Disclosed herein are a fuzzing preprocessing apparatus and method for automating smart network fuzzing. The fuzzing preprocessing method includes collecting communication message samples that are sent by a fuzzing target client to a fuzzing target system, comparing the communication message samples with each other, and then identifying sizes and types of fields of a fuzzing target protocol, determining a property of a protocol field value with reference to ASCII code, determining a coverage of a user field based on a response message to a test communication message that has been sent to the fuzzing target system, and storing a fuzzing protocol data model having a field number, a field type, a field size, a field value property, and a field value of the fuzzing target protocol, as elements.

In some implementations, a device may determine one or more parameters for filtering network traffic of a network that includes a plurality of virtual packet brokers provided for a plurality of cloud random access networks and a plurality of traffic aggregation points. The device may provide the one or more parameters to the plurality of virtual packet brokers, to cause the plurality of virtual packet brokers to filter the network traffic to obtain network visibility traffic. The device may receive, from one or more probes of a session aggregation point of the network, one or more metrics calculated based on the network visibility traffic by the plurality of virtual packet brokers. The device may determine one or more actions to be implemented based on the one or more metrics. The device may cause the one or more actions to be implemented in the network.

End-to-end latency measurements for synchronization are described. A first wireless device may determine an uplink latency associated with sending communications to a second wireless device. The first wireless device may determine a downlink latency associated with receiving communications from the second wireless device. Based on the uplink latency being different from the downlink latency, the first wireless device, the second wireless, and/or a base station may perform an alignment (e.g., time-based alignment, channel-based alignment) to modify and/or adjust one or more parameters to make the uplink latency equivalent to the downlink latency.

This application sets forth various techniques for monitoring a status of a service. According to some embodiments, a DNS server can implement a health check engine that monitors the status of the service in order to implement round-robin DNS among a plurality of availability zones. Each service instance for the service can include a monitoring agent configured to (1) monitor the status of the service instance, and (2) respond to health check messages received from the health check engine. The monitoring agent can also be configured to (1) collect statistics associated with one or more service dependencies of the service instance during a tracking window, (2) calculate at least one ratio based on the statistics collected during the tracking window, and (3) generate the status of the service instance by comparing the at least one ratio to a threshold value.

A method and an apparatus for filtering a wireless signal by parsing a wireless signal transmitted from a surrounding wireless terminal so as to extract channel information of the surrounding wireless terminal in a MAC frame and performing adaptive filtering on the wireless signal.

A computer method and system for determining common network security filter settings for one or more clusters of network servers. Network traffic samples are captured which are associated with a plurality of network servers. The captured network traffic samples are collated with regards to each of the plurality of network servers. The collated network traffic is analyzed for each of the plurality of network servers for determining suggested network security filter settings for each network server. One or more clusters of network servers are determined contingent upon the determined suggested network security filter settings for each of the plurality of network servers. Common network security group filter settings are determined for each determined cluster of network servers.

Monitoring an operational characteristic of a data communication device within a network includes sampling an operational characteristic of the data communication device at a fine-grain sample rate over a first sampling interval to produce fine-grain samples of the operational characteristic of the data communication device, training a machine learning algorithm using the fine-grain samples of the operational characteristic of the data communication device, the fine-grain sample rate, and a coarse-grain sample rate that is less than the fine-grain sample rate, sampling the operational characteristic of the data communication device at the coarse-grain sample rate over a second sampling interval to produce coarse-grain samples of the operational characteristic of the data communication device, and using the machine learning algorithm to process the coarse-grain samples of the operational characteristic of the data communication device to produce accuracy-enhanced samples of the operational characteristic of the data communication device.

A system and a method for performing programmable analytics on network data are described. A data layer constructs flow behavior information based on information present within headers of data packets flowing across one or more network devices configured in a computer network. An inline heuristics layer performs one or more inline heuristic operations on the flow behavior information to obtain aggregate statistical information. An integrated analytics layer performs one or more analytical operations on the flow behavior information to obtain network insights. A presentation layer filters and plots information obtained from the data layer, the inline heuristics layer, and the integrated analytics layer, based on a user input.

An apparatus, systems, methods, and the like, for autonomous scaling of Internet services, such as scaling Internet access speeds, provided by a communications network to one or more connected networks is provided. The service-providing network may include one or more virtual interface devices through which the connected network or device may access the Internet. The connected networks may utilize these virtual devices to communicate with the broader Internet to exchange data. For example, the connected networks may be associated with a common access point to the service providing network, such as an on-net building or other common location that utilizes the same device or devices to access the network to receive services from the network. In some implementations, the virtual network access devices may be instantiated on one or more network computing devices, such as an application server or other configurable computing and networking device.

The packet capture manager uses a multi-tiered storage for storing captured network traffic. Captured packets are stored on a primary storage with a time-to-live according to a retention policy. The packet capture manager receives instructions from one or more network monitoring devices identifying one or more captured packets as packets of interest. The packet capture manager flags the identified packets as packets of interest, moves the flagged packets to a secondary storage, and changes the TTL of the moved packets. A machine learning model analyzes historical data of the instructions received from the one or more network monitoring devices. The packet capture manager uses the machine learning model to identify packets of interest and move identified packets to the secondary storage without specific instructions from a network monitoring device.