CN111694783A - Parallel data analysis method and device applied to DPI equipment - Google Patents
Parallel data analysis method and device applied to DPI equipment Download PDFInfo
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- CN111694783A CN111694783A CN202010527181.9A CN202010527181A CN111694783A CN 111694783 A CN111694783 A CN 111694783A CN 202010527181 A CN202010527181 A CN 202010527181A CN 111694783 A CN111694783 A CN 111694783A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F13/00—Interconnection of, or transfer of information or other signals between, memories, input/output devices or central processing units
- G06F13/38—Information transfer, e.g. on bus
- G06F13/40—Bus structure
- G06F13/4004—Coupling between buses
- G06F13/4027—Coupling between buses using bus bridges
- G06F13/405—Coupling between buses using bus bridges where the bridge performs a synchronising function
- G06F13/4054—Coupling between buses using bus bridges where the bridge performs a synchronising function where the function is bus cycle extension, e.g. to meet the timing requirements of the target bus
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F13/00—Interconnection of, or transfer of information or other signals between, memories, input/output devices or central processing units
- G06F13/38—Information transfer, e.g. on bus
- G06F13/42—Bus transfer protocol, e.g. handshake; Synchronisation
- G06F13/4204—Bus transfer protocol, e.g. handshake; Synchronisation on a parallel bus
- G06F13/4221—Bus transfer protocol, e.g. handshake; Synchronisation on a parallel bus being an input/output bus, e.g. ISA bus, EISA bus, PCI bus, SCSI bus
Abstract
The invention provides a parallel data analysis method applied to DPI equipment, which is applied to the DPI equipment, wherein the DPI equipment comprises a PCB (printed circuit board), a clamping groove is arranged on the PCB, the clamping groove is electrically connected with the PCB, and a parallel extended computing device is clamped in the clamping groove; the method comprises the following steps: s1: acquiring an application layer flow data packet; each flow data packet corresponds to a feature code; s2: classifying the obtained application layer flow data packet according to the feature code so as to determine the type of the current flow data packet; s31: when the type of the flow data packet is a first type, calling a parallel extension computing device to analyze the flow data packet of the first type to obtain a first analysis result; the first analysis result comprises the application type of the source of the first type of traffic data packet. The invention adopts the parallel expansion computing device to compute the first type of data, thereby improving the data processing efficiency.
Description
Technical Field
The invention relates to the field of DPI equipment, in particular to a parallel data analysis method and device applied to DPI equipment.
Background
The current DPI (deep packet inspection) technology is a key technology for message identification in the field of network security, a serial CPU is adopted in the traditional DPI equipment, and the efficiency of a common serial computing CPU in decrypting a huge data encryption data message is low. If the parallel computing technology is adopted, the data decryption mode can greatly improve the data decryption efficiency, and the decryption analysis parallel computing efficiency such as SSL is far higher than that of a CPU with a common serial structure, but the cost is higher.
As is well known, the serial computing approach has a very limited ability to decode large amounts of encrypted data and can consume significant computing resources. In terms of analysis of a large number of captured encrypted data packets, the serial computing mode will seriously affect the data processing efficiency. If an expansion device can be added to the original serial computing CPU device, the expansion device has the function of processing data in parallel, and the data processing efficiency of the DPI board card for encryption and decryption is effectively improved, which is very significant.
Disclosure of Invention
Therefore, a technical scheme applied to parallel data analysis of the DPI device needs to be provided to solve the problem of low data processing efficiency caused by the adoption of a serial computing mode of the existing DPI board card.
In order to achieve the above object, the inventor provides a parallel data analysis method applied to a DPI device, the method is applied to a DPI device, the DPI device includes a PCB circuit board, a card slot is arranged on the PCB circuit board, the card slot is electrically connected with the PCB circuit board, and a parallel expansion computing device is connected in the card slot in a clamped manner; the method comprises the following steps:
s1: acquiring an application layer flow data packet; each flow data packet corresponds to a feature code;
s2: classifying the obtained application layer flow data packet according to the feature code so as to determine the type of the current flow data packet;
s31: when the type of the flow data packet is a first type, calling a parallel extension computing device to analyze the flow data packet of the first type to obtain a first analysis result; the first analysis result comprises the application type of the source of the first type of traffic data packet.
