A computing platform may train, using historical node performance information and historical application parameter information, a node selection model, which may configure the model to select nodes for application cloud deployment. The computing platform may receive a request to deploy an application to a cloud network. The computing platform may select a node, of the plurality of nodes of the cloud network, to which the application should be deployed. The computing platform may queue, along with other applications scheduled for deployment to the plurality of nodes, the application for deployment to the node. After identifying that the application is first in the queue, the computing platform may deploy the application to the node of the cloud network, which may create, at the node, a container corresponding to the application.

In a communication network, a device is configured to predict attacks and detect attacks from data logs received from the network and generate a response to an attack upon prediction or detection of an attack. Graph representations of data logs are generated based on a predefined schema. Attacks are detected by applying inference rules to a graph representation of the data logs. Attacks are predicted by using a graph neural network trained with subgraphs obtained by querying a graph representation of training data corresponding to normal traffic and attacks.

Systems and methods are provided for mitigating undesirable service disruptions in a communications network. Based on a determination that an access failure rate exceeds a threshold, it may be determined that a particular service is degraded or that a requesting user device is not authorized to access the service. One or more mitigation protocols may be used to block subsequent attempts by the requesting user device, a set of user devices associated with the requesting user device, or an area associated with the requesting user device to request access to the service.