System and methods are described for efficient monitoring of network traffic in a public cloud computing environment. In one implementation, a method comprises: generating flow log records of network traffic in the public cloud computing environment; identifying a data packet that presents a potential security risk; identifying a captured data packet (PCAP) record corresponding to the identified data packet; and transmitting the PCAP record to a computing device for network traffic analysis.

Embodiments are directed to managing communication over one or more networks. A monitoring engine may be instantiated to perform actions including receiving network traffic from a physical network that may be associated with network addresses of the physical network. The monitoring engine may analyze the network traffic to associate activity with gateway identifiers (GIDs) associated with gateway computers in an overlay network such that the GIDs are separate from the network addresses. The monitoring engine may be arranged to monitor the network traffic based on monitoring rules. The monitoring engine may provide metrics associated with the gateway computers based on the monitoring of the network traffic. The monitoring engine may compare the metrics to event rules. The monitoring engine may generate events based on affirmative results of the comparison. The events may be mapped to actions based on characteristics of the events and executed.

[Object] The object is to present a technical solution for a bank to disclose information on a deposit account directly to a third party. [Solution Means] The owner of a virtual currency address or real-currency deposit account provides a disclosure key to a disclosee (third party). The third party accesses a disclosure server 6 by operating a disclosee terminal 7, and transmits a disclosure request with the disclosure key. The disclosure server 6 accesses to an opener bank server 1, 8, acquires the virtual currency address owner information or the instant real-currency deposit account balance information, transmits it to the disclosee terminal 7, and make it browsable on the disclosee terminal 7.

A system for telemedicine diagnostics through remote sensing includes a computing device configured to initiate a communication interface between the computing device and a client device operated by a human subject, wherein the secure communication interface includes an audiovisual streaming protocol, receive, from at least a remote sensor at the human subject, a plurality of current physiological data, generate a clinical measurement approximation as a function of the change of a first discrete and a second discrete set of current physiological data, wherein generating further comprises receiving approximation training data correlating physiological data with clinical measurement data, training a measurement approximation model as a function of the training data and a machine-learning process, and generating the clinical measurement approximation as a function of the current physiological data and the measurement approximation model, and presenting the clinical measurement approximation to a user of the computing device using the secure communication interface.

Techniques described herein include a platform and process for provisioning user information onto a machine-to-machine device in order to enable the machine-to-machine device to conduct transactions utilizing the user information. In some embodiments, a user device is used to relay information between a machine-to-machine device and a provisioning service provider computer. In some embodiments, a machine-to-machine device is connected to the provisioning service provider computer via a network connection. Upon receiving a request to provision the machine-to-machine device, the service provider computer may identify the device from a device identifier. The service provider computer may generate an access credential or token for the machine-to-machine device. The access credential, token, and/or one or more policies may be provisioned onto the machine-to-machine device.

Requests are received for handling by a cloud computing environment which are then executed by the cloud computing environment. While each request is executing, performance metrics associated with the request are monitored. A vector is subsequently generated that encapsulates information associated with the request including the text within the request and the corresponding monitored performance metrics. Each request is then assigned (after it has been executed) to either a normal request cluster or an abnormal request cluster based on which cluster has a nearest mean relative to the corresponding vector. In addition, data can be provided that characterizes requests assigned to the abnormal request cluster. Related apparatus, systems, techniques and articles are also described.

Embodiments of the present disclosure provide systems, methods, and non-transitory computer storage media for detecting abnormal behavior of device in an enterprise network based on an analysis of behavioral information of the device's neighbors in network. At a high level, embodiments of the present disclosure employ a hive-mind approach to determine anomalous behavior of a device in a network based on analyzing behavior information reported by neighboring devices within the network. Embodiments identify that a device is alive and connected within the network based on multiple neighboring devices reporting behavioral information about the device; however, the device may be dysfunctional and failing to report its own information. By aggregating and analyzing behavioral information of a device based on the reporting information of its neighboring devices, embodiments of the present disclosure are able to determine whether a device is healthy even when the device is unable to report its own information.

Techniques are described for securely managing computing resources in a computing environment comprising a computing service provider and a remote computing network. The remote computing network includes computing and network devices configured to extend computing resources of the computing service provider to remote users of the computing service provider. The network devices include a trusted network device that includes a root of trust. The trusted network device detects that a new device is communicatively coupled to a port on the trusted network device. The trusted network device determines that the new device is not authorized to access computing resources at the remote computing network. The port is isolated at the trusted network device.

A method for generating a secret key at a first node for data communication between the first node and a second node. A channel estimate of a communication channel between the first and second nodes is obtained. A time-frequency matrix associated with the communication channel is then obtained based on the time-frequency transformation of the channel estimate. The secret key is then produced based on the time-frequency matrix. Furthermore, a corresponding key generator may be provided for generating a secret key.

A computer-implemented system and method for secure authentication of IoT devices are disclosed. The method for secure authentication of IoT devices comprises establishing a network connection with a network operator server via a control channel, establishing identity of the network operator server using a pre-shared server key from one or more of pre-shared server keys, establishing identity of the IoT device using a pre-shared client key from one or more of pre-shared client keys and cryptographically generating a session key for a network session to allow secure data exchange between the network operator server and the IoT device. The cryptographically generated session key is used for securely authenticating application running on the authenticated IoT device.