Dynamic ML Model Storage for Adversarial Threat Filtering
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Solution Overview
Problem
Existing machine learning systems are vulnerable to adversarial attacks, and there is a need for a system that can defend against evolving threats while maintaining computational efficiency and stability.
Innovation Solution
A distributed model with dynamic spatial decomposition (DM-DSD) system that continuously updates and filters machine learning models based on attributes and performance metrics, forming ensembles to process requests efficiently and accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large number of machine learning models are stored and used to handle diverse adversarial attacks, then the system's ability to defend against evolving threats improves, but the computational complexity and resource consumption increase
Solution Approach 1:
The patent segments the model storage into multiple categories based on attack types and characteristics. Each category stores models specific to certain attack patterns, allowing the system to divide the large model set into manageable segments that can be selectively accessed based on the detected attack type, reducing overall computational complexity while maintaining comprehensive defense capability
Solution Approach 2:
The system dynamically updates and filters the model storage based on detected attack patterns and system performance. Models are added, removed, or prioritized based on real-time conditions, enabling the system to adapt its computational resources to current threats without maintaining a static large model collection, thus managing complexity while improving reliability
2Adaptability or versatility
If machine learning models are continuously updated and filtered to maintain accuracy, then the system's adaptability to evolving attacks improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary filtering and categorization of models before they are needed for attack detection. Models are pre-organized by attack type and characteristics, and the system pre-establishes criteria for model selection and replacement. This preliminary organization reduces the time required during actual attack response, as the system can quickly access pre-sorted model categories rather than searching through the entire model storage
Solution Approach 2:
The system implements feedback mechanisms that monitor attack detection performance and model effectiveness. Based on this feedback, the system automatically adjusts which models to prioritize for updates and which to remove, enabling continuous adaptation to evolving attacks. The feedback loop ensures that computational resources are allocated to models that provide the most current protective value, balancing adaptability with processing efficiency
3Reliability
If a comprehensive set of models is maintained to cover all possible attack scenarios, then the system's robustness against adversarial attacks improves, but the storage requirements and system complexity increase
Solution Approach 1:
The patent applies local quality by storing and managing models with specific characteristics tailored to particular attack types rather than maintaining a uniform comprehensive model set. Each attack category has models optimized for its specific patterns, allowing the system to store only the necessary model variants for each local scenario rather than redundant comprehensive models, reducing overall storage requirements while maintaining robustness
Solution Approach 2:
The system dynamically changes storage parameters such as model resolution, precision level, and cache size based on the detected attack type and system resources. For common or high-priority attack scenarios, the system maintains higher-quality models with more storage resources, while less critical attack types use compressed or simplified model representations, optimizing the balance between robustness and storage requirements
Data Source
AI summary
In an embodiment, a method includes updating a first model storage that includes a first plurality of machine learning (ML) models to generate a second model storage that includes a second plurality of ML models. The second plurality of ML models is different than the first plurality of ML models. A representation of a request is received. A set of attributes associated with the request is identified. The second model storage is filtered, based on the set of state features, to identify a subset of ML models included in the second model storage. Each model from the subset of ML models is associated with at least one attribute from the set attributes. The request is caused to be processed using the subset of ML models to generate an output.