New Framework Released to Protect Machine Learning Systems From Adversarial Attacks

guest · Oct 24, 2020

New Framework Released to Protect Machine Learning Systems From Adversarial Attacks
October 23, 2020
https://thehackernews.com/2020/10/adversarial-ml-threat-matrix.html

Microsoft, in collaboration with MITRE, IBM, NVIDIA, and Bosch, has released a new open framework that aims to help security analysts detect, respond to, and remediate adversarial attacks against machine learning (ML) systems.

Called the Adversarial ML Threat Matrix, the initiative is an attempt to organize the different techniques employed by malicious adversaries in subverting ML systems.
What's more, researchers have studied what's called model-inversion attacks, wherein access to a model is abused to infer information about the training data.

According to a Gartner report cited by Microsoft, 30% of all AI cyberattacks by 2022 are expected to leverage training-data poisoning, model theft, or adversarial samples to attack machine learning-powered systems.

"Despite these compelling reasons to secure ML systems, Microsoft's survey spanning 28 businesses found that most industry practitioners have yet to come to terms with adversarial machine learning," the Windows maker said. "Twenty-five out of the 28 businesses indicated that they don't have the right tools in place to secure their ML systems."
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Adversarial ML Threat Matrix hopes to address threats against data weaponization of data with a curated set of vulnerabilities and adversary behaviors that Microsoft and MITRE vetted to be effective against ML systems.

"The goal of the Adversarial ML Threat Matrix is to position attacks on ML systems in a framework that security analysts can orient themselves in these new and upcoming threats," Microsoft said.
"The matrix is structured like the ATT&CK framework, owing to its wide adoption among the security analyst community – this way, security analysts do not have to learn a new or different framework to learn about threats to ML systems."
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Microsoft: Cyberattacks against machine learning systems are more common than you think

Machine learning (ML) is making incredible transformations in critical areas such as finance, healthcare, and defense, impacting nearly every aspect of our lives. Many businesses, eager to capitalize on advancements in ML, have not scrutinized the security of their ML systems.

Today, along with MITRE, and contributions from 11 organizations including IBM, NVIDIA, Bosch, Microsoft is releasing the Adversarial ML Threat Matrix, an industry-focused open framework, to empower security analysts to detect, respond to, and remediate threats against ML systems.
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New Framework Released to Protect Machine Learning Systems From Adversarial Attacks

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