CN109302381B - Radius attribute extension method, device, electronic equipment and computer readable medium - Google Patents
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Abstract
The application provides a Radius attribute extension method, a Radius attribute extension device, electronic equipment and a computer readable medium, which relate to the technical field of communication and comprise the following steps: acquiring a Radius interactive message, wherein the Radius interactive message is an authentication request message, an authentication response message, a charging start request message, a charging start response message, a charging stop request message or a charging end response message; performing feature extraction on the Radius interactive message to obtain a target attribute field for representing the Radius private extended attribute; inputting the target attribute field into a target neural network to determine whether the Radius private extended attribute represented by the target attribute field is a newly added Radius private extended attribute; if the new Radius private extended attribute is determined to be added to the Radius private dictionary, automatic learning and automatic addition of the Radius private extended attribute can be achieved through the method provided by the application, and the technical problem that automatic learning and addition of the private extended attribute cannot be achieved in the prior art is solved.
Description
Technical Field
The present application relates to the field of communications technologies, and in particular, to a Radius attribute extension method, apparatus, electronic device, and computer readable medium.
Background
Radius (remote authentication Dial In User service), is an abbreviation for remote authentication Dial-up User service. In the field of communications, clients are typically switches/routers; the server side is a Radius server, the types of the Radius servers are numerous at present, each manufacturer has the Radius server of the manufacturer, and the Radius servers of an operator and a third party are also provided.
Radius has a plurality of Attributes fields (attribute fields), and different functions of a user after the user is on line are achieved by carrying different attribute fields. With the increasing use scenes of users, in order to meet the increasing new requirements, manufacturers expand the property No. 26 Vendor-Specific in the Attributes domain of Radius, which means that the further expanded property of each manufacturer is not standard/uniform.
Taking the speed limit function as an example, different companies have different rate fields, which means that the Radius server can issue/identify different rate fields for different companies. Various vendors are currently expanding on the Vendor-Specific attribute. This requires that the Radius server needs to continuously add the Radius private dictionary files of various manufacturers to match the communication devices of various manufacturers, and the process of updating the private dictionary by the Radius server is often passive and needs manual participation.
If the Radius server of the third party does not update the Radius private dictionary of a certain company, the situation that the function of the user is invalid after the user is online when the communication equipment of the company is docked can be caused. In the prior art, a Radius server manufacturer adds a Radius private dictionary of an equipment manufacturer in a manual mode; the manual addition process is often passive, i.e., after a problem is discovered, the device manufacturer notifies the Radius server manufacturer to update its private dictionary to support the new function.
Disclosure of Invention
In view of the above, an object of the present application is to provide a Radius attribute extension method, apparatus, electronic device and computer readable medium, so that the device automatically learns and automatically adds a Radius private extension attribute.
In a first aspect, an embodiment of the present application provides a Radius attribute extension method, including: acquiring a Radius interactive message, wherein the Radius interactive message is an authentication request message, an authentication response message, a charging start request message, a charging start response message, a charging stop request message or a charging end response message; performing feature extraction on the Radius interactive message to obtain a target attribute field for representing the Radius private extended attribute; inputting the target attribute field into a target neural network to determine whether the Radius private extended attribute represented by the target attribute field is a newly added Radius private extended attribute; and if so, adding the newly added Radius private extension attribute into a Radius private dictionary.
Further, adding the newly added Radius private extension attribute to a Radius private dictionary comprises: verifying the correctness of the newly added Radius private extended attribute; and if the verification is passed, adding the newly added Radius private extension attribute into a Radius private dictionary.
Further, verifying the correctness of the newly added Radius private extension attribute includes: acquiring a simulated Radius interaction message carrying the target attribute field, which is sent by a Radius client, wherein the simulated Radius interaction message comprises at least one of the following: simulating an authentication request message, a charging start request message and a charging stop request message; verifying the legality of the target attribute field carried in the simulated Radius interactive message based on the attribute field in the simulated Radius private dictionary, wherein the target attribute field legally indicates that the attribute type, the attribute name and the attribute value of the target attribute field can be found in the simulated Radius private dictionary; and if the verification is legal, verifying that the target attribute field is correct.
Further, the target neural network is determined by: deploying an initial neural network; acquiring a training sample message, wherein the training sample message is a Radius interaction message; extracting a Radius private extended attribute field from the training sample message, wherein the Radius private extended attribute field comprises: attribute type, attribute name and attribute value; and taking the extracted Radius private extended attribute field and the corresponding label as the input of an initial neural network, and training the initial neural network to obtain the target neural network.
Further, the inputting the target attribute field into the target neural network to determine whether the Radius private extended attribute represented by the target attribute field is a newly added Radius private extended attribute includes: inputting the target attribute field into a target neural network to obtain a corresponding label; inquiring recorded Radius private extended attribute fields corresponding to the tags, and determining whether the recorded Radius private extended attribute fields same as the target attribute fields exist or not; and if the attribute does not exist, determining that the Radius private extended attribute represented by the target attribute field is the newly added Radius private extended attribute.
In a second aspect, an embodiment of the present application further provides a Radius attribute extension apparatus, including: the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a Radius interaction message, wherein the Radius interaction message is an authentication request message, an authentication response message, a charging start request message, a charging start response message, a charging stop request message or a charging end response message; the characteristic extraction unit is used for extracting the characteristics of the Radius interactive message to obtain a target attribute field for representing the Radius private extended attribute; the determining unit is used for inputting the attribute field into a target neural network so as to determine whether the Radius private extended attribute represented by the target attribute field is a newly added Radius private extended attribute; and the adding unit is used for adding the newly added Radius private extension attribute into the Radius private dictionary if the determination is positive.
Further, the adding unit includes: the verification module is used for verifying the correctness of the newly added Radius private extended attribute; and the adding module is used for adding the newly added Radius private extension attribute into the Radius private dictionary if the verification is passed.
Further, the verification module is to: acquiring a simulated Radius interaction message carrying the target attribute field, which is sent by a Radius client, wherein the simulated Radius interaction message comprises at least one of the following: simulating an authentication request message, a charging start request message and a charging stop request message; verifying the legality of the target attribute field carried in the simulated Radius interactive message based on the attribute field in the simulated Radius private dictionary, wherein the target attribute field legally represents that the attribute type, the attribute name and the attribute value of the target attribute field can be found in the simulated Radius private dictionary; and if the verification is legal, verifying that the target attribute field is correct.
Further, the apparatus is further configured to determine the target neural network by: deploying an initial neural network; acquiring a training sample message, wherein the training sample message is a Radius interaction message; extracting an attribute field of Radius private extended attribute in the training sample message, wherein the attribute field of Radius private extended attribute comprises: attribute type, attribute name and attribute value; and taking the extracted Radius private extended attribute field and the corresponding label as the input of an initial neural network, and training the initial neural network to obtain the target neural network.
Further, the determining unit is configured to: inputting the target attribute field into a target neural network to obtain a corresponding label; inquiring recorded Radius private extended attribute fields corresponding to the tags, and determining whether the recorded Radius private extended attribute fields same as the target attribute fields exist or not; and if the attribute field does not exist, determining that the Radius private extended attribute represented by the target attribute field is the newