Emc IP Holding Company LLC

Dany Shapiro

Shiri Gaber

Ohad Arnon

Techniques described herein relate to a method for predicting results using ensemble models. The method may include receiving trained model data sets from a model source nodes, each trained model data set comprising a trained model, an important feature list, and a missing feature generator; receiving a prediction request data set; making a determination that the prediction request data set does not include an input feature for a trained model; generating, based on the determination and using a missing feature generator, a substitute feature to replace the input feature; executing the trained model using the prediction request data set and the substitute feature to obtain a first prediction; executing a second trained model using the prediction request data set to obtain a second prediction; and obtaining a final prediction using the first prediction, the second prediction, and an ensemble model.

Privacy preserving ensemble learning as a service

One example method includes data protection operations including cyber security operations, threat detection operations, and other security operations. Normal device behavior is learned based on data collected by an anomaly detection engine operating in a kernel. The normal data is used to train a machine learning model. Threats are detected when the machine learning model indicates that new data points deviate from normal device behavior. Associated processes are stopped. This allows threats to be detected based on normal behavior rather than on unknown threat behavior.

Cyber immunity system as a biological self-recognition model on operating systems

One example method includes collecting, in a closed network, raw network traffic from one or more devices in the closed network, extracting metadata from the raw network traffic, processing the metadata, analyzing the metadata after the metadata has been processed, and based on the analyzing, determining whether or not an actual attack or attack threat is present in the closed network. If an attack or threat of attack is determined to exist, one or more remedial actions may then be taken.

Ai/ml approach for DDOS prevention on 5G CBRS networks

Techniques are provided for machine learning-based anomaly detection in a monitored location. One method comprises obtaining data from multiple data sources associated with a monitored location for storage into a data repository; processing the data to generate substantially continuous time-series data for multiple distinct features within the data; applying the substantially continuous time-series data for the distinct features to a machine learning baseline behavioral model to obtain a probability distribution representing a behavior of the monitored location over time; and evaluating a probability score generated by the machine learning baseline behavioral model to identify an anomaly at the monitored location. The machine learning baseline behavioral model is trained, for example, to identify anomalies in correlations between the plurality of distinct features at each timestamp. A presence verification is optionally provided based on a deviation from the machine learning baseline behavioral model at the monitored location.

Machine learning-based anomaly detection for human presence verification

Systems and methods are provided for sharing context of a mobile device among base station nodes for mobility management in mobile communications networks. An active connection is established between a first base station node and a mobile device, within radio access network of a mobile communications network. Other base station nodes in the radio access network within a tracking area of the mobile device are identified, and a state propagation process is performed to share a state of the active connection between the first base station node and the mobile device with at least a second base station node determined to be within the tracking area of the mobile device. The shared state is utilized to enable the mobile device to seamlessly establish an active connection with the second base station node and communicate with the mobile communications network through the active connection with the second base station node.

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Alfi Menashe, Israel

Dany Shapiro