Abstract: Systems and methods are provided for detecting link aggregation group (LAG) misconfigurations. Conventional operation of LACP is modified so that a network entity, such as a fabric manager may broadcast LAG configuration information to switches or devices of a fabric or network along with partner LAG configuration information. In this way, practical implementational challenges associated with the use of LACP instances in individual switches or devices and with the use of a fabric manager, can be avoided, where a switch or device in receipt of such partner LAG configuration information can compare that information with the switch's/device's current LAG configuration. Switches or devices need not exchange LACP messages directly, but can still be aware of fabric-wide LAG configurations that may impact their operation.

Abstract: A computer-implemented method for determining an operating instruction for a corrosion monitoring location (CML) comprises the steps of receiving input data describing a current corrosion state of the CML, predicting, by a first artificial intelligence (AI) model, a future corrosion visual state of the CML using at least a first part of the input data, predicting, by a second AI, model, a future corrosion severity state of the CML using at least a second part of the input data, determining, by a third AI, model, a future corrosion type of the CML based at least on the future corrosion visual state of the CML, and determining an operating instruction for the CML based on the future corrosion visual state of the CML, the future corrosion severity state of the CML and the future corrosion type of the CML.

Abstract: Systems and methods are provided for detecting link aggregation group (LAG) misconfigurations. Conventional operation of LACP is modified so that a network entity, such as a fabric manager may broadcast LAG configuration information to switches or devices of a fabric or network along with partner LAG configuration information. In this way, practical implementational challenges associated with the use of LACP instances in individual switches or devices and with the use of a fabric manager, can be avoided, where a switch or device in receipt of such partner LAG configuration information can compare that information with the switch's/device's current LAG configuration. Switches or devices need not exchange LACP messages directly, but can still be aware of fabric-wide LAG configurations that may impact their operation.

Abstract: Some examples relate to classifying IoT malware at a network device. An example includes receiving, by a network device, network traffic from an Internet of Things (IoT) device. Network device may analyze network parameters from the network traffic with a machine learning model. In response to analyzing, network device may classify the network traffic into a category of malware activity. Network device may determine an effectiveness of network traffic classification by measuring a deviation of the network parameters from previously trained network parameters that were used for training the machine learning model. In response to a determination that the deviation of the network parameters from the trained network parameters is more than a pre-defined threshold, network device may generate an alert highlighting the deviation, which allows a user to perform a remedial action pertaining to the IoT device.

Abstract: Some examples relate to classifying IoT malware at a network device. An example includes receiving, by a network device, network traffic from an Internet of Things (IoT) device. Network device may analyze network parameters from the network traffic with a machine learning model. In response to analyzing, network device may classify the network traffic into a category of malware activity. Network device may determine an effectiveness of network traffic classification by measuring a deviation of the network parameters from previously trained network parameters that were used for training the machine learning model. In response to a determination that the deviation of the network parameters from the trained network parameters is more than a pre-defined threshold, network device may generate an alert highlighting the deviation, which allows a user to perform a remedial action pertaining to the IoT device.

Abstract: The present invention provides systems and methods for determining and reporting the cardiac troponin I (cTnI) concentration in a sample from a pregnant woman (in the first, second, or third trimester) by determining whether the sample (cTnI) concentration is higher than a 99th percentile cTnI concentration from a corresponding first, second, or third trimester uncomplicated pregnancy control group, thereby determining if the pregnant woman has or is at risk of developing cardiovascular disease, such as gestational hypertension.