Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform certain acts. The acts can include receiving a deployment model selection of a software-defined-network (SDN) control service. The deployment model selection includes one of a centralized model, a decentralized model, a distributed model, or a hybrid model. The acts also can include deploying the SDN control service in the deployment model selection to control a physical computer network. The SDN control service uses a routing agent model trained using a reinforcement-learning model. Other embodiments are described.

Abstract: Techniques and architectures include representing data with one or more n-dimensional representations and using one or more analysis models to identify target properties associated with the one or more n-dimensional representations. For example, data can be represented as a plurality of points in a coordinate system. A set of points in the plurality of points can be identified and an n-dimensional model can be generated for the set of points. The n-dimensional model can be compared to a plurality of n-dimensional models that are tagged as including a target property associated with malicious behavior, benign behavior, and/or a vulnerability. Based on the comparison, a likelihood can be determined that the data includes the target property.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform certain acts. The acts can include receiving a deployment model selection of a software-defined-network (SDN) control service. The deployment model selection includes one of a centralized model, a decentralized model, a distributed model, or a hybrid model. The acts also can include deploying the SDN control service in the deployment model selection to control a physical computer network. The SDN control service uses a routing agent model trained using a reinforcement-learning model. Other embodiments are described.