As an alternative embodiment, the method further comprises, after step S31, step S4: and executing a preset processing strategy corresponding to the first analysis result according to the corresponding relation between the first analysis result and the preset processing strategy.
As an alternative embodiment, the types of the current traffic data packet include a first type and a second type, the first type is an encryption type, the second type is a non-encryption type, and the method further includes, after step S2, step S32:
and when the type of the flow data packet is a second type, analyzing the flow data packet of the second type by adopting a first processing chip on the PCB to obtain a second analysis result.
As an alternative embodiment, the method further comprises, after step S32, step S5: and executing a preset processing strategy corresponding to the second analysis result according to the corresponding relation between the second analysis result and the preset processing strategy.
As an alternative embodiment, step S31 includes:
creating a plurality of work queues by a second processing chip of the parallel expansion computing device; each work queue processes data in parallel;
in each work queue, matching the feature codes corresponding to the first type of flow data packets with the feature codes in the dictionary library to obtain a first analysis result; the dictionary library stores the corresponding relation between the feature codes and the application types.
As an alternative embodiment, the method comprises:
creating a matching library from the dictionary library according to the prefix part of the feature code corresponding to the flow data packet of the first type;
in each work queue, matching is carried out according to one part of the feature codes corresponding to the first type of flow data packets and the corresponding part of the feature codes in the matching library so as to obtain a first analysis result.
As an alternative embodiment, the apparatus is used to perform the parallel data analysis method according to any of claims 1 to 6 applied to a DPI device.
As an alternative embodiment, the parallel expansion computing device includes a USB interface, a first control chip, a second control chip, a firmware chip, a power control chip, and a graphics processor;
the USB interface is connected with a first control chip, the first control chip is connected with a second control chip, the second control chip is respectively connected with a firmware chip, a power supply control chip and a graphic processor, and the power supply control chip is further connected with the graphic processor.
As an alternative embodiment, the USB interface is a USB4.0 interface.
Different from the prior art, the parallel data analysis method applied to the DPI equipment in the technical scheme is applied to the DPI equipment, the DPI equipment comprises a PCB, a card slot is arranged on the PCB, the card slot is electrically connected with the PCB, and a parallel expansion computing device is connected in the card slot in a clamped mode; the method comprises the following steps: s1: acquiring an application layer flow data packet; each flow data packet corresponds to a feature code; s2: classifying the obtained application layer flow data packet according to the feature code so as to determine the type of the current flow data packet; s31: when the type of the flow data packet is a first type, calling a parallel extension computing device to analyze the flow data packet of the first type to obtain a first analysis result; the first analysis result comprises the application type of the source of the first type of traffic data packet. The invention adopts the parallel expansion computing device to compute the first type of data, thereby improving the data processing efficiency.
Drawings
Fig. 1 is a flowchart of a parallel data analysis method applied to a DPI device according to the prior art;
fig. 2 is a flowchart of a parallel data analysis method applied to a DPI device according to an embodiment of the present invention;
fig. 3 is a flowchart of a parallel data analysis method applied to a DPI device according to another embodiment of the present invention;
fig. 4 is a flowchart of a parallel data analysis method applied to a DPI device according to another embodiment of the present invention;
fig. 5 is a schematic structural diagram of a parallel expansion computing apparatus of a DPI device according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a parallel expansion computing system of a DPI device according to an embodiment of the present invention.
Description of reference numerals:
10. a parallel expansion computing device of the DPI equipment;
101. a USB interface;
102. a first control chip;
103. a second control chip;
104. a firmware chip;
105. a power supply control chip;
106. a graphics processor;
20. PCB circuit board.
Detailed Description
To explain technical contents, structural features, and objects and effects of the technical solutions in detail, the following detailed description is given with reference to the accompanying drawings in conjunction with the embodiments.
