CN108234452B - System and method for identifying network data packet multilayer protocol - Google Patents
System and method for identifying network data packet multilayer protocol Download PDFInfo
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- CN108234452B CN108234452B CN201711322465.9A CN201711322465A CN108234452B CN 108234452 B CN108234452 B CN 108234452B CN 201711322465 A CN201711322465 A CN 201711322465A CN 108234452 B CN108234452 B CN 108234452B
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Abstract
The invention provides a system and a method for identifying a network data packet multilayer protocol, which comprises the following steps: a data input module: reading data packets from a data source, stripping a known lower layer protocol, extracting an unknown load, and outputting a plurality of data packets, wherein each output data packet comprises the unknown load and known meta-information of the lower layer protocol; an analysis module: and extracting continuous data blocks from the output of the data input module, executing protocol detection on each continuous data block fragment, and counting the adjacent probability of the protocol according to the protocol detection result. The invention solves the defect that the prior art can not classify the complete protocol stack.
Description
Technical Field
The invention relates to the field of network flow analysis and counting, in particular to a system and a method for identifying a network data packet multi-layer protocol.
Background
The network traffic classification technology is to determine a protocol or application category to which network traffic belongs by analyzing network traffic data. Classifying network traffic is an important means for analyzing network load-bearing traffic and performing service performance analysis.
Currently, in the field of traffic classification, a load-based classification method is generally used as an accurate method, and the Deep inspection (DPI) of the content of a network Packet is performed by the load-based classification method, which includes:
1. the method adopts a group of load characteristics (precise characteristics and regular expressions) to identify the application in the network traffic, and has very high identification precision. For example, the invention patent with application number 200710152390.4 discloses a network traffic classification processing method and a network traffic classification processing device.
2. The samples of the collection protocol are classified by training a classifier through machine learning techniques. For example, the invention patent with application number 201310414970.1, "network traffic classification method and device", and the invention patent with application number 201510176138.1, "network traffic classification method".
However, as technology develops, network traffic becomes more complex, and each layer of lower layer protocol can carry a plurality of different upper layer protocols. The true class of a piece of data is likely to be a protocol tree and cannot be simply expressed in terms of a class.
In one example, a complex sample network protocol stack, such as that shown in FIG. 1, is a protocol stack that a typical web site may contain. The HTTP upper layer may carry various traffic including HTML pages, JSON data, and MIME may be used to upload files and submit forms. There are also many middleware in enterprise applications that carry a variety of different upper layer applications.
In order to analyze network traffic more accurately, especially when the service information in the network traffic needs to be analyzed, it is not enough to simply give a classification, and a complete protocol stack must be classified.
The prior art including the three patents can only provide one classification result, and the problem of classifying a complex protocol stack cannot be solved.
Disclosure of Invention
In view of the deficiencies of the prior art, it is an object of the present invention to provide a system and method for multi-layer protocol identification of network packets.
The system for identifying the network data packet multilayer protocol provided by the invention comprises the following components:
a data input module: reading data packets from a data source, stripping a known lower layer protocol, extracting an unknown load, and outputting a plurality of data packets, wherein each output data packet comprises the unknown load and known meta-information of the lower layer protocol;
an analysis module: and extracting continuous data blocks from the output of the data input module, executing protocol detection on each continuous data block fragment, and counting the adjacent probability of the protocol according to the protocol detection result.
Preferably, the performing protocol detection on each continuous data block fragment includes:
the method comprises the steps of segmenting continuous data blocks into data segments with fixed sizes, detecting a protocol for each data segment, and generating a classification sequence comprising a plurality of classifications;
inserting a special classification in the head of the generated classification sequence, wherein the special classification represents a classification known by an upper layer.
Preferably, the statistical protocol adjacency probability includes:
establishing a matrix of M x M by using all different classifications, wherein M is the number of all different classifications, and initializing all values of the matrix to be 0;
and adding 1 to the value of every two adjacent classifications in the classification sequence at the corresponding position of the matrix according to the result obtained by executing the protocol detection on each data fragment.
