Abstract: In one embodiment, a device in a network detects an encrypted traffic flow associated with a client in the network. The device captures contextual traffic data regarding the encrypted traffic flow from one or more unencrypted packets associated with the client. The device performs a classification of the encrypted traffic flow by using the contextual traffic data as input to a machine learning-based classifier. The device generates an alert based on the classification of the encrypted traffic flow.

Abstract: An audio encoder for encoding an audio signal has: a first encoding processor for encoding a first audio signal portion in a frequency domain, having: a time frequency converter for converting the first audio signal portion into a frequency domain representation; an analyzer for analyzing the frequency domain representation to determine first spectral portions to be encoded with a first spectral resolution and second regions to be encoded with a second resolution; and a spectral encoder for encoding the first spectral portions with the first spectral resolution and encoding the second portions with the second resolution; a second encoding processor for encoding a second different audio signal portion in the time domain; a controller for analyzing and determining, which portion of the audio signal is the first audio signal portion encoded in the frequency domain and which portion is the second audio signal portion encoded in the time domain; and an encoded signal former for forming an encoded audio signal havi

Abstract: An audio encoder for encoding an audio signal includes: a first encoding processor for encoding a first audio signal portion in a frequency domain, wherein the first encoding processor includes: a time frequency converter for converting the first audio signal portion into a frequency domain representation having spectral lines up to a maximum frequency of the first audio signal portion; a spectral encoder for encoding the frequency domain representation; a second encoding processor for encoding a second different audio signal portion in the time domain; a cross-processor for calculating, from the encoded spectral representation of the first audio signal portion, initialization data of the second encoding processor, so that the second encoding processing is initialized to encode the second audio signal portion immediately following the first audio signal portion in time in the audio signal; a controller configured for analyzing the audio signal and for determining, which portion of the audio signal is the firs

Abstract: In one embodiment, a device in a network receives certificate data for an encrypted traffic flow associated with a client node in the network. The device determines one or more data features from the certificate data. The device determines one or more flow characteristics of the encrypted traffic flow. The device performs a classification of an application executed by the client node and associated with the encrypted traffic flow by using a machine learning-based classifier to assess the one or more data features from the certificate data and the one or more flow characteristics of the traffic flow. The device causes performance of a network action based on a result of the classification of the application.

Abstract: In one embodiment, a service receives traffic telemetry data regarding encrypted traffic sent by an endpoint device in a network. The service analyzes the traffic telemetry data to infer characteristics of an application on the endpoint device that generated the encrypted traffic. The service receives, from a monitoring agent on the endpoint device, application telemetry data regarding the application. The service determines that the application is evasive malware based on the characteristics of the application inferred from the traffic telemetry data and on the application telemetry data received from the monitoring agent on the endpoint device. The service initiates performance of a mitigation action in the network, after determining that the application on the endpoint device is evasive malware.

Abstract: In one embodiment, a service receives traffic telemetry data regarding encrypted traffic sent by an endpoint device in a network. The service analyzes the traffic telemetry data to infer characteristics of an application on the endpoint device that generated the encrypted traffic. The service receives, from a monitoring agent on the endpoint device, application telemetry data regarding the application. The service determines that the application is evasive malware based on the characteristics of the application inferred from the traffic telemetry data and on the application telemetry data received from the monitoring agent on the endpoint device. The service initiates performance of a mitigation action in the network, after determining that the application on the endpoint device is evasive malware.

Abstract: In one embodiment, a device in a network detects an encrypted traffic flow associated with a client in the network. The device captures contextual traffic data regarding the encrypted traffic flow from one or more unencrypted packets associated with the client. The device performs a classification of the encrypted traffic flow by using the contextual traffic data as input to a machine learning-based classifier. The device generates an alert based on the classification of the encrypted traffic flow.

Abstract: In one embodiment, a device in a network receives certificate data for an encrypted traffic flow associated with a client node in the network. The device determines one or more data features from the certificate data. The device determines one or more flow characteristics of the encrypted traffic flow. The device performs a classification of an application executed by the client node and associated with the encrypted traffic flow by using a machine learning-based classifier to assess the one or more data features from the certificate data and the one or more flow characteristics of the traffic flow. The device causes performance of a network action based on a result of the classification of the application.

Abstract: In one embodiment, a device in a network receives certificate data for an encrypted traffic flow associated with a client node in the network. The device determines one or more data features from the certificate data. The device determines one or more flow characteristics of the encrypted traffic flow. The device performs a classification of an application executed by the client node and associated with the encrypted traffic flow by using a machine learning-based classifier to assess the one or more data features from the certificate data and the one or more flow characteristics of the traffic flow. The device causes performance of a network action based on a result of the classification of the application.

Abstract: In one embodiment, a device in a network obtains log data regarding replication of files stored on an endpoint client to a file replication service. The device tracks, based on the obtained logs, encryption changes to the files that convert the files from unencrypted files to encrypted files. The device determines that the tracked encryption changes to the files are indicative of a ransomware infection on the endpoint client. The device initiates a mitigation action regarding the ransomware infection.

