Abstract: Embodiments are disclosed for a method for a security model. The method includes identifying a security threat attack to a security domain by generating a Bloch sphere based on a system information and event management (STEM) of the security domain and a security threat attack framework. The attack is non-linear. Identifying the attack also includes generating a quantum state probabilities matrix (QSPM) based on the Bloch sphere. Further, identifying the attack includes training a security threat model to perform classifications based on the QSPM. Additionally, identifying the attack includes performing a classification of the security domain that identifies a first attack method using the security threat model. Identifying the attack further includes performing a second classification of the security domain that identifies a second attack method. The second attack method follows a non-linear path from the first attack method along the security threat attack framework.

Abstract: Embodiments are disclosed for a method for a security model. The method includes generating a machine learning model that determines probabilities of a plurality of specific techniques, tactics, and procedures (TTPs) for a security domain. The method also includes generating a machine learning model that maps multiple TTPs to a polytope for the security domain. Additionally, the method includes generating a polytope visualization having multiple visualized points in a multi-dimensional space. The visualized points represent corresponding TTPs of a same type and associated probabilities. Further, a disposition of each of the visualized points is based on the determined probabilities and the mapped plurality of TTPs.

Abstract: Embodiments are disclosed for a method for a security model. The method includes generating a Bloch sphere based on a system information and event management (SIEM) of a security domain and a structured threat information expression trusted automated exchange of indicator information. The method also includes generating a quantum state probabilities matrix based on the Bloch sphere. Further, the method includes training a security threat model to perform security threat classifications based on the quantum state probabilities matrix. Additionally, the method includes performing a machine learning classification of the security domain based on the quantum state probabilities matrix.

Abstract: Embodiments are disclosed for a method for a security model. The method includes generating a Bloch sphere based on a system information and event management (SIEM) of a security domain and a structured threat information expression trusted automated exchange of indicator information. The method also includes generating a quantum state probabilities matrix based on the Bloch sphere. Further, the method includes training a security threat model to perform security threat classifications based on the quantum state probabilities matrix. Additionally, the method includes performing a machine learning classification of the security domain based on the quantum state probabilities matrix.