Abstract: Systems, methods, and computer media are described for user risk assessment using similarity analysis. Records of transactions performed by a user while in previous enhanced application access sessions can be evaluated against records of transactions performed by other users in previous sessions. The more similar a user is to other users, the more likely it is the user was acting in a typical manner, and the less likely the user poses a security risk. A similarity analysis can be performed using a bipartite graph linking a group of users and a group of application transactions. By examining an edge between a user and a performed transaction, other edges (and corresponding other users) can be identified that also performed the transaction. A similarity score can be calculated based on the bipartite graph and can be used to determine a risk classification and allow or deny an enhanced application access session request.

Abstract: A system, a method, and a computer program product for generation of keywords for a solution note for resolving an issue associated with a computing component. A dataset for training a keyword data model is received. The dataset includes a plurality of variables associated with one or more values. The keyword data model is configured for determination, as a function of one or more variables in the plurality of variables, of one or more keywords in a plurality of keywords associated with a computing solution in a plurality of computing solutions for resolving a problem with an operation of a computing component in a plurality of computing components. The keyword data model is trained using the received dataset and the keyword data model is applied to one or more variables in the received dataset to generate one or more keywords. One or more keywords associated with the computing solution is generated.

Abstract: Systems, methods, and computer media are described for user risk assessment using similarity analysis. Records of transactions performed by a user while in previous enhanced application access sessions can be evaluated against records of transactions performed by other users in previous sessions. The more similar a user is to other users, the more likely it is the user was acting in a typical manner, and the less likely the user poses a security risk. A similarity analysis can be performed using a bipartite graph linking a group of users and a group of application transactions. By examining an edge between a user and a performed transaction, other edges (and corresponding other users) can be identified that also performed the transaction. A similarity score can be calculated based on the bipartite graph and can be used to determine a risk classification and allow or deny an enhanced application access session request.