Threats and Difficulties During Training and Inference in Machine Learning-Based Systems

Dr Anumaan shah


The exponential rise in the interdependence of the cyber and physical worlds generates a massive volume of data that must be efficiently handled and stored. As a result, computing paradigms are shifting toward machine learning (ML)-based systems due to their capacity to analyze massive amounts of data rapidly and reliably. Although ML-based solutions solve the efficient computing needs of big data, they introduce security risks into the systems that traditional monitoring-based security measures cannot address. As a result, this article begins with a brief introduction of major security concerns in machine learning, as well as their corresponding threat models.

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