Preferably, the analysis module further constructs a protocol relationship graph after counting the protocol adjacent probability:
converting the matrix into a graph, wherein the nodes of the graph correspond to the protocol, the weights of the edges correspond to the values in the matrix, and the root nodes of the graph are the special classifications.
Preferably, the system further comprises a query module: providing a user interface, executing a query algorithm according to a query requirement input by a user, and outputting a query result.
The method for identifying the network data packet multilayer protocol provided by the invention comprises the following steps:
a data input step: reading data packets from a data source, stripping a known lower layer protocol, extracting an unknown load, and outputting a plurality of data packets, wherein each output data packet comprises the unknown load and known meta-information of the lower layer protocol;
and (3) an analysis step: extracting continuous data blocks from the output of the data input step, executing protocol detection on each continuous data block fragment, and counting the adjacent probability of the protocol according to the protocol detection result.
Preferably, the performing protocol detection on each continuous data block fragment includes:
the method comprises the steps of segmenting continuous data blocks into data segments with fixed sizes, detecting a protocol for each data segment, and generating a classification sequence comprising a plurality of classifications;
inserting a special classification in the head of the generated classification sequence, wherein the special classification represents a classification known by an upper layer.
Preferably, the statistical protocol adjacency probability includes:
establishing a matrix of M x M by using all different classifications, wherein M is the number of all different classifications, and initializing all values of the matrix to be 0;
and adding 1 to the value of every two adjacent classifications in the classification sequence at the corresponding position of the matrix according to the result obtained by executing the protocol detection on each data fragment.
Preferably, the analyzing step further comprises constructing a protocol relationship graph after the statistics of the adjacent probabilities of the protocols:
converting the matrix into a graph, wherein the nodes of the graph correspond to the protocol, the weights of the edges correspond to the values in the matrix, and the root nodes of the graph are the special classifications.
Preferably, the method further comprises the following query steps: providing a user interface, executing a query algorithm according to a query requirement input by a user, and outputting a query result.
Compared with the prior art, the invention has the following beneficial effects:
1. the defect that the prior art cannot classify a complete protocol stack is overcome;
2. the classification result is stored by using the graph data structure, the complex protocol level information in the data is accurately represented, and the results with different detail degrees can be extracted according to different scenes.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a schematic diagram of a complex sample network protocol stack;
FIG. 2 is a block diagram of a system for multi-layer protocol identification of network packets according to the present invention;
FIG. 3 is a schematic diagram of the operation of an analysis module according to the present invention;
FIGS. 4-7 are schematic diagrams of the data transformation process of the analysis module of the present invention;
FIG. 8 is a flow chart of a query algorithm of the present invention;
fig. 9 is a protocol relationship diagram according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
As shown in fig. 2, the system for network packet multi-layer protocol identification provided by the present invention includes three modules: the device comprises a data input module, an analysis module and a query module.
The data input module comprises:
reading data packets from a data source, stripping a known lower layer protocol, extracting an unknown load, and outputting a plurality of data packets, wherein each data packet comprises the unknown load and known meta information of the lower layer protocol. The data source may be, but is not limited to, capture from a network card, read from a file in real time.
II, an analysis module:
1. a data segment protocol detection submodule is constructed, and the feature keyword rule can be constructed manually by utilizing the existing DPI software package or the proprietary protocol to be detected.
2. Extracting continuous data blocks from the output of the data input module, performing protocol detection on each continuous data block fragment, counting adjacent probability of protocols, and constructing a protocol relation graph. Wherein, the operation of judging which protocol a data fragment is defined as protocol detection.
As shown in fig. 3, the analysis module works as follows:
step 2.1, segmenting the continuous data block into data segments with fixed size (such as 256 bytes), calling a data segment protocol detection submodule to execute protocol detection on each data segment, and generating a classification sequence containing a plurality of classifications, such as [ P1, P2 ];
step 2.2, insert a special classification P0 at the head of the classification sequence, the special classification P0 represents the known classification at the upper level, resulting in a classification sequence [ P0, P1, P2 ].
