18529714. Method for Protecting an Embedded Machine Learning Model simplified abstract (Robert Bosch GmbH)
Contents
Method for Protecting an Embedded Machine Learning Model
Organization Name
Inventor(s)
Benjamin Hettwer of Stuttgart (DE)
Christoph Schorn of Benningen Am Neckar (DE)
Method for Protecting an Embedded Machine Learning Model - A simplified explanation of the abstract
This abstract first appeared for US patent application 18529714 titled 'Method for Protecting an Embedded Machine Learning Model
The patent application describes a method for protecting an embedded machine learning model from physical attacks by monitoring intermediate results from the model and detecting any potential attacks based on the evaluation of these results.
- The method involves ascertaining a monitoring input based on intermediate results from the machine learning model.
- The monitoring input is then evaluated by a monitoring system to detect any physical attacks.
- By continuously monitoring the model's performance and detecting anomalies, the system can effectively protect the model from potential attacks.
Potential Applications: - This technology can be applied in various industries where embedded machine learning models are used, such as autonomous vehicles, smart devices, and industrial automation. - It can also be utilized in cybersecurity systems to protect sensitive data and networks from physical attacks on machine learning models.
Problems Solved: - Protecting embedded machine learning models from physical attacks. - Ensuring the integrity and security of machine learning systems in real-world applications.
Benefits: - Enhanced security and reliability of embedded machine learning models. - Prevention of potential physical attacks on machine learning systems. - Increased trust and confidence in the performance of machine learning technologies.
Commercial Applications: - Cybersecurity companies can integrate this technology into their products to offer enhanced protection against physical attacks on machine learning models. - Manufacturers of autonomous vehicles and smart devices can use this method to safeguard their products from potential security breaches.
Questions about the technology: 1. How does this method differ from traditional cybersecurity measures in protecting machine learning models? 2. What are the potential limitations or challenges in implementing this technology in real-world applications?
Original Abstract Submitted
A method for protecting an embedded machine learning model from at least one physical attack includes (i) ascertaining a monitoring input, wherein the monitoring input is based on at least one intermediate result from the machine learning model, (ii) evaluating the ascertained monitoring input by way of a monitoring system, and (iii) detecting the at least one physical attack on the basis of the evaluation.
