Multimodal Embedding Fusion for Unauthorized Access 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 detecting and mitigating complex cyber threats.
Innovation Solution
A cybersecurity platform incorporating multimodal data fusion, privacy-preserving federated learning, and adaptive policy management using a Multi-Agent System (MAS) framework, with advanced AI-driven risk mitigation strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized data analysis is used to detect cybersecurity threats, then detection capability is improved, but data privacy risks worsen
Solution Approach 1:
The system segments data analysis by implementing local nodes that process data independently within their own environments. Each node has local copies of models that analyze data locally, with only model updates and aggregations shared centrally, thereby maintaining detection capability while minimizing data privacy risks through data localization.
Solution Approach 2:
The system introduces a federated learning framework as an intermediary between data sources and central analysis. This intermediary enables collaborative model training across distributed nodes without requiring raw data to leave local environments, resolving the contradiction by allowing centralized intelligence development while preserving local data privacy.
2Device complexity
If single-modality cybersecurity detection is used, then system complexity is reduced, but detection effectiveness worsens against multi-modal threats
Solution Approach 1:
The system implements a universal detection platform that handles multiple data modalities (network traffic, endpoint data, cloud logs, device metadata) through a single integrated architecture. The shared vector space and unified model framework enable the system to process diverse data types effectively, improving detection effectiveness against multi-modal threats while avoiding the complexity of separate single-modality systems.
Solution Approach 2:
The system merges multiple modalities of cybersecurity data into a unified representation using shared embedding vectors. By combining network traffic analysis, endpoint monitoring, cloud logging, and device metadata processing through a common neural network architecture, the system achieves sophisticated multi-modal threat detection without proportionally increasing overall system complexity.
3Measurement precision
If custom loss functions with multiple loss functions are used, then training accuracy is improved, but computational cost worsens
Solution Approach 1:
The system dynamically adjusts loss function weights and parameters based on the specific data modalities being processed and the security context. By adapting the combination of contrastive loss and triplet loss parameters to match the characteristics of different data types and threat scenarios, the system achieves high training accuracy while optimizing computational resource consumption for each specific task.
Data Source
AI summary
A method for detecting unauthorized access attempts to a digital system using multi-modal data and neural networks. The method involves receiving multi-modal data indicative of an access attempt and processing different portions of this data using modality-specific layers of a neural network to generate corresponding embedding vectors. Custom embedding vectors are then generated for each data portion using respective neural network layers. These custom embedding vectors are combined into a single embedding vector through a fusion layer, with all layers being jointly trained based on a common loss function. The combined embedding vector is processed by a trained model to determine whether the access attempt is unauthorized.