AI Model Security via Moving Target Defense
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Solution Overview
Problem
Current AI/ML-based endpoint protection systems are vulnerable to adversarial attacks, where attackers can manipulate AI models to evade detection by altering or obfuscating malware, exploiting global model vulnerabilities and leading to incorrect predictions or decisions, thus failing to account for unique user and device behaviors.
Innovation Solution
Implementing AI-based model security using moving target defenses with Low-Rank Adaptation (LoRA) for fine-tuning pre-trained endpoint protection models locally on each device, tailoring them to specific user and platform behaviors, making each model unique and more accurate than static models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a pre-trained endpoint detection model is deployed globally, then malware detection coverage is improved, but the system becomes vulnerable to adversarial attacks that exploit global model vulnerabilities
Solution Approach 1:
The patent implements dynamic model adaptation by fine-tuning the globally pre-trained endpoint detection model with local behavior data from each device. This creates a dynamically adapting model that evolves to match specific user and device patterns, making it resistant to adversarial attacks that exploit static global model vulnerabilities. The model continuously adapts its parameters locally while maintaining the benefits of global pre-training.
2Ease of operation
If a static detection model is used, then deployment simplicity is improved, but the system fails to account for unique user and device behaviors
Solution Approach 1:
The patent segments the endpoint detection system into two distinct components: a globally pre-trained model that provides baseline detection capabilities, and a locally fine-tuned model that adapts to specific device behaviors. This segmentation allows the system to maintain deployment simplicity through the global model while gaining adaptability through the local customization layer.
Solution Approach 2:
The patent performs preliminary action by pre-training the detection model globally before deployment. This pre-training phase establishes a robust baseline model that can be quickly deployed, and subsequent local fine-tuning then adapts it to specific environments without requiring complete retraining.
3Measurement precision
If local fine-tuning is performed on each device, then detection accuracy for individual devices is improved, but computational resources and time are increased
Solution Approach 1:
The patent applies partial action by performing selective fine-tuning only on the local behavior data specific to each device, rather than complete retraining of the entire model. This approach achieves high detection accuracy for individual devices while significantly reducing the computational time and resources required compared to full model retraining.
Data Source
AI summary
An example apparatus includes interface circuitry, machine-readable instructions, and at least one processor circuit to be programmed by the machine-readable instructions to interface circuitry to obtain a pre-trained detection model, machine-readable instructions, and at least one processor circuit to be programmed by the machine-readable instructions to tune the pre-trained detection model based on first local behavior data and execute the tuned detection model to detect an anomaly in second local behavior data associated with the apparatus.