Fig. 1 is a flow chart of a parallel data analysis method applied to a DPI device in the prior art. The prior art DPI device does not have a PCE-E interface, and thus cannot be externally connected with other processing devices (such as a GPU and a graphics processor) supporting parallel computing to perform data operation, so that the DPI device often only can serially process data, and the data processing efficiency is greatly affected. When the field of data encryption and decryption is concerned, the method relates to characteristic comparison of a large amount of data.
As shown in fig. 1, the prior art, when processing the traffic data of the application layer, includes the following steps: firstly, capturing flow packet data, then extracting feature codes from the flow packet, comparing and judging whether the data is encrypted, if not, marking a corresponding program, and releasing or blocking the flow packet data according to a strategy; if the flow packet data is encrypted data, the flow packet data is marked as encrypted flow, a dictionary library is called to compare and decode the flow packet data one by one, and then the current flow packet is released or blocked according to a preset processing strategy according to a decoding result. It is obvious that, no matter encrypted traffic or unencrypted traffic, the prior art relies on a processing chip of the DPI device itself to process, and the encrypted traffic and the unencrypted traffic can only be processed in one time period, which is a serial data calculation manner essentially. When the data volume to be processed is huge, the efficiency is low by adopting a serial calculation mode, and the requirement cannot be met at all.
In order to solve the problems in the prior art, the invention provides a parallel data analysis method applied to DPI equipment. Fig. 4 is a flowchart of a parallel data analysis method applied to a DPI device according to an embodiment of the present invention. The method is applied to DPI equipment, the DPI equipment comprises a PCB, a card slot is arranged on the PCB, the card slot is electrically connected with the PCB, and a parallel expansion computing device is connected in the card slot in a clamping way; the method comprises the following steps:
the process first proceeds to step S1: acquiring an application layer flow data packet; each traffic packet corresponds to a signature. The feature code may be one or more of a number, a letter, a symbol, and preferably a character string. Each flow data packet has its corresponding feature code to mark which application program of the application layer sends out the flow data packet, and the source of the flow data packet can be effectively analyzed by analyzing the feature code.
Then, the process proceeds to step S2: classifying the obtained application layer flow data packet according to the feature code so as to determine the type of the current flow data packet;
then, the process proceeds to step S31: when the type of the flow data packet is a first type, calling a parallel extension computing device to analyze the flow data packet of the first type to obtain a first analysis result; the first analysis result comprises the application type of the source of the first type of traffic data packet.
Preferably, the first type is an encryption type, that is, when it is determined that the current traffic data packet is encrypted traffic, the parallel expansion computing device on the PCB card slot is called to analyze the current traffic data packet. The parallel expansion computing device can be provided with a processing chip supporting parallel operation, and the parallel analysis operation of the encrypted flow data can be realized through the processing chip, so that the processing efficiency of the original DPI equipment is greatly improved.
In certain embodiments, the method further comprises, after step S31, step S4: and executing a preset processing strategy corresponding to the first analysis result according to the corresponding relation between the first analysis result and the preset processing strategy.
In other embodiments, the types of the current traffic data packet include a first type and a second type, the first type is an encrypted type, and the second type is an unencrypted type, and the method further includes, after step S2, step S32: and when the type of the flow data packet is a second type, analyzing the flow data packet of the second type by adopting a first processing chip on the PCB to obtain a second analysis result. The first processing chip is preferably a CPU. Further, the method further includes, after the step S32, a step S5: and executing a preset processing strategy corresponding to the second analysis result according to the corresponding relation between the second analysis result and the preset processing strategy.
The preset processing strategy can be set according to the actual needs of the user, and comprises 'allowing the current traffic data packet to pass' or 'preventing the current traffic data packet from passing'. For example, in an application scenario, a traffic feature code corresponding to a wechat application is captured, after feature code comparison, the application type of the source of the feature code is found to be the wechat application, then a processing strategy formulated by a user is searched, if the processing strategy corresponding to the wechat application is inquired to be 'allowing the wechat traffic to pass', the traffic is released, and if the processing strategy corresponding to the wechat application is inquired to be 'not allowing the wechat to be used', the traffic is prevented.
In certain embodiments, step S31 includes: creating a plurality of work queues by a second processing chip of the parallel expansion computing device; each work queue processes data in parallel; in each work queue, matching the feature codes corresponding to the first type of flow data packets with the feature codes in the dictionary library to obtain a first analysis result; the dictionary library stores the corresponding relation between the feature codes and the application types. The dictionary library stores characteristic codes corresponding to a plurality of application programs, and is marked with different characteristic codes corresponding to the application types. By creating a plurality of work queues, each work queue is equivalent to one work thread, and the feature code comparison can be synchronously performed in parallel, so that the data analysis efficiency is effectively improved.
Preferably, the method comprises: creating a matching library from the dictionary library according to the prefix part of the feature code corresponding to the flow data packet of the first type; in each work queue, matching is carried out according to one part of the feature codes corresponding to the first type of flow data packets and the corresponding part of the feature codes in the matching library so as to obtain a first analysis result. The feature codes stored in the dictionary library are large in quantity, in order to improve the subsequent comparison speed, the prefix part of the feature codes to be compared at present is used as an index condition, the part of the feature codes meeting the condition is screened out to be used as a matching library, and then the full version of the feature codes to be compared is matched with all the feature codes in the matching library, so that the matching efficiency is improved.
As shown in fig. 2, the present application relates to a method comprising the steps of, in its entirety: firstly, capturing flow packet data, then extracting feature codes from the flow packet, comparing and judging whether the data is encrypted, if not, marking a corresponding program, and releasing or blocking the flow packet data according to a strategy; if the flow packet data is encrypted data, the flow packet data is marked as encrypted flow, a parallel computing expansion device is called for decoding, and then the current flow packet is released or blocked according to a preset processing strategy according to a decoding result.
As shown in fig. 3, taking a processing chip on the parallel computing expansion device as a GPU as an example, after acquiring the encrypted traffic, a matching library is created from a dictionary library according to a prefix portion of the encrypted traffic feature code, and then work queues are created according to the GPU thread number, each work queue has its own corresponding processing task, so as to implement parallel processing of the multi-thread tasks.
For example, the feature code has 100 bits, a prefix portion 20 bits, a suffix portion 20 bits, and a middle portion 60 bits, when the feature code is compared, 4 work queues may be created, the 1 st work queue compares the prefix portion of the feature code, the 2 nd work queue compares the suffix portion of the feature code, the 3 rd work queue compares from the 50 th bit to the 21 st bit (i.e., compares from the middle portion to the prefix portion), and the 4 th work queue compares from the 50 th bit to the 79 th bit (i.e., compares from the middle portion to the suffix portion), because for the same feature code, the 4 queues are compared synchronously in parallel, the comparison efficiency of a single feature code is greatly improved, and the data parsing efficiency is further improved. Of course, when the number of the feature codes is multiple, different parts of the multiple feature codes can be compared and calculated in parallel, and the comparison mode is similar to that of a single feature code, and is not described herein again.
The inventor also provides a parallel data analysis apparatus applied to a DPI device, the apparatus being used for executing the parallel data analysis method applied to a DPI device as described above.
Fig. 5 is a schematic structural diagram of a parallel expansion computing device of a DPI device according to an embodiment of the present invention. The device 10 includes a USB interface 101, a first control chip 102, a second control chip 103, a firmware chip 104, a power control chip 105, and a graphics processor 106. The USB interface 101 is connected to a first control chip 102, the first control chip 102 is connected to a second control chip 103, the second control chip 103 is connected to a firmware chip 104, a power control chip 105 and a graphics processor 106, respectively, and the power control chip 105 is further connected to the graphics processor 106.
