H04L69/22

Detection block sending and receiving method, and network device and system

This application provides example detection block sending and receiving methods, and network devices and systems. One example detection block sending method includes obtaining, by a network device, an original bit block data flow. At least one detection block is generated, and the at least one detection block is inserted into a position of at least one idle block in the original bit block data flow. The bit block data flow including the at least one detection block is then sent.

Detection block sending and receiving method, and network device and system

This application provides example detection block sending and receiving methods, and network devices and systems. One example detection block sending method includes obtaining, by a network device, an original bit block data flow. At least one detection block is generated, and the at least one detection block is inserted into a position of at least one idle block in the original bit block data flow. The bit block data flow including the at least one detection block is then sent.

Emergency data gateway device
11539839 · 2022-12-27 · ·

A gateway device includes a call handling equipment (CHE) listener interface, an Internet Protocol (IP) interface, a provisioning engine, and a message parsing engine. The CPE listener interface forms a communication channel with a CHE and receives call event data from the CHE. The IP interface communicates with a cloud-based processing system. The provisioning engine receives, from the cloud-based processing system via the IP interface, instructions for parsing data from a data output format of the CHE into a consistent data format of the cloud-based processing system. The message parsing engine parses the call event data received from the CHE via the CHE listener interface, and formats the call event data according to the consistent data format. The gateway device transmits the formatted call event data to the cloud-based processing system via the IP interface.

Dynamic TCP stream processing with modification notification

Techniques for content inspection in a communication network, including detecting a packet in transit between a first and second endpoint, determining that content of the packet fails a content check, modifying a payload containing the content, adjusting a sequence number to account for the modification, and injecting a response message into a corresponding stream in an opposite direction. The response message may contain information relating to a reason for the rejection.

Dynamic TCP stream processing with modification notification

Techniques for content inspection in a communication network, including detecting a packet in transit between a first and second endpoint, determining that content of the packet fails a content check, modifying a payload containing the content, adjusting a sequence number to account for the modification, and injecting a response message into a corresponding stream in an opposite direction. The response message may contain information relating to a reason for the rejection.

Using machine learning algorithm to ascertain network devices used with anonymous identifiers

Techniques for identifying certain types of network activity are disclosed, including parsing of a Uniform Resource Locator (URL) to identify a plurality of key-value pairs in a query string of the URL. The plurality of key-value pairs may include one or more potential anonymous identifiers. In an example embodiment, a machine learning algorithm is trained on the URL to determine whether the one or more potential anonymous identifiers are actual anonymous identifiers (i.e., advertising identifiers) that provide advertisers a method to identify a user device without using, for example, a permanent device identifier. In this embodiment, a ranking threshold is used to verify the URL. A verified URL associate the one or more potential anonymous identifiers with the user device as actual anonymous identifiers. Such techniques may be used to identify and eliminate malicious and/or undesirable network traffic.

Using machine learning algorithm to ascertain network devices used with anonymous identifiers

Techniques for identifying certain types of network activity are disclosed, including parsing of a Uniform Resource Locator (URL) to identify a plurality of key-value pairs in a query string of the URL. The plurality of key-value pairs may include one or more potential anonymous identifiers. In an example embodiment, a machine learning algorithm is trained on the URL to determine whether the one or more potential anonymous identifiers are actual anonymous identifiers (i.e., advertising identifiers) that provide advertisers a method to identify a user device without using, for example, a permanent device identifier. In this embodiment, a ranking threshold is used to verify the URL. A verified URL associate the one or more potential anonymous identifiers with the user device as actual anonymous identifiers. Such techniques may be used to identify and eliminate malicious and/or undesirable network traffic.

Dynamic authentication scheme selection in computing systems

Techniques of dynamic authentication scheme selection in distributed computing systems are disclosed herein. One example technique includes analyzing a received authentication request for an indicator of an authentication scheme that is supported by a computing service submitting the authentication request. The example technique can also include determining whether the authentication scheme associated with the indicator is also supported by the authentication service and in response to determining that the authentication scheme associated with the indicator is also supported by the authentication service, initiating an authentication process with the computing service according to the authentication scheme that is supported by both the computing service and the authentication service. As such, the authentication scheme can be dynamically selected at the authentication service for the received authentication request.

Dynamic authentication scheme selection in computing systems

Techniques of dynamic authentication scheme selection in distributed computing systems are disclosed herein. One example technique includes analyzing a received authentication request for an indicator of an authentication scheme that is supported by a computing service submitting the authentication request. The example technique can also include determining whether the authentication scheme associated with the indicator is also supported by the authentication service and in response to determining that the authentication scheme associated with the indicator is also supported by the authentication service, initiating an authentication process with the computing service according to the authentication scheme that is supported by both the computing service and the authentication service. As such, the authentication scheme can be dynamically selected at the authentication service for the received authentication request.

Systems and methods for detection of vehicle bus protocol using signal analysis
11539550 · 2022-12-27 · ·

Embodiments of the invention include a vehicle telematics device that performs vehicle CAN bus discovery using bit timing analysis. In an embodiment, the vehicle telematics device enters a vehicle CAN bus protocol discovery mode, samples a vehicle CAN bus signal, performs bit timing analysis of the CAN bus signal, calculates a BAUD rate of the vehicle CAN bus based on the bit timing analysis, determines a data packet format of data packets on the vehicle CAN bus, and identifies a vehicle CAN bus protocol from a plurality of vehicle CAN bus protocols based on the calculated BAUD rate and data packet format.