Custom Loss Functions for Multimodal Cybersecurity Threat Detection
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Solution Overview
Problem
Cybersecurity technologies often fail to effectively detect and mitigate sophisticated, multi-modal threats due to their reliance on individual modalities and pose significant privacy risks through centralized data analysis.
Innovation Solution
A cybersecurity platform incorporating multimodal data fusion, federated learning, and AI-driven risk mitigation, using custom loss functions and reinforcement learning to analyze multiple data modalities while preserving privacy and adapting to regulatory changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized data analysis is used to detect cybersecurity threats, then detection accuracy is improved, but privacy risks increase
Solution Approach 1:
The system segments data processing by implementing modality-specific layers for different data types (text, image, audio) and distributing analysis across multiple independent nodes. Each node processes its local data independently, preventing centralized data collection while maintaining detection capability through coordinated analysis of segmented results.
Solution Approach 2:
The patent introduces an intermediary embedding space that mediates between different data modalities and analysis nodes. Instead of centralizing raw data, the system creates a shared vector space where modality-specific embeddings are projected and fused, enabling coordination without direct data sharing and thus preserving privacy.
2Device complexity
If multiple data modalities are processed separately, then processing complexity is reduced, but threat detection effectiveness deteriorates
Solution Approach 1:
The system merges modality-specific processing streams through a unified embedding space and fusion mechanism. Each modality maintains its own specialized layer for initial processing, then all modalities are projected into a shared vector space where their representations are combined, enabling effective multi-modal threat detection while keeping individual processing paths manageable.
Solution Approach 2:
The patent creates a universal embedding space that serves multiple functions: it accommodates different data modalities, enables cross-modality relationships, and provides a common representation for threat detection. This multi-functional space allows the system to handle diverse data types without requiring separate specialized systems for each modality.
3Reliability
If custom loss functions with multiple loss functions are used, then training effectiveness is improved, but computational requirements increase
Solution Approach 1:
The system applies different loss functions to different modalities based on their specific characteristics. Each modality-specific layer uses a loss function optimized for its data type (e.g., contrastive loss for image embeddings, triplet loss for text embeddings), allowing tailored training that improves effectiveness while avoiding the computational overhead of applying all possible loss functions uniformly across all data.
Data Source
AI summary
A method for training a neural network for detecting cybersecurity threats to a digital system. The method involves receiving multi-modal data for training the neural network and processing different portions of this data using modality-specific layers of a neural network to generate corresponding embedding vectors. The layers of the neural network are trained to be configured to generate custom embedding vectors. An additional layer of the neural network is trained to identify patterns across multiple modalities. All layers are trained jointly on a common loss function generated as a weighted combination of multiple loss functions. The layers are trained to generate an indication whether multimodal attempts to access a digital system are unauthorized.