Abstract: A machine learning model protection method comprising: generating, based on a set of parameters that define a machine learning model, an item of software which, when executed by one or more processors, provides an implementation for the machine learning model; and applying one or more software protection techniques to the item of software.

Abstract: Methods and systems related to increasing the cryptographic security of keys used by software with cryptographic functions. This is done by increasing the mathematical complexity of the software. The components and functions used by the software are first determined and, using these components, functions, and the data exchanged between them, the software is made more resistant to analysis. The methods used in increasing analytical resistance are grouped into 3 general types: adjusting the information exchanged between the components, replacing some components with different but related components, and adjusting the data flow between the components.