Abstract: Examples relate to extracting data from network communications. In one example, a programmable hardware processor may: receive a first set of network packets; store each network packet included in the first set in a first storage device; identify, from each network packet included in a subset of the first set of network packets, data included in the network packet, the data meeting at least one condition defined by first programmable logic of the programmable hardware processor; and for each network packet included in the subset: extract, from the network packet, data of interest; and store, in a second storage device, i) the extracted data of interest, and ii) an identifier associated with the network packet.

Abstract: In one embodiment, local begin and end tags are detected by a network security device to determine a local context of a network traffic flow, and a local feature vector is obtained for that local context. At least one triggering machine learning model is applied by the network security device to the local feature vector, and the result determines whether or not deeper analysis is warranted. In most cases, very substantial resources are not required because deeper analysis is not indicated. If deeper analysis is indicated, one or more deeper machine learning model may then be applied to global and local feature vectors, and regular expressions may be applied to packet data, which may include the triggering data packet and one or more subsequent data packets. Other embodiments, aspects and features are also disclosed.

Abstract: Examples relate to organizing and storing network communications. In one example, a programmable hardware processor may: receive a first set of network packets; identify, for each network packet included in the first set, a network flow, each network flow including at least one related packet; store each network packet included in a subset of the first set in a first data storage device; for each network packet included in the subset, organize the network packet according to the network flow identified for the network packet; identify, from the network flows, a set of network flows that each have at least one characteristic of interest; and store, in a second data storage device, each network packet included in each network flow of the set of network flows.

Abstract: Examples of implementations relate to metadata extraction. For example, a system of privacy preservation comprises a physical processor that executes machine-readable instructions that cause the system to normalize a network traffic payload with a hardware-based normalization engine controlled by a microcode program; parse the normalized network traffic payload, as the network traffic payload passes through a network, by performing a parsing operation of a portion of the normalized network traffic payload with a hardware-based function engine of a plurality of parallel-distributed hardware-based function engines controlled by the microcode program; and provide the hardware-based function engine with a different portion of the normalized network traffic payload responsive to an indication, communicated through a common status interface, that the different portion of the normalized network traffic payload is needed to complete the parsing operation.

Abstract: Examples of implementations relate to metadata extraction. For example, a system of privacy preservation comprises a physical processor that executes machine-readable instructions that cause the system to normalize a network traffic payload with a hardware-based normalization engine controlled by a microcode program; parse the normalized network traffic payload, as the network traffic payload passes through a network, by performing a parsing operation of a portion of the normalized network traffic payload with a hardware-based function engine of a plurality of parallel-distributed hardware-based function engines controlled by the microcode program; and provide the hardware-based function engine with a different portion of the normalized network traffic payload responsive to an indication, communicated through a common status interface, that the different portion of the normalized network traffic payload is needed to complete the parsing operation.

Abstract: In one embodiment, local begin and end tags are detected by a network security device to determine a local context of a network traffic flow, and a local feature vector is obtained for that local context. At least one triggering machine learning model is applied by the network security device to the local feature vector, and the result determines whether or not deeper analysis is warranted. In most cases, very substantial resources are not required because deeper analysis is not indicated. If deeper analysis is indicated, one or more deeper machine learning model may then be applied to global and local feature vectors, and regular expressions may be applied to packet data, which may include the triggering data packet and one or more subsequent data packets. Other embodiments, aspects and features are also disclosed.

Abstract: Examples relate to organizing and storing network communications. In one example, a programmable hardware processor may: receive a first set of network packets; identify, for each network packet included in the first set, a network flow, each network flow including at least one related packet; store each network packet included in a subset of the first set in a first data storage device; for each network packet included in the subset, organize the network packet according to the network flow identified for the network packet; identify, from the network flows, a set of network flows that each have at least one characteristic of interest; and store, in a second data storage device, each network packet included in each network flow of the set of network flows.

Abstract: Examples of implementations relate to metadata extraction. For example, a system of privacy preservation comprises a physical processor that executes machine-readable instructions that cause the system to normalize a network traffic payload with a hardware-based normalization engine controlled by a microcode program; parse the normalized network traffic payload, as the network traffic payload passes through a network, by performing a parsing operation of a portion of the normalized network traffic payload with a hardware-based function engine of a plurality of parallel-distributed hardware-based function engines controlled by the microcode program; and provide the hardware-based function engine with a different portion of the normalized network traffic payload responsive to an indication, communicated through a common status interface, that the different portion of the normalized network traffic payload is needed to complete the parsing operation.

Abstract: Examples relate to organizing and storing network communications. In one example, a programmable hardware processor may: receive a first set of network packets; identify, for each network packet included in the first set, a network flow, each network flow including at least one related packet; store each network packet included in a subset of the first set in a first data storage device; for each network packet included in the subset, organize the network packet according to the network flow identified for the network packet; identify, from the network flows, a set of network flows that each have at least one characteristic of interest; and store, in a second data storage device, each network packet included in each network flow of the set of network flows.

Abstract: Examples of implementations relate to metadata extraction. For example, a system of privacy preservation comprises a physical processor that executes machine-readable instructions that cause the system to normalize a network traffic payload with a hardware-based normalization engine controlled by a microcode program; parse the normalized network traffic payload, as the network traffic payload passes through a network, by performing a parsing operation of a portion of the normalized network traffic payload with a hardware-based function engine of a plurality of parallel-distributed hardware-based function engines controlled by the microcode program; and provide the hardware-based function engine with a different portion of the normalized network traffic payload responsive to an indication, communicated through a common status interface, that the different portion of the normalized network traffic payload is needed to complete the parsing operation.

Abstract: Examples relate to organizing and storing network communications. In one example, a programmable hardware processor may: receive a first set of network packets; identify, for each network packet included in the first set, a network flow, each network flow including at least one related packet; store each network packet included in a subset of the first set in a first data storage device; for each network packet included in the subset, organize the network packet according to the network flow identified for the network packet; identify, from the network flows, a set of network flows that each have at least one characteristic of interest; and store, in a second data storage device, each network packet included in each network flow of the set of network flows.

Abstract: Examples relate to extracting data from network communications. In one example, a programmable hardware processor may: receive a first set of network packets; store each network packet included in the first set in a first storage device; identify, from each network packet included in a subset of the first set of network packets, data included in the network packet, the data meeting at least one condition defined by first programmable logic of the programmable hardware processor; and for each network packet included in the subset: extract, from the network packet, data of interest; and store, in a second storage device, i) the extracted data of interest, and ii) an identifier associated with the network packet.