In this embodiment, the USB interface is a USB4.0 interface. USB4.0 is a more supportive standard, which is not only directly compatible with Thunderbolt, but also supports multiple existing transmission standards such as 100W charging, DisplayPort, PCI-E, etc., and is also compatible with old USB standards such as USB 2.0. Due to the compatible action of the USB4.0, the USB interface on the original DPI board card can be switched to be the PCI-E interface, and then the PCI-E interface is connected with the graphics processing unit (namely the GPU), so that the computing capacity of the original DPI board card is expanded.
In certain embodiments, the first control chip is a TI83USB4 control chip. The second control chip is a lightning control chip. Preferably, the lightning control chip is a JHL6540USB4 lightning control chip. In short, an important role of the expansion computing device is to serve as a bridge between the DPI board (i.e., the "PCB board" in the foregoing) and the expanded graphics processor, so that the part of the DPI board that is interfaced with the DPI board is electrically connected through the USB 4.0-supporting control chip, and the part connected with the GPU is electrically connected through the PCI-e bus-supporting lightning control. As mentioned above, since the USB4.0 internal standard is compatible with the lightning interface, the conversion from the USB4.0 to the PCI-E interface can be achieved by using the lightning interface.
Thunderbolt technology fuses two communication protocols, pci express and DisplayPort. The pci express is used for data transmission, and any type of equipment expansion can be conveniently carried out; DisplayPort is used for display, and can transmit 1080p or even ultra high definition video and up to eight channels of audio simultaneously. Meanwhile, the two channels have independent channels during transmission, and no interference is generated. The physical appearance of the Thunderbolt interface (i.e. the thunder and lightning interface) is the same as that of the original MiniDisplayPort interface, and the display of the MiniDP interface and the adapters of the interfaces from MiniDP to HDMI/DVI/VGA and the like can be used on the Thunderbolt interface.
Generally, the Thunderbolt interface with more advantages in the aspect of compatibility can be driven by an Intel control chip by applying the conventional physical interface, is connected with a system chip set through a PCI-Ex4 and a DisplayPort bus, and can also be directly connected with an Intel processor core display card to carry out DisplayPort output. The transmission mechanism based on the PCI-E protocol enables data transmission to be more convenient, conversion steps are omitted, and meanwhile the method can be suitable for more occasions. Thunderbolt uses copper wire cables, limited in length to 3 meters. The fiber optic cable product will also be pushed out. The theoretical transmission rate of the optical fiber can reach 100Gbps, and the copper wire is only set to 10 Gbps. Copper wire will remain an advantage in the future in that 10W of power is provided through the cable, whereas fiber Thunderbolt does not provide power.
In this embodiment, the standard for converting the Thunderbolt interface into the NVME (m.2) interface is PCI-E, and the Thunderbolt interface can be converted into the PCI-E interface, so that the Thunderbolt interface can be conveniently used for expanding the GPU. Because the USB4.0 interface supports Thunderbolt characteristics, the USB4.0 interface can be converted into a PCI-E interface for expanding the GPU, and the problem that the GPU cannot be expanded due to the fact that an original DPI board does not support the conversion of the PCI-E interface is effectively solved.
As shown in fig. 6, the inventor further provides a parallel expansion computing system of DPI equipment, where the system includes a PCB circuit board 20, and a card slot is disposed on the PCB circuit board 20, the card slot is electrically connected to the PCB circuit board, a parallel expansion computing device of DPI equipment is connected in the card slot, and the parallel expansion computing device of DPI equipment is the parallel expansion computing device of DPI equipment described above. When the GPU is used, the GPU can be inserted into the clamping groove, and the GPU and a CPU of the PCB can realize a parallel operation function, so that the data processing efficiency is improved. Specifically, the USB4.0 interface on the PCB 20 is first converted into a PCI-E interface by expanding other components on the computing device except the GPU by using the feature of its compatible lightning interface, and the GPU is connected to the PCI-E interface to be inserted into the card slot.
