Security News > 2021 > January > The fight to stymie adversarial machine learning is on

Adversarial machine learning is a technique aimed at deceiving the ML model by providing specially crafted input to fool the AV into classifying the malicious input as a benign file and evade detection.

There is great impetus to expand the knowledge that we have not just on the machine learning models that we use, but the adversarial attacks made against them.

As one of the leading cybersecurity companies applying deep learning to cybersecurity, Deep Instinct continues to play a significant role in advancing adversarial machine learning research.

Deep Instinct's deep learning PhD experts have shared their knowledge of adversarial attacks towards the development of the machine learning threat matrix, a project that was led by Microsoft and builds on the widely used MITRE AT&ACK framework.

The Adversarial Machine Learning Threat Matrix aims to equip security analysts with the knowledge that they need to combat this adversarial frontier.

Just like the widely used MITRE ATT&CK matrix maps methods commonly used by hackers to subvert software, the adversarial machine learning threat matrix maps techniques used by adversaries to subvert machine learning models.

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