Abstract: Systems and methods for monitoring a network slice are provided. A method, according to one implementation, include extracting information from network traffic received from one or more User Plane Function (UPF) components of a network slice; examining the extracted information using Machine Learning (ML), and, in response to detecting of one or more malicious threats based on the examined extracted information by the ML, causing one or more actions to isolate the network traffic to protect at least the network slice from the one or more malicious threats.

Abstract: Systems and methods include receiving inputs comprising a Raman gain target for a Raman amplifier, fiber parameters, and fiber characteristics, each for an optical span; analyzing the inputs with a deep neural network (DNN) having a plurality of layers; providing a plurality of outputs from the DNN; and utilizing the plurality of outputs for a link budget calculation on the optical span. The Raman amplifier includes a plurality of pumps each at a fixed wavelength.

Abstract: Systems and methods are provided for placement of User Plane Functions (UPFs) on one or more nodes and assigning Distributed Units (DUs) to the UPF-hosting nodes of a 5G network slice. A method, according to one implementation, includes the step of obtaining a network topology map portraying a network that includes at least a plurality of components of a Radio Access Network (RAN) and a plurality of eligible nodes capable of connecting the components to the Internet. The method also includes the step of creating a tree graph from the network topology map. The tree graph includes a plurality of branches, where each branch represents the lowest cost path between a respective eligible node and a selected one of the plurality of components. In addition, the method includes selecting a group of the eligible nodes that collectively are capable of connecting the plurality of components to the Internet.

Abstract: Systems and methods for detecting intrusions, attacks, and sub-attacks launched against a network under observations are provided. A method, according to one implementation, includes obtaining network traffic information regarding data traffic in a network under observation and obtaining system log information regarding operations of the network under observation. The method further includes the step of inserting the network traffic information and system log information into one or more analysis procedures, where each analysis procedure is configured to detect a respective sub-attack of a multi-stage attack to which the network under observation is susceptible. Also, the method includes the step of combining the outputs of the one or more analysis procedures to detect whether one or more sub-attacks have been launched against the network under observation.

Abstract: Systems and methods for embedding concealed meta-data into DNNs. Specifically, the system and method presented consists of receiving a trained neural network that includes one or more layers each having weights. The disclosed process includes transforming at least one layer to a transformed domain, adding information to the layer(s) in the transformed domain, and performing an inverse domain transform on at least one layer such that the layer(s) has new weights with an embedded watermark.

Abstract: Systems and methods include monitoring packets, by a network edge device, from one or more endpoint devices where the packets are destined for corresponding application services in a network; classifying the one or more endpoint devices based on the monitoring into a corresponding trust level of a plurality of trust levels; and, responsive to a first endpoint device of the one or more endpoint devices being untrusted, steering the packets from the first endpoint device into a restricted zone.

Abstract: The present disclosure relates to systems and methods for embedding concealed meta-data into DNNs. Specifically, the system and method presented consists of receiving a trained neural network that includes one or more layers each having weights. The disclosed process includes transforming at least one layer to a transformed domain, adding information to the layer(s) in the transformed domain, and performing an inverse domain transform on at least one layer such that the layer(s) has new weights with an embedded watermark.

Abstract: Systems, methods, and computer-readable medium for forecasting a time-series are provided. In one implementation, a method is configured to include a step of providing a time-series to a neural network including one or more branches for processing one or more portions of the time-series. In each of the one or more branches, the method includes separating the respective portion of the time-series into individual portions and applying each portion to a respective sub-branch of a plurality of sub-branches of the one or more branches. The method also includes generating forecasting coefficients for each output time point in each of the respective sub-branches and providing a forecast of the time-series based at least on the forecasting coefficients.

Abstract: A method embodiment includes implementing, by a base station (BS), a grant-free uplink transmission scheme. The grant-free uplink transmission scheme defines a first contention transmission unit (CTU) access region in a time-frequency domain, defines a plurality of CTUs, defines a default CTU mapping scheme by mapping at least some of the plurality of CTUs to the first CTU access region, and defines a default user equipment (UE) mapping scheme by defining rules for mapping a plurality of UEs to the plurality of CTUs.

Abstract: The present disclosure relates to systems and methods for embedding concealed meta-data into DNNs. Specifically, the system and method presented consists of receiving a trained neural network that includes one or more layers each having weights. The disclosed process includes transforming at least one layer to a transformed domain, adding information to the layer(s) in the transformed domain, and performing an inverse domain transform on at least one layer such that the layer(s) has new weights with an embedded watermark.