In some embodiments, the system includes a body having a cavity therein for receiving the PCB circuit board, the PCB circuit board being disposed within the cavity. Preferably, a fixing frame is further arranged in the body, and the PCB is fixedly connected to one side of the fixing frame. The outer surface of the body is also provided with a support frame for supporting the parallel expansion computing device, and the support frame is used for supporting the parallel expansion computing device. Preferably, the number of the supporting frames is two, and the supporting frames are symmetrically distributed on the outer side of the body.
The invention provides a parallel data analysis method applied to DPI equipment, which is applied to the DPI equipment, wherein the DPI equipment comprises a PCB (printed circuit board), a clamping groove is arranged on the PCB, the clamping groove is electrically connected with the PCB, and a parallel extended computing device is clamped in the clamping groove; the method comprises the following steps: s1: acquiring an application layer flow data packet; each flow data packet corresponds to a feature code; s2: classifying the obtained application layer flow data packet according to the feature code so as to determine the type of the current flow data packet; s31: when the type of the flow data packet is a first type, calling a parallel extension computing device to analyze the flow data packet of the first type to obtain a first analysis result; the first analysis result comprises the application type of the source of the first type of traffic data packet. The invention adopts the parallel expansion computing device to compute the first type of data, thereby improving the data processing efficiency.
It should be noted that, although the above embodiments have been described herein, the invention is not limited thereto. Therefore, based on the innovative concepts of the present invention, the technical solutions of the present invention can be directly or indirectly applied to other related technical fields by making changes and modifications to the embodiments described herein, or by using equivalent structures or equivalent processes performed in the content of the present specification and the attached drawings, which are included in the scope of the present patent.
Claims (9)
1. The parallel data analysis method is applied to DPI equipment, and is characterized in that the DPI equipment comprises a PCB (printed circuit board), wherein a card slot is arranged on the PCB, the card slot is electrically connected with the PCB, and a parallel expansion computing device is connected in the card slot in a clamped mode; the method comprises the following steps:
s1: acquiring an application layer flow data packet; each flow data packet corresponds to a feature code;
s2: classifying the obtained application layer flow data packet according to the feature code so as to determine the type of the current flow data packet;
s31: when the type of the flow data packet is a first type, calling a parallel extension computing device to analyze the flow data packet of the first type to obtain a first analysis result; the first analysis result comprises the application type of the source of the first type of traffic data packet.
2. The parallel data analysis method applied to DPI devices according to claim 1, wherein the method further comprises step S4 after step S31: and executing a preset processing strategy corresponding to the first analysis result according to the corresponding relation between the first analysis result and the preset processing strategy.
3. The parallel data analysis method applied to DPI devices in claim 1, wherein the types of the current traffic data packet include a first type and a second type, the first type is an encrypted type, the second type is an unencrypted type, the method further comprises after step S2 step S32:
and when the type of the flow data packet is a second type, analyzing the flow data packet of the second type by adopting a first processing chip on the PCB to obtain a second analysis result.
4. The parallel data analysis method applied to DPI devices according to claim 3, wherein the method further comprises step S5 after step S32: and executing a preset processing strategy corresponding to the second analysis result according to the corresponding relation between the second analysis result and the preset processing strategy.
5. The parallel data analysis method applied to DPI devices according to claim 1, wherein step S31 includes:
creating a plurality of work queues by a second processing chip of the parallel expansion computing device; each work queue processes data in parallel;
in each work queue, matching the feature codes corresponding to the first type of flow data packets with the feature codes in the dictionary library to obtain a first analysis result; the dictionary library stores the corresponding relation between the feature codes and the application types.
6. The method of parallel data analysis applied to DPI devices according to claim 5, wherein the method comprises:
creating a matching library from the dictionary library according to the prefix part of the feature code corresponding to the flow data packet of the first type;
in each work queue, matching is carried out according to one part of the feature codes corresponding to the first type of flow data packets and the corresponding part of the feature codes in the matching library so as to obtain a first analysis result.
7. Parallel data analysis apparatus for DPI devices, characterized in that said apparatus is adapted to perform a parallel data analysis method according to any of claims 1 to 6 for DPI devices.
