Dynamic ML Model Augmentation for Malware Detection
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Solution Overview
Problem
Existing machine learning-based malware detection systems face inefficiencies due to resource constraints across different execution environments, leading to suboptimal model usage and potential evasion by malicious developers.
Innovation Solution
The system dynamically augments machine learning models based on contextual factors such as performance profiles, processing resources, and software environments to optimize model performance and resource utilization across various execution environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a more complicated and robust machine learning model is used on a desktop computing system, then detection accuracy improves, but resource consumption exceeds available computing resources
Solution Approach 1:
The patent implements dynamic model selection where the machine learning model complexity is adjusted based on the execution environment's computing resources. The system evaluates available resources (CPU, memory, bandwidth) and dynamically selects or augments the appropriate model subset, allowing desktop systems to use lighter models while gateway servers can utilize more complex models for higher detection accuracy.
Solution Approach 2:
The patent creates different model configurations tailored to specific execution environments. Desktop computing systems receive simplified models with fewer features, while dedicated gateway servers receive comprehensive robust models. This local customization ensures each environment uses the optimal model complexity for its resource constraints while maintaining adequate detection accuracy.
2Productivity
If a simpler machine learning model is used on a dedicated gateway server, then resource efficiency improves, but detection capability is underutilized
Solution Approach 1:
The system dynamically determines the appropriate model complexity based on the execution environment. Gateway servers with abundant resources are automatically assigned more complex and robust models that can fully utilize their computing power for enhanced detection capability, rather than being constrained by simpler models designed for resource-constrained environments.
Solution Approach 2:
The patent changes the model parameters (complexity, number of features, depth of decision trees) based on the execution environment's capabilities. Gateway servers receive models with higher parameter complexity that can process more features and relationships, thereby improving detection capability while efficiently utilizing the available computing resources.
3Productivity
If multiple different machine learning models are created for different execution environments, then resource optimization improves, but model maintenance complexity increases
Solution Approach 1:
The patent segments the machine learning model into a core model and optional augmentation components. This segmentation allows the system to maintain a single base model while providing environment-specific augmentations (such as additional decision trees or features) that can be selectively applied. This reduces maintenance complexity compared to maintaining entirely separate models for each environment.
Solution Approach 2:
The patent creates a universal base machine learning model that can function across all execution environments, with the ability to augment the model based on environmental capabilities. This universal approach eliminates the need to maintain multiple completely different models, reducing development and maintenance overhead while still optimizing resource utilization for each environment.
4Stability of the object's composition
If a fixed machine learning model is used across all execution environments, then model consistency improves, but resource utilization becomes inefficient
Solution Approach 1:
The system transitions from a static fixed model to a dynamic model configuration that adapts to execution environments. The machine learning model structure remains consistent (maintaining stability), but the active components and complexity level are dynamically adjusted based on available computing resources, achieving both consistency and efficient resource utilization.
Solution Approach 2:
The patent maintains model consistency by using the same base model structure across all environments, but changes parameters such as the number of active decision trees, features enabled, and model depth based on resource availability. This allows the core model to remain consistent while optimizing resource utilization for each specific execution environment.
Data Source
AI summary
The disclosed computer-implemented method for dynamically augmenting machine learning models based on contextual factors associated with execution environments may include (1) generating a base machine learning model and a supplemental set of machine learning models, (2) determining at least one contextual factor associated with an execution environment of a machine learning system that is configured to make predictions regarding a set of input data using at least the base machine learning model, (3) selecting, based on the contextual factor, a continuation set of machine learning models from the supplemental set of machine learning models, and (4) directing the machine learning system to utilize both the base machine learning model and the continuation set of machine learning models when making predictions regarding the set of input data. Various other methods, systems, and computer-readable media are also disclosed.