Abstract: Balancing the observed signals used to train network intrusion detection models allows for a more accurate allocation of computing resources to defend the network from malicious parties. The models are trained against live data defined within a rolling window and historic data to detect user-defined features in the data. Automated attacks ensure that various kinds of attacks are always present in the rolling training window. The set of models are constantly trained to determine which model to place into production, to alert analysts of intrusions, and/or to automatically deploy countermeasures. The models are continually updated as the features are redefined and as the data in the rolling window changes, and the content of the rolling window is balanced to provide sufficient data of each observed type by which to train the models. When balancing the dataset, low-population signals are overlaid onto high-population signals to balance their relative numbers.

Abstract: Detecting compromised devices and user accounts within an online service via multi-signal analysis allows for fewer false positives and thus a more accurate allocation of computing resources and human analyst resources. Individual scopes of analysis, related to devices, accounts, or processes are specified and multiple behaviors over a period of time are analyzed to detect persistent (and slow acting) threats as well as brute force (and fast acting) threats. Analysts are alerted to individually affected scopes suspected of being compromised and may address them accordingly.

Abstract: Detecting emergent abnormal behavior in a computer network faster and more accurately allows for the security of the network against malicious parties to be improved. To detect abnormal behavior, outbound traffic is examined from across several devices and processes in the network to identify rarely communicated-with destinations that are associated with rarely-executed processes. As a given destination and process is used more frequently over time by the network, the level of suspicion associated with that destination and process is lowered as large groups of devices are expected to behave the same when operating properly and not under the control of a malicious party. Analysts are alerted in near real-time to the destinations associated with the activities deemed most suspicious.

Abstract: A set of candidate malicious activity identification models are trained and evaluated against a production malicious activity identification model to identify a best performing model. If the best performing model is one of the candidate models, then an alert threshold is dynamically set for the best performing model, for each of a plurality of different urgency levels. A reset threshold, for each urgency level, is also dynamically set for the best performing model.

Abstract: Detecting compromised devices and user accounts within an online service via multi-signal analysis allows for fewer false positives and thus a more accurate allocation of computing resources and human analyst resources. Individual scopes of analysis, related to devices, accounts, or processes are specified and multiple behaviors over a period of time are analyzed to detect persistent (and slow acting) threats as well as brute force (and fast acting) threats. Analysts are alerted to individually affected scopes suspected of being compromised and may address them accordingly.

Abstract: Detecting compromised devices and user accounts within an online service via multi-signal analysis allows for fewer false positives and thus a more accurate allocation of computing resources and human analyst resources. Individual scopes of analysis, related to devices, accounts, or processes are specified and multiple behaviors over a period of time are analyzed to detect persistent (and slow acting) threats as well as brute force (and fast acting) threats. Analysts are alerted to individually affected scopes suspected of being compromised and may address them accordingly.

Abstract: Balancing the observed signals used to train network intrusion detection models allows for a more accurate allocation of computing resources to defend the network from malicious parties. The models are trained against live data defined within a rolling window and historic data to detect user-defined features in the data. Automated attacks ensure that various kinds of attacks are always present in the rolling training window. The set of models are constantly trained to determine which model to place into production, to alert analysts of intrusions, and/or to automatically deploy countermeasures. The models are continually updated as the features are redefined and as the data in the rolling window changes, and the content of the rolling window is balanced to provide sufficient data of each observed type by which to train the models. When balancing the dataset, low-population signals are overlaid onto high-population signals to balance their relative numbers.

Abstract: Balancing the observed signals used to train network intrusion detection models allows for a more accurate allocation of computing resources to defend the network from malicious parties. The models are trained against live data defined within a rolling window and historic data to detect user-defined features in the data. Automated attacks ensure that various kinds of attacks are always present in the rolling training window. The set of models are constantly trained to determine which model to place into production, to alert analysts of intrusions, and/or to automatically deploy countermeasures. The models are continually updated as the features are redefined and as the data in the rolling window changes, and the content of the rolling window is balanced to provide sufficient data of each observed type by which to train the models. When balancing the dataset, low-population signals are overlaid onto high-population signals to balance their relative numbers.

Abstract: A set of candidate malicious activity identification models are trained and evaluated against a production malicious activity identification model to identify a best performing model. If the best performing model is one of the candidate models, then an alert threshold is dynamically set for the best performing model, for each of a plurality of different urgency levels. A reset threshold, for each urgency level, is also dynamically set for the best performing model.

Abstract: Detecting compromised devices and user accounts within an online service via multi-signal analysis allows for fewer false positives and thus a more accurate allocation of computing resources and human analyst resources. Individual scopes of analysis, related to devices, accounts, or processes are specified and multiple behaviors over a period of time are analyzed to detect persistent (and slow acting) threats as well as brute force (and fast acting) threats. Analysts are alerted to individually affected scopes suspected of being compromised and may address them accordingly.

Abstract: Detecting emergent abnormal behavior in a computer network faster and more accurately allows for the security of the network against malicious parties to be improved. To detect abnormal behavior, outbound traffic is examined from across several devices and processes in the network to identify rarely communicated-with destinations that are associated with rarely-executed processes. As a given destination and process is used more frequently over time by the network, the level of suspicion associated with that destination and process is lowered as large groups of devices are expected to behave the same when operating properly and not under the control of a malicious party. Analysts are alerted in near real-time to the destinations associated with the activities deemed most suspicious.

Abstract: Balancing the observed signals used to train network intrusion detection models allows for a more accurate allocation of computing resources to defend the network from malicious parties. The models are trained against live data defined within a rolling window and historic data to detect user-defined features in the data. Automated attacks ensure that various kinds of attacks are always present in the rolling training window. The set of models are constantly trained to determine which model to place into production, to alert analysts of intrusions, and/or to automatically deploy countermeasures. The models are continually updated as the features are redefined and as the data in the rolling window changes, and the content of the rolling window is balanced to provide sufficient data of each observed type by which to train the models. When balancing the dataset, low-population signals are overlaid onto high-population signals to balance their relative numbers.

Abstract: A system or method for generating gestures in a robot during generation of a speech output by the robot by analyzing a speech text and selecting appropriate gestures from a plurality of candidate gestures. The speech text is analyzed and tagged with information relevant to generating of the gestures. Based on the speech text, the tagged information and other relevant information, a gesture identifier is selected. A gesture template corresponding to the gesture identifier is retrieved and then processed by adding relevant parameter to generate a gesture descriptor representing a gesture to be taken by the robot. A gesture motion is planned based on the gesture descriptor and analysis of timing associated with the speech, wherein the amplitude, frequency or speed of the selected gesture is modified based on random numbers of specific range depending on the status of the robot. Actuator signals for controlling the actuators such as arms and hands are generated based on the planned gesture motion.

Abstract: A system or method for generating gestures in a robot during generation of a speech output by the robot by analyzing a speech text and selecting appropriate gestures from a plurality of candidate gestures. The speech text is analyzed and tagged with information relevant to generating of the gestures. Based on the speech text, the tagged information and other relevant information, a gesture identifier is selected. A gesture template corresponding to the gesture identifier is retrieved and then processed by adding relevant parameter to generate a gesture descriptor representing a gesture to be taken by the robot. A gesture motion is planned based on the gesture descriptor and analysis of timing associated with the speech. Actuator signals for controlling the actuators such as arms and hands are generated based on the planned gesture motion.