Abstract: In one embodiment, a security device in a computer network detects potential domain generation algorithm (DGA) searching activity using a domain name service (DNS) model to detect abnormally high DNS requests made by a host attempting to locate a command and control (C&C) server in the computer network. The server device also detects potential DGA communications activity based on applying a hostname-based classifier for DGA domains associated with any server internet protocol (IP) address in a data stream from the host. The security device may then correlate the potential DGA searching activity with the potential DGA communications activity, and identifies DGA performing malware based on the correlating, accordingly.

Abstract: In one embodiment, a device in a network receives traffic information regarding one or more secure sessions in the network. The device associates the one or more secure sessions with corresponding certificate validation check traffic indicated by the received traffic information. The device makes a self-signed certificate determination for an endpoint domain of a particular secure session based on whether the particular secure session is associated with certificate validation check traffic. The device causes the self-signed certificate determination for the endpoint domain to be used as input to a malware detector.

Abstract: In one embodiment, a device in a network detects an encrypted traffic flow associated with a client in the network. The device captures contextual traffic data regarding the encrypted traffic flow from one or more unencrypted packets associated with the client. The device performs a classification of the encrypted traffic flow by using the contextual traffic data as input to a machine learning-based classifier. The device generates an alert based on the classification of the encrypted traffic flow.

Abstract: In one embodiment, a device obtains simulation environment data regarding traffic generated within a simulation environment in which malware is executed. The device trains a malware detector using the simulation environment data. The device obtains deployment environment characteristics of a network to which the malware detector is to be deployed. The device configures the malware detector to ignore data in the simulation environment data that is associated with one or more environment characteristics that are not present in the deployment environment characteristics.

Abstract: In one embodiment, a service receives traffic telemetry data regarding encrypted traffic sent by an endpoint device in a network. The service analyzes the traffic telemetry data to infer characteristics of an application on the endpoint device that generated the encrypted traffic. The service receives, from a monitoring agent on the endpoint device, application telemetry data regarding the application. The service determines that the application is evasive malware based on the characteristics of the application inferred from the traffic telemetry data and on the application telemetry data received from the monitoring agent on the endpoint device. The service initiates performance of a mitigation action in the network, after determining that the application on the endpoint device is evasive malware.

Abstract: In one embodiment, a device in a network detects an encrypted traffic flow associated with a client in the network. The device captures contextual traffic data regarding the encrypted traffic flow from one or more unencrypted packets associated with the client. The device performs a classification of the encrypted traffic flow by using the contextual traffic data as input to a machine learning-based classifier. The device generates an alert based on the classification of the encrypted traffic flow.

Abstract: A computing device having connectivity to a network stores one or more existing device models, where each of the one or more existing device models is a representation of a different client device used by a first authenticated user to access the network. The computing device obtains a device sample, which comprises network traffic data that is captured during a period of time and which is generated by a particular client device associated with the authenticated user of the network. The computing device determines, based on one or more relational criteria, whether the device sample should be assigned to one of the one or more existing device models or to an additional device model that has not yet been created. The computing device then determines relative identity of the particular client device based on whether the device sample is assigned to one of the one or more device models or to an additional device model that has not yet been created.

Abstract: A valve, in particular a 4/2-way slide valve (60), includes a valve piston (67), which is guided in a longitudinally movable manner in a valve housing (25), for selectively connecting and disconnecting fluid connections in the valve housing (25), such as a pressure supply connection (P), a tank connection (T), and two utility connections (A, B). A magnetic system (49), to which current can be supplied, controls the valve piston (67). When no current is supplied to the magnetic system (49), the control pressure at one of the utility connections (A, B) can be set, in particular limited, by a control device (62, 70, 79, 80, 88) arranged in the valve housing.

Abstract: The invention relates to a sheet depositing arrangement. The sheet depositing arrangement comprises at least one sheet depositing apparatus having a sheet stacking chamber for creating a sheet stack, and a sheet extraction device which in turn has a sheet transporting means that is drivable in a direction of circulation, a sheet delivery region for delivering the sheets to be stacked to the sheet stacking chamber, a sheet stacking region, provided downstream of the sheet delivery region, for stacking the sheets to be stacked into the sheet stacking chamber, and a return region, provided downstream of the sheet stacking region. Furthermore, the sheet depositing apparatus has a sheet retaining device with at least one sheet retaining element, arranged adjacent to the sheet stacking region, for preventing at least one sheet to be stacked from being drawn into the sheet extraction device from the sheet stacking chamber by the sheet transporting means.

Abstract: In one embodiment, a device in a network receives certificate data for an encrypted traffic flow associated with a client node in the network. The device determines one or more data features from the certificate data. The device determines one or more flow characteristics of the encrypted traffic flow. The device performs a classification of an application executed by the client node and associated with the encrypted traffic flow by using a machine learning-based classifier to assess the one or more data features from the certificate data and the one or more flow characteristics of the traffic flow. The device causes performance of a network action based on a result of the classification of the application.