3.1, establishing a matrix of M x M by using all different classifications, wherein M is the number of all the different classifications, and all the initial matrixing values are 0;
and 3.2, according to the result obtained by executing the protocol detection on each data fragment, adding 1 to the value of each two adjacent classifications in the classification sequence at the corresponding position of the matrix, if the detection result is P1 or P2, adding 1 to the value of the matrix P1 or P2, and if the detection result is P0, P1 or P2, adding 1 to the value of the matrix P0 or P1 or P1 or P2.
And 4, converting the matrix into a graph, wherein the nodes of the graph correspond to the protocol, the weights of the edges correspond to the values in the matrix, and the root node of the graph is the special classification P0.
Fig. 4 to 7 are flow charts of data conversion of the analysis module, which correspond to the data results output in the steps 1 to 4.
Fig. 4 shows the number of data blocks and the corresponding data block data obtained in step 1, and the data is represented by 16-ary notation. The data in fig. 4 is processed in step 2 to obtain the data block number and the corresponding protocol path in fig. 5. And (3) counting the data in the figure 5 through the step 3 to obtain a matrix of the adjacent relation times of each protocol shown in the figure 6. In conjunction with the data of fig. 5 and fig. 6, the protocol relationship diagram represented in fig. 7 is transformed through step 4, the weight of the edge in the diagram is the value of the corresponding cell in fig. 6, and the root node is the inserted a.
Thirdly, the query module:
executing the query algorithm according to the query requirement of the user, analyzing the protocol relation graph, and outputting a result meeting the query requirement.
The query requirement input by the user may contain the following conditions:
the number of protocol levels required, denoted by D; the number of the child nodes reserved at most in each layer is represented by K; the edge probability threshold (corresponding to the edge value in fig. 7) is denoted by W.
Query algorithm flow as shown in fig. 8, this is a variation of the Breadth First Search (BFS) algorithm:
1. the initialization queue Q is empty, the initialized visited node set V is empty, and the initialization result node set TN and the result edge set TE are empty;
2. firstly, putting a root node into a queue Q;
3. taking out the node N from the head of the queue Q;
3.1, adding the node N into the TN, and adding the node N into the V;
3.2, judging whether the node reaches the depth D or not, and if the current layer number is more than or equal to D, finishing the step;
3.3, otherwise, executing the following steps;
3.3.1, performing the following two filtering on the child node list of the node;
3.3.1.1, the child node is not in set V;
3.3.1.2, the weight from the current node to the child node is more than or equal to W;
3.3.2, sorting the edges of the node to the child nodes according to the filtered result, and taking out the largest K edges which are expressed by TOP _ CHILDREN;
3.3.3, add the edge of this node to TOP CHILDREN to TE. Adding TOP _ CHILDREN to queue Q;
4. if the queue is empty, returning TN and TE as results to the user, otherwise, repeating the step 3.
To facilitate understanding by those skilled in the art, the present invention provides a specific implementation as follows:
1. providing a webpage-based interface and providing an uploading file form.
2. After a PCAP file uploaded by a user is uploaded, analyzing the known Ethernet/IP/TCP layer information, extracting the load of the TCP upper layer, and generating a plurality of data blocks. And generating a complete protocol relation diagram through the analysis module and displaying the complete protocol relation diagram to a user.
3. Providing a form on the web page for the user to input query conditions, including the following conditions:
the number of protocol levels required, denoted by D;
the number of the child nodes reserved at most in each layer is represented by K;
an edge probability threshold (corresponding to the value of an edge in the graph), denoted by W;
4. and executing the query of the user, outputting a subgraph which accords with the query, and displaying the subgraph to the user.
Fig. 9 is a protocol relationship diagram analyzed in step 2 of the above process, which includes 5 protocols a, B, C, F, and E, and the root node is a.
