Wireless ML Inference Security with TPM Telemetry Attestation
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Solution Overview
Problem
ML-based algorithms in wireless communication systems are susceptible to adversarial attacks, particularly during inference time, which can disrupt network operations and degrade quality of service and experience due to the black-box nature of these systems, and existing defense mechanisms like adversarial training may compromise model accuracy and are ineffective against real-world noise.
Innovation Solution
Implement a combination of hardware-based attestation using Trusted Platform Modules (TPM) and algorithmic methods, such as statistical anomaly detection, to secure telemetry data and detect adversarial attacks, ensuring integrity and authenticity of ML models in wireless communication systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ML-based algorithms are deployed in wireless communication systems, then network optimization and automation are improved, but susceptibility to adversarial attacks increases
Solution Approach 1:
The patent implements hardware-based attestation using Trusted Platform Modules (TPM) to verify the integrity of ML models before they are deployed for inference. This preliminary verification prevents adversarial models from being executed, thereby maintaining security while allowing legitimate ML-based network optimization to proceed
Solution Approach 2:
The patent introduces an intermediary attestation mechanism that sits between the ML model and the inference engine. This intermediary layer verifies the authenticity of the model through hardware-based attestation and statistical anomaly detection, allowing secure ML deployment without direct exposure to adversarial attacks
2Reliability
If adversarial training is used to defend against attacks, then model robustness is improved, but model accuracy deteriorates
Solution Approach 1:
Instead of pre-training the model with adversarial examples, the patent performs hardware-based attestation and statistical anomaly detection before inference. This approach provides robustness without requiring the model to be trained on noisy adversarial data, thereby preserving accuracy
Solution Approach 2:
The patent replaces the software-based adversarial training mechanism with hardware-based attestation using TPM. This substitution provides robustness through cryptographic verification and statistical detection rather than through model retraining, avoiding the accuracy-robustness tradeoff
3Reliability
If hardware-based attestation is implemented, then security against adversarial attacks is improved, but device complexity increases
Solution Approach 1:
The patent leverages the self-service capabilities of Trusted Platform Modules (TPM) to perform hardware-based attestation. The TPM automatically verifies the integrity of ML models using cryptographic primitives stored in secure hardware, providing security without requiring complex software verification systems
Solution Approach 2:
The patent utilizes the multi-functionality of TPM, which serves both as a secure hardware storage for cryptographic keys and as an attestation mechanism for verifying ML model integrity. This universal component provides security functionality without adding dedicated complex security hardware
4Reliability
If statistical anomaly detection is used, then detection of adversarial attacks is improved, but processing time increases
Solution Approach 1:
The patent implements statistical anomaly detection that checks only critical parameters of the input data and model behavior, rather than performing exhaustive analysis. This partial detection approach identifies adversarial attacks effectively while minimizing additional processing time beyond what is already required for normal inference
Data Source
AI summary
An apparatus may include a trusted execution environment and a processor configured to execute a machine learning (ML)-based application within the trusted execution environment, the ML-based application is configured to provide an output based on input data comprising telemetry data and decrypt encrypted data received by the trusted execution environment to obtain the telemetry data of the network.