A method, computer program product, and computer system for identifying, by a computing device, content of a first social media post from a first user of a simulated social network. Content of a second social media post from a second user of the simulated social network may be identified. It may be determined that the first social media post is a candidate for an engagement event by the second user. The engagement event may be executed by the second user.

Embodiments of the present invention generate network communication policies by applying machine learning to existing network communications, and without using information that labels such communications as healthy or unhealthy. The resulting policies may be used to validate communication between applications (or services) over a network.

Described are systems and methods for evaluating cyber effects in a cyber-physical system (CPS). In some embodiments, a simulation model of the CPS is built and includes an attacked component set and an evaluated component set. A control component is inserted into the simulation model. One or more direct connections between the attacked component set and the evaluated component set are disconnected. One or more indirect connections are identified and then disconnected from the simulation model with disconnected direct connections. The one or more direct connections and indirect connections are routed through the control component. A cyber-attack on the attacked component set can be simulated by configuring the control component to control outputs transmitted via a routed connection, the routed connection being one of the routed direct or indirect connections. The simulated components of the simulation model can be progressively and iteratively replaced by corresponding components from the CPS.

The disclosed embodiments provide improved methods and systems for predicting spectrum allocation in a wireless access network. The method may comprise receiving data associated with an access point (AP), creating, based on the received data and a machine learning algorithm, a decision model for predicting whether to allocate spectrum to the AP, generating a prediction, using the decision model, whether to allocate spectrum to the AP, and sending the prediction to a spectrum access system (SAS) in the wireless access network. The SAS may be configured to allocate spectrum to the AP based on the prediction.

Methods, systems, and apparatus for detecting and mitigating anomalous network traffic. With at least one processor in a network, information regarding network traffic flows is obtained and a classification model is generated based on the obtained information, the classification model comprising one or more classification rules for classifying network traffic as normal or anomalous. With the at least one processor in the network, the network traffic is classified as anomalous or normal based on the generated classification model and at least one mitigation action is initiated based on the network traffic being classified as anomalous.

A method for Over-The-Top (OTT) media service monitoring and testing including deploying multiple test agents throughout the network and orchestrating them together to create a single OTT media service delivery path with coherent measurement points. The method includes selecting and configuring all test agents forming an OTT media service delivery path in a network, wherein the test agents are deployed on devices in the network with pairs of agents in a client-server relationship; for each selected test agent, simulating a Hyper Text Transport Protocol (HTTP) Adaptive Streaming (HAS) media playback using a pair test agent as a HAS server; for each selected test agent, monitoring the HAS media playback via a Quality of Experience (QoE) model to collect metrics, influence factors, and QoE scores; and reporting collected metrics, influence factors and QoE scores, wherein the QoE scores are compared to isolate any performance degradation.

A method (1000) for performance modeling of a plurality of microservices (215) includes deploying the plurality of microservices (215) within a network (1260). The plurality of microservices (215) are communicatively coupled to generate at least one service chain (310) for providing at least one service. Based on a resource allocation configuration, an initial set of training data for the plurality of microservices within the network (1260) is determined. At least a portion of data is excluded from the initial set of training data to generate a subset of training data. A Quality of Service (QoS) behaviour model is generated based on the subset of the training data.

Examples disclosed herein relate to tracking a usage metric for each of a plurality of communication assets, determining whether the usage metric fails to meet a threshold for each of the plurality of communication assets, and in response to determining that the usage metric fails to meet the threshold for at least one of the plurality of communication assets, causing the at least one of the plurality of communication assets to be disabled.

Aspects of the present disclosure relate to allocating RAN resources among RAN slices using a machine learning model. In examples, the machine learning model may determine an optimal RAN resource configuration based on compute power needs. As a result, RAN resource allocation generation and compute power requirements may improve, even in instances with changing or unknown network conditions. In examples, a prediction engine may receive communication parameters and/or requirements associated with service-level agreements (SLAs) for applications executing at least partially at a device in communication with the RAN. The RAN may generate one or more RAN resource configuration for implementation among RAN slices. Upon a change in network conditions or SLA requirements, an optimal RAN configuration may be determined in terms of required compute power.

Systems, methods, apparatuses, and computer program products for a service model management request. The method may include receiving an end-to-end network service model for network services. The method may also includes determining a set of domain services that are required to be able to provide an end-to-end network service defined by the end-to-end network service model. The method may further include checking whether at least one management domain is capable of providing the required set of domain services. The method may also include requesting, when the at least one management domain is not capable of providing the required set of domain services, to prepare the at least one management domain to become able to provide the required set of domain services. In addition, the method may include receiving a notification from the at least one management domain indicating an availability to provide the required set of domain services.