Suppose that the input query condition is D-3, K-1, and W-0.
And adding the root node A into the queue to be traversed.
And taking out a node from the queue to be traversed, wherein the node is a node A, the level of the node A is 1 and is smaller than D, adding the node A into a result set, the weights of three child nodes F, B and C of the node A are respectively 5, 100 and 50, and the results from high to low after sorting are B, C and F.
Since K ═ 1, only node B is reserved, the edge a- > B is added to the result set. And adding B into the queue to be traversed.
And taking out a node from the queue to be traversed, wherein the node is a node B, the level of the node B is 2, and is smaller than D, and the node B is added into the result set.
And the child nodes C and E of the B have the weights of 30 and 5 respectively, the result from high to low is C and E after the sorting, only the node C is reserved because K is 1, and the edge of B → C is added into the result set. And adding C into the queue to be traversed.
And taking out a node from the queue to be traversed, namely a node C, and adding the node C into the result set because the level of the node C is 3 and is equal to D. But no longer traverses its children.
By this point the queue to be traversed is already empty and the query is complete. The result set is the subtree A → B → C, marked in FIG. 9 with a bolded edge.
Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, units provided by the present invention as pure computer readable program code, the system and its various devices, modules, units provided by the present invention can be fully implemented by logically programming method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units included in the system for realizing various functions can also be regarded as structures in the hardware component; means, modules, units for performing the various functions may also be regarded as structures within both software modules and hardware components for performing the method.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (4)
1. A system for multi-layer protocol identification of network packets, comprising:
a data input module: reading data packets from a data source, stripping a known lower layer protocol, extracting an unknown load, and outputting a plurality of data packets, wherein each output data packet comprises the unknown load and known meta-information of the lower layer protocol;
an analysis module: extracting continuous data blocks from the output of the data input module, executing protocol detection on each continuous data block fragment, and counting the adjacent probability of the protocol according to the protocol detection result;
the analysis module performs operations comprising:
step 2, executing the following operations for each continuous data block:
step 2.1, segmenting continuous data blocks into data segments with fixed sizes, calling a data segment protocol detection submodule to execute protocol detection on each data segment, and generating a classification sequence comprising a plurality of classifications;
step 2.2, inserting a special classification P0 at the head of the classification sequence, wherein the special classification P0 represents the known classification at the upper layer, and generating a classification sequence [ P0, P1, P2 ];
step 3, counting and generating a protocol adjacent relation matrix, comprising the following steps:
3.1, establishing a matrix of M x M by using all different classifications, wherein M is the number of all the different classifications, and all the initial matrixing values are 0;
step 3.2, according to the result obtained by executing protocol detection on each data fragment, adding 1 to the value of each two adjacent classifications in the classification sequence at the corresponding position of the matrix, if the detection result is P1 or P2, adding 1 to the value of the matrix P1 or P2, and if the detection result is P0, P1 or P2, adding 1 to the value of the matrix P0 or P1 or P1 or P2;
and 4, converting the matrix into a graph, wherein the nodes of the graph correspond to the protocol, the weights of the edges correspond to the values in the matrix, and the root node of the graph is the special classification P0.
2. The system for multi-layer protocol identification of network packets of claim 1, further comprising a query module: providing a user interface, executing a query algorithm according to a query requirement input by a user, and outputting a query result.
