Abstract: Methods and systems are disclosed for using a feature hierarchy with multiple levels with a different number of features at different levels to segment a dataset. The mechanism may use the first level of the hierarchy to segment a dataset into multiple datasets and then generate a timeseries dataset for each segment. Those timeseries datasets may be input into an anomaly detection model to identify a number of anomalies detected within those segments. Based on the number of anomalies not reaching a threshold, a second level of the hierarchy may be used to segment the dataset. Those segments may be input into the anomaly detection model to determine a number of anomalies for the second level. This process may continue until a level of the hierarchy is determined such that the number of anomalies reaches the threshold. The mechanism may then generate a security rule to deal with the anomalies.

Abstract: Methods and systems are described herein for determining auxiliary parameters within datasets, the auxiliary parameters being used to segregate the datasets such that anomaly detection may be performed on the segregated datasets. Based on anomaly detection, alert conditions may then be identified. In particular, a system may, using a machine learning model, determine for a particular target feature (e.g., a parameter being monitored) one or more auxiliary features (other parameters) that effect the values of that parameter and transmit the target feature and the auxiliary features in a message to a monitoring system indicating which features to monitor. The collected data may then be received by the system and transformed into a timeseries dataset, which may then be used to detect anomalies within the data and thereby identify any anomalous points.

Abstract: Methods and systems are disclosed for using a feature hierarchy with multiple levels with a different number of features at different levels to segment a dataset. The mechanism may use the first level of the hierarchy to segment a dataset into multiple datasets and then generate a timeseries dataset for each segment. Those timeseries datasets may be input into an anomaly detection model to identify a number of anomalies detected within those segments. Based on the number of anomalies not reaching a threshold, a second level of the hierarchy may be used to segment the dataset. Those segments may be input into the anomaly detection model to determine a number of anomalies for the second level. This process may continue until a level of the hierarchy is determined such that the number of anomalies reaches the threshold. The mechanism may then generate a security rule to deal with the anomalies.

Abstract: Methods and systems are described herein for determining auxiliary parameters within datasets, the auxiliary parameters being used to segregate the datasets such that anomaly detection may be performed on the segregated datasets. Based on anomaly detection, alert conditions may then be identified. In particular, a system may, using a machine learning model, determine for a particular target feature (e.g., a parameter being monitored) one or more auxiliary features (other parameters) that effect the values of that parameter and transmit the target feature and the auxiliary features in a message to a monitoring system indicating which features to monitor. The collected data may then be received by the system and transformed into a timeseries dataset, which may then be used to detect anomalies within the data and thereby identify any anomalous points.

Abstract: Methods and systems are disclosed for using a feature hierarchy with multiple levels with a different number of features at different levels to segment a dataset. The mechanism may use the first level of the hierarchy to segment a dataset into multiple datasets and then generate a timeseries dataset for each segment. Those timeseries datasets may be input into an anomaly detection model to identify a number of anomalies detected within those segments. Based on the number of anomalies not reaching a threshold, a second level of the hierarchy may be used to segment the dataset. Those segments may be input into the anomaly detection model to determine a number of anomalies for the second level. This process may continue until a level of the hierarchy is determined such that the number of anomalies reaches the threshold. The mechanism may then generate a security rule to deal with the anomalies.

Abstract: Embodiments include receiving one or more packets of a Wi-Fi calling session via a secure tunnel from a user device, where the user device is connected to a source network via a Wi-Fi access point. Embodiments also include determining whether the Wi-Fi calling session is a threat based, at least in part, on identifying an anomaly of at least one packet of the one or more packets. An action can be taken if the Wi-Fi calling communication is determined to be a threat. More specific embodiments include determining the at least one packet is associated with the Wi-Fi calling session by correlating information in the packet with control plane data of the Wi-Fi calling session. Further embodiments can include intercepting the one or more packets in a second secure tunnel established between an evolved packet data gateway and a service provider network associated with the user device.

Abstract: Embodiments include receiving one or more packets of a Wi-Fi calling session via a secure tunnel from a user device, where the user device is connected to a source network via a Wi-Fi access point. Embodiments also include determining whether the Wi-Fi calling session is a threat based, at least in part, on identifying an anomaly of at least one packet of the one or more packets. An action can be taken if the Wi-Fi calling communication is determined to be a threat. More specific embodiments include determining the at least one packet is associated with the Wi-Fi calling session by correlating information in the packet with control plane data of the Wi-Fi calling session. Further embodiments can include intercepting the one or more packets in a second secure tunnel established between an evolved packet data gateway and a service provider network associated with the user device.

Abstract: A gateway is preconfigured to establish an Internet Protocol (IP) tunnel with a default local mobility anchor on behalf of a mobile node. The gateway receives from the mobile node an Internet access request including a mobile identifier and authorization and authentication protocol information, and sends to the default local mobility anchor an IP tunnel request to establish an IP tunnel. The gateway receives from the default local mobility anchor a tunnel redirect message to redirect the IP tunnel from the default local mobility anchor to a serving local mobility anchor and, responsive to the tunnel redirect message, authenticates the mobile node and establishes an IP tunnel with the serving local mobility anchor through which the mobile node communicates.

Abstract: A gateway is preconfigured to establish an Internet Protocol (IP) tunnel with a default local mobility anchor on behalf of a mobile node. The gateway receives from the mobile node an Internet access request including a mobile identifier and authorization and authentication protocol information, and sends to the default local mobility anchor an IP tunnel request to establish an IP tunnel. The gateway receives from the default local mobility anchor a tunnel redirect message to redirect the IP tunnel from the default local mobility anchor to a serving local mobility anchor and, responsive to the tunnel redirect message, authenticates the mobile node and establishes an IP tunnel with the serving local mobility anchor through which the mobile node communicates.

Abstract: Rotary engine has four pistons circling in a toroidal chamber formed inside a casing, successive pairs of cylinders accelerating and decelerating relative to each other. The pistons are carried on piston carriers that are generally figures of revolution. Portions of these piston carriers are joined by bevel gears through which both rotation and accompanying oscillation are enforced upon the piston carriers.

Abstract: This rotary engine has two rotary piston carriers, with each of them carrying two pistons which circle a toroidal chamber. Crankshafts contacting those piston carriers offcenter program them in oppositely phased acceleration and deceleration, with two cycles of this superimposed upon one 360.degree. turn of the piston carriers.Pinion gears are solidly secured to the crankshafts and run in (and mesh with) a fixed ring gear of twice the pinion diameter. That diameter relation precludes running the ordinary shaft through the toroid's central axis. As disclosed here a drum-like assembly is put together which can rotate in that space. Diametrically opposite the offcenter crankshaft is a tie rod. Both it and the crankshaft are secured to the piston carriers snugly against any looseness, and this drum-like assembly rotates continuously at and around the central axis.