Abstract: Systems and methods for receiving a time-series that includes a historical or current observation and determining future points of the time-series utilizing a forecasting deep neural network (DNN) to analyze the time-series and determining an uncertainty of the future points utilizing an uncertainty DNN to analyze the time-series and future points. The output would include providing the future points of the time-series and the uncertainty data. The steps further include training the forecasting DNN with historical data and training the uncertainty DNN with the trained forecasting DNN utilizing a residual of an estimate from the forecasting DNN and actual data.

Abstract: Systems and methods are provided for utilizing polarization parameters obtained from an optical network to determine vibrations in optical fibers using coherent optics equipment and machine learning techniques. A method, according to one implementation, includes the step of obtaining a time-series dataset that includes measurements of polarization characteristics of light traversing an optical fiber of an optical network. The method also includes the step of detecting vibration characteristics of the optical fiber based on the time-series dataset. In some implementations, the time-series dataset may be a multi-variate dataset and the polarization characteristics may be related to transients in a State of Polarization (SOP). The SOP, for example, may be represented by an amplitude and a phase of an electric field vector and may be defined as having one of a linear polarization, elliptical polarization, and circular polarization.

Abstract: Systems and methods include receiving network data from a network; estimating large flows from the network data utilizing a first statistical approach; responsive to the network being large flow dominant, recursively estimating flows in the network utilizing the first statistical approach until an exit condition is reached; and combining the recursively estimated flows and forming a traffic matrix based thereon. The recursively estimating flows can include estimating a set of large flows utilizing the first statistical approach and freezing a resulting estimate while leaving smaller flows unsolved; repeating the estimating for a next set of large flows from the unsolved smaller flows; and continuing the repeating until the exit condition is reached.

Abstract: Deep Neural Networks (DNNs) for forecasting future data are provided. In one embodiment, a non-transitory computer-readable medium is configured to store computer logic having instructions that, when executed, cause one or more processing devices to receive, at each of a plurality of Deep Neural Network (DNN) forecasters, an input corresponding to a time-series dataset of a plurality of input time-series datasets. The instructions further cause the one or more processing devices to produce, from each of the plurality of DNN forecasters, a forecast output and provide the forecast output from each of the plurality of DNN forecasters to a DNN mixer for combining the forecast outputs to produce one or more output time-series datasets.

Abstract: Systems and methods include receiving network topology information of a network including a plurality of routers; receiving link measurements defining bandwidth on links in the network; determining routes in the network based on the network topology information; and utilizing the routes and the link measurements to determine an estimate of an initial traffic matrix that includes the bandwidth between origin routers and destination routers.

Abstract: Systems and methods for detecting patterns in data from a time-series are provided. In one implementation, a method for pattern detection includes obtaining data in a time-series and creating one-dimensional or multi-dimensional windows from the time-series data. The one-dimensional or multi-dimensional windows are created either independently or jointly with the time-series. The method also includes training a deep neural network with the one-dimensional or multi-dimensional windows utilizing historical and/or simulated data to provide a neural network model. Also, the method includes processing ongoing data with the neural network model to detect one or more patterns of a particular category in the ongoing data, and localizing the one or more patterns in time.

Abstract: An optical network element includes a connection to an optical fiber in an optical line system including a coherent receiver; a microphone configured to detect sound; and circuitry connected to the microphone and configured to cause transmission of information related to sounds detected by the microphone to a receiver at an end of the optical line system, wherein the transmission is over the optical fiber in the optical line system to the coherent receiver. The optical network element can include a polarization controlling device connected to the circuitry and configured to modulate a state-of-polarization (SOP) envelope for the transmission.

Abstract: Systems and methods for compressing network data are provided. According to one implementation, a method includes the step of collecting raw telemetry data from a network environment. The raw telemetry data is collected as time-series datasets. The method also includes the step of compressing the time-series datasets by deploying the time-series datasets as a Deep Neural Network (DNN) in the network environment itself. The time-series datasets are configured to be substantially reconstructed from the DNN using predictive functionality of the DNN.

Abstract: Systems and methods include receiving network topology information of a network including a plurality of routers; receiving link measurements defining bandwidth on links in the network; determining routes in the network based on the network topology information; and utilizing the routes and the link measurements to determine an estimate of an initial traffic matrix that includes the bandwidth between origin routers and destination routers.

Abstract: Systems and methods for forecasting time series data are provided. In one implementation, a method includes the steps of obtaining time series data from a network. The method also comprises the step of determining one or more forecasters to be used based on a type of the time series data and based on previous training that determine that the one or more forecasters from a number of forecasters are best suited for the type of time series data. The method further comprises making a forecast of the time series data using the one or more forecasters and to save and/or display the forecast.