3. A method for network packet multi-layer protocol identification, comprising:
a data input step: reading data packets from a data source, stripping a known lower layer protocol, extracting an unknown load, and outputting a plurality of data packets, wherein each output data packet comprises the unknown load and known meta-information of the lower layer protocol;
and (3) an analysis step: extracting continuous data blocks from the output of the data input step, executing protocol detection on each continuous data block fragment, and counting the adjacent probability of the protocol according to the protocol detection result;
the analyzing step comprises:
step 2, executing the following operations for each continuous data block:
step 2.1, segmenting continuous data blocks into data segments with fixed sizes, calling a data segment protocol detection submodule to execute protocol detection on each data segment, and generating a classification sequence comprising a plurality of classifications;
step 2.2, inserting a special classification P0 at the head of the classification sequence, wherein the special classification P0 represents the known classification at the upper layer, and generating a classification sequence [ P0, P1, P2 ];
step 3, counting and generating a protocol adjacent relation matrix, comprising the following steps:
3.1, establishing a matrix of M x M by using all different classifications, wherein M is the number of all the different classifications, and all the initial matrixing values are 0;
step 3.2, according to the result obtained by executing protocol detection on each data fragment, adding 1 to the value of each two adjacent classifications in the classification sequence at the corresponding position of the matrix, if the detection result is P1 or P2, adding 1 to the value of the matrix P1 or P2, and if the detection result is P0, P1 or P2, adding 1 to the value of the matrix P0 or P1 or P1 or P2;
and 4, converting the matrix into a graph, wherein the nodes of the graph correspond to the protocol, the weights of the edges correspond to the values in the matrix, and the root node of the graph is the special classification P0.
4. The method of network packet multi-layer protocol identification as claimed in claim 3, further comprising the step of querying: providing a user interface, executing a query algorithm according to a query requirement input by a user, and outputting a query result.
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| CN110661682B (en) * | 2019-09-19 | 2021-05-25 | 上海天旦网络科技发展有限公司 | Automatic analysis system, method and equipment for universal interconnection data |
| CN113364647B (en) * | 2021-06-03 | 2022-10-11 | 上海天旦网络科技发展有限公司 | Rapid protocol stack identification method and system based on multitask network |
| CN114024868B (en) * | 2022-01-06 | 2022-03-25 | 北京安博通科技股份有限公司 | Flow statistical method, flow quality analysis method and device |
| CN114640611A (en) * | 2022-03-09 | 2022-06-17 | 西安电子科技大学 | Unknown heterogeneous industrial protocol detection and identification method, system, equipment and medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101707532A (en) * | 2009-10-30 | 2010-05-12 | 中山大学 | Automatic analysis method for unknown application layer protocol |
| WO2011143817A1 (en) * | 2010-05-19 | 2011-11-24 | 阿尔卡特朗讯 | Method and apparatus for identifying application protocol |
| CN103003792A (en) * | 2010-06-10 | 2013-03-27 | 美光科技公司 | Analyzing data using a hierarchical structure |
| CN104935567A (en) * | 2015-04-20 | 2015-09-23 | 中国电子科技集团公司第二十九研究所 | Unknown protocol message format deduction method |
| CN105100091A (en) * | 2015-07-13 | 2015-11-25 | 北京奇虎科技有限公司 | Protocol identification method and protocol identification system |
-
2017
- 2017-12-12 CN CN201711322465.9A patent/CN108234452B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101707532A (en) * | 2009-10-30 | 2010-05-12 | 中山大学 | Automatic analysis method for unknown application layer protocol |
| WO2011143817A1 (en) * | 2010-05-19 | 2011-11-24 | 阿尔卡特朗讯 | Method and apparatus for identifying application protocol |
| CN103003792A (en) * | 2010-06-10 | 2013-03-27 | 美光科技公司 | Analyzing data using a hierarchical structure |
| CN104935567A (en) * | 2015-04-20 | 2015-09-23 | 中国电子科技集团公司第二十九研究所 | Unknown protocol message format deduction method |
| CN105100091A (en) * | 2015-07-13 | 2015-11-25 | 北京奇虎科技有限公司 | Protocol identification method and protocol identification system |
Non-Patent Citations (2)
| Title |
|---|
| "利用统计特征的网络应用协议识别方法";徐莉;;《西安交通大学学报》;20090210;全文 * |
| "基于DPI的流量识别与控制系统的研究与设计";程博;《中国优秀硕士学位论文全文数据库-信息科技辑》;20150815;全文 * |
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