving the confidentiality of local data, thus improving detection capability without compromising privacy.
2Device complexity
If single-modality analysis is used for cybersecurity detection, then system complexity is reduced, but detection accuracy for sophisticated threats deteriorates
Solution Approach 1:
The system merges multiple data modalities (network traffic, endpoint data, cloud logs, etc.) into a unified federated learning framework. This merging enables comprehensive multi-modal threat detection while managing complexity through standardized data processing pipelines and federated learning protocols that handle diverse inputs systematically.
Solution Approach 2:
The federated learning platform is designed as a universal system that can process multiple types of security data modalities through common architectural components. This multi-functionality allows the system to handle diverse threat indicators across different data sources without proportionally increasing complexity, as the same federated learning infrastructure serves all modalities.
3Speed
If real-time threat response is implemented, then response speed is improved, but decision accuracy may deteriorate due to limited analysis time
Solution Approach 1:
The system performs preliminary threat analysis and model training in advance through continuous federated learning operations. By pre-processing data and updating threat detection models continuously in the background, the system prepares detection capabilities beforehand, enabling fast real-time responses without sacrificing accuracy, as the heavy computational work is done preliminarily.
Solution Approach 2:
The federated learning framework enables continuous model improvement and threat detection without interruption. The system maintains continuous learning operations across distributed nodes, ensuring that detection models are constantly refined while providing uninterrupted real-time threat response capability, thus maintaining both speed and accuracy.
Data Source
AI summary
A multi-agent system implementing federated learning for cybersecurity applications. The system includes one or more processing devices that perform operations including receiving, from each node of multiple nodes of the distributed system, a plurality of parameters representative of model updates to a corresponding machine-learning model. The corresponding machine learning model is trained to detect cybersecurity threats within a context of the corresponding node. Based on the parameters received from the multiple nodes, a set of global model parameters that represents global updates to the individual machine-learning models. The global model parameters are transmitted to at least a subset of the multiple nodes of the distributed system, and the global model parameters are configured to update local model parameters of the corresponding machine learning model at each node.