Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to improve feature engineering efficiency. An example method disclosed herein includes retrieving a log file in a first file format, the log file containing feature occurrence data, generating a first unit operation based on the first file format to extract the feature occurrence data from the log file to a string, the first unit operation associated with a first metadata tag, generating second unit operations to identify respective features from the feature occurrence data, the second unit operations associated with respective second metadata tags, and generating a first sequence of the first metadata tag and the second metadata tags to create a first vector output file of the feature occurrence data.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to facilitate electronic data security. An example apparatus includes a data storage including a memory adjusted to store data organized according to a data table including columns identifying a first data record and a first security tag associated with the first data record. In the example apparatus, retrieval of data from the data storage involves a bit operation comparing the first security tag with a first privilege tag. In the example apparatus, the data storage provides the first data record when the bit operation comparing the first security tag with the first privilege tag has a non-zero result, and the data storage does not provide the first data record when the bit operation comparing the first security tag with the first privilege tag has a zero result.

Abstract: A malicious object detection system for use in managed runtime environments includes a check circuit to receive call information generated by an application, such as an Android application. A machine learning circuit coupled to the check circuit applies a machine learning model to assess the information and/or data included in the call and detect the presence of a malicious object, such as malware or a virus, in the application generating the call. The machine learning model may include a global machine learning model distributed across a number of devices, a local machine learning model based on use patterns of a particular device, or combinations thereof. A graphical user interface management circuit halts execution of applications containing malicious objects and generates a user perceptible output.

Abstract: Technology for switching from a wireless local area network (WLAN) to a wireless wide area network (WWAN) is disclosed. A multi-radio access technology (multi-RAT) user equipment (UE) can receive WLAN-specific dedicated physical random access channel (PRACH) allocation information from an evolved node B (eNB) to enable the multi-RAT UE to perform an inter-RAT WLAN-to-WWAN handover. The multi-RAT UE can initiate the inter-RAT WLAN-to-WWAN handover at the multi-RAT UE by performing random access with the eNB using the WLAN-specific dedicated PRACH allocation information.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to improve feature engineering efficiency.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to facilitate electronic data security. An example apparatus includes a data storage including a memory adjusted to store data organized according to a data table including columns identifying a first data record and a first security tag associated with the first data record. In the example apparatus, retrieval of data from the data storage involves a bit operation comparing the first security tag with a first privilege tag. In the example apparatus, the data storage provides the first data record when the bit operation comparing the first security tag with the first privilege tag has a non-zero result, and the data storage does not provide the first data record when the bit operation comparing the first security tag with the first privilege tag has a zero result.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to improve feature engineering efficiency. An example method disclosed herein includes retrieving a log file in a first file format, the log file containing feature occurrence data, generating a first unit operation based on the first file format to extract the feature occurrence data from the log file to a string, the first unit operation associated with a first metadata tag, generating second unit operations to identify respective features from the feature occurrence data, the second unit operations associated with respective second metadata tags, and generating a first sequence of the first metadata tag and the second metadata tags to create a first vector output file of the feature occurrence data.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to improve feature engineering efficiency.

Abstract: A malicious object detection system for use in managed runtime environments includes a check circuit to receive call information generated by an application, such as an Android application. A machine learning circuit coupled to the check circuit applies a machine learning model to assess the information and/or data included in the call and detect the presence of a malicious object, such as malware or a virus, in the application generating the call. The machine learning model may include a global machine learning model distributed across a number of devices, a local machine learning model based on use patterns of a particular device, or combinations thereof. A graphical user interface management circuit halts execution of applications containing malicious objects and generates a user perceptible output.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to facilitate electronic data security. An example apparatus includes a data storage including a memory adjusted to store data organized according to a data table including columns identifying a first data record and a first security tag associated with the first data record. In the example apparatus, retrieval of data from the data storage involves a bit operation comparing the first security tag with a first privilege tag. In the example apparatus, the data storage provides the first data record when the bit operation comparing the first security tag with the first privilege tag has a non-zero result, and the data storage does not provide the first data record when the bit operation comparing the first security tag with the first privilege tag has a zero result.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to improve feature engineering efficiency. An example method disclosed herein includes retrieving a log file in a first file format, the log file containing feature occurrence data, generating a first unit operation based on the first file format to extract the feature occurrence data from the log file to a string, the first unit operation associated with a first metadata tag, generating second unit operations to identify respective features from the feature occurrence data, the second unit operations associated with respective second metadata tags, and generating a first sequence of the first metadata tag and the second metadata tags to create a first vector output file of the feature occurrence data.