8. The parallel data analysis apparatus applied to DPI devices in claim 7, wherein the parallel expansion computation apparatus comprises a USB interface, a first control chip, a second control chip, a firmware chip, a power control chip and a graphics processor;
the USB interface is connected with a first control chip, the first control chip is connected with a second control chip, the second control chip is respectively connected with a firmware chip, a power supply control chip and a graphic processor, and the power supply control chip is further connected with the graphic processor.
9. The apparatus according to claim 8, wherein the USB interface is a USB4.0 interface.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090327854A1 (en) * | 2008-06-30 | 2009-12-31 | Yahoo!, Inc. | Analysis of Database Performance Reports for Graphical Presentation of Summary Results |
| CN102394827A (en) * | 2011-11-09 | 2012-03-28 | 浙江万里学院 | Hierarchical classification method for internet flow |
| CN104038389A (en) * | 2014-06-19 | 2014-09-10 | 高长喜 | Multiple application protocol identification method and device |
| US20140317276A1 (en) * | 2013-03-15 | 2014-10-23 | Aerohive Networks, Inc. | Application based data traffic routing using network tunneling |
| CN104518984A (en) * | 2014-12-22 | 2015-04-15 | 北京航空航天大学 | SDN controller for carrying out traffic classification on data package based on multiple classifiers |
| CN106452954A (en) * | 2016-09-30 | 2017-02-22 | 苏州迈科网络安全技术股份有限公司 | HTTP data characteristic analysis method and system |
| CN106940778A (en) * | 2017-03-10 | 2017-07-11 | 华东师范大学 | A kind of encryption data method cracked based on the parallel dictionaries of GPU in support storehouse |
| CN110022267A (en) * | 2018-01-09 | 2019-07-16 | 阿里巴巴集团控股有限公司 | Processing method of network data packets and device |
| CN110019087A (en) * | 2017-11-09 | 2019-07-16 | 北京京东尚科信息技术有限公司 | Data processing method and its system |
| CN110908798A (en) * | 2019-11-08 | 2020-03-24 | 丁剑明 | Multi-process cooperative network traffic analysis method and device |
-
2020
- 2020-06-11 CN CN202010527181.9A patent/CN111694783B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090327854A1 (en) * | 2008-06-30 | 2009-12-31 | Yahoo!, Inc. | Analysis of Database Performance Reports for Graphical Presentation of Summary Results |
| CN102394827A (en) * | 2011-11-09 | 2012-03-28 | 浙江万里学院 | Hierarchical classification method for internet flow |
| US20140317276A1 (en) * | 2013-03-15 | 2014-10-23 | Aerohive Networks, Inc. | Application based data traffic routing using network tunneling |
| CN104038389A (en) * | 2014-06-19 | 2014-09-10 | 高长喜 | Multiple application protocol identification method and device |
| CN104518984A (en) * | 2014-12-22 | 2015-04-15 | 北京航空航天大学 | SDN controller for carrying out traffic classification on data package based on multiple classifiers |
| CN106452954A (en) * | 2016-09-30 | 2017-02-22 | 苏州迈科网络安全技术股份有限公司 | HTTP data characteristic analysis method and system |
| CN106940778A (en) * | 2017-03-10 | 2017-07-11 | 华东师范大学 | A kind of encryption data method cracked based on the parallel dictionaries of GPU in support storehouse |
| CN110019087A (en) * | 2017-11-09 | 2019-07-16 | 北京京东尚科信息技术有限公司 | Data processing method and its system |
| CN110022267A (en) * | 2018-01-09 | 2019-07-16 | 阿里巴巴集团控股有限公司 | Processing method of network data packets and device |
| CN110908798A (en) * | 2019-11-08 | 2020-03-24 | 丁剑明 | Multi-process cooperative network traffic analysis method and device |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113965526A (en) * | 2021-09-18 | 2022-01-21 | 网宿科技股份有限公司 | Data processing method, electronic device and computer readable storage medium |
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