Abstract: A malicious object detection system for use in managed runtime environments includes a check circuit to receive call information generated by an application, such as an Android application. A machine learning circuit coupled to the check circuit applies a machine learning model to assess the information and/or data included in the call and detect the presence of a malicious object, such as malware or a virus, in the application generating the call. The machine learning model may include a global machine learning model distributed across a number of devices, a local machine learning model based on use patterns of a particular device, or combinations thereof. A graphical user interface management circuit halts execution of applications containing malicious objects and generates a user perceptible output.

Abstract: Systems and methods to support intra-application flow prioritization are disclosed herein. User equipment (UE) may be configured to communicatively couple to an Evolved Universal Terrestrial Radio Access Network (E-UTRAN) Node B (eNB). The eNB may transmit packets from the UE to an evolved packet core (EPC), which may transmit schedule packets to an application function (AF) via a network. The AF may provide classification information and prioritization information for a plurality of intra-application flows transmitted between the AF and the UE. The EPC may classify uplink and/or downlink traffic into the intra-application flows and mark and/or schedule the traffic based on the prioritization information. Absolute and/or modular length, payload values, and/or packet type may be used to classify the traffic into the intra-application flows.

Abstract: Technology for switching from a wireless local area network (WLAN) to a wireless wide area network (WWAN) is disclosed. A multi-radio access technology (multi-RAT) user equipment (UE) can receive WLAN-specific dedicated physical random access channel (PRACH) allocation information from an evolved node B (eNB) to enable the multi-RAT UE to perform an inter-RAT WLAN-to-WWAN handover. The multi-RAT UE can initiate the inter-RAT WLAN-to-WWAN handover at the multi-RAT UE by performing random access with the eNB using the WLAN-specific dedicated PRACH allocation information.

Abstract: A malicious object detection system for use in managed runtime environments includes a check circuit to receive call information generated by an application, such as an Android application. A machine learning circuit coupled to the check circuit applies a machine learning model to assess the information and/or data included in the call and detect the presence of a malicious object, such as malware or a virus, in the application generating the call. The machine learning model may include a global machine learning model distributed across a number of devices, a local machine learning model based on use patterns of a particular device, or combinations thereof. A graphical user interface management circuit halts execution of applications containing malicious objects and generates a user perceptible output.

Abstract: Systems and methods to support intra-application flow prioritization are disclosed herein. User equipment (UE) may be configured to communicatively couple to an Evolved Universal Terrestrial Radio Access Network (E-UTRAN) Node B (eNB). The eNB may transmit packets from the UE to an evolved packet core (EPC), which may transmit schedule packets to an application function (AF) via a network. The AF may provide classification information and prioritization information for a plurality of intra-application flows transmitted between the AF and the UE. The EPC may classify uplink and/or downlink traffic into the intra-application flows and mark and/or schedule the traffic based on the prioritization information. Absolute and/or modular length, payload values, and/or packet type may be used to classify the traffic into the intra-application flows.

Abstract: Technology for switching from a wireless local area network (WLAN) to a wireless wide area network (WWAN) is disclosed. A multi-radio access technology (multi-RAT) user equipment (UE) can receive WLAN-specific dedicated physical random access channel (PRACH) allocation information from an evolved node B (eNB) to enable the multi-RAT UE to perform an inter-RAT WLAN-to-WWAN handover. The multi-RAT UE can initiate the inter-RAT WLAN-to-WWAN handover at the multi-RAT UE by performing random access with the eNB using the WLAN-specific dedicated PRACH allocation information.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to improve feature engineering efficiency.

Abstract: Systems and methods to support intra-application flow prioritization are disclosed herein. User equipment (UE) may be configured to communicatively couple to an Evolved Universal Terrestrial Radio Access Network (E-UTRAN) Node B (eNB). The eNB may transmit packets from the UE to an evolved packet core (EPC), which may transmit schedule packets to an application function (AF) via a network. The AF may provide classification information and prioritization information for a plurality of intra-application flows transmitted between the AF and the UE. The EPC may classify uplink and/or downlink traffic into the intra-application flows and mark and/or schedule the traffic based on the prioritization information. Absolute and/or modular length, payload values, and/or packet type may be used to classify the traffic into the intra-application flows.

Abstract: An adversarial environment classifier training system includes feature extraction circuitry to identify a number of features associated with each sample included in an initial data set that includes a plurality of samples. The system further includes sample allocation circuitry to allocate at least a portion of the samples included in the initial data set to at least a training data set; machine-learning circuitry communicably coupled to the sample allocation circuitry, the machine-learning circuitry to: identify at least one set of compromiseable features for at least a portion of the initial data set; define a classifier loss function [l(xi, yi, w)] that includes: a feature vector (xi) for each sample included in the initial data set; a label (yi) for each sample included in the initial data set; and a weight vector (w) associated with the classifier; and determine the minmax of the classifier loss function (minwmaxi l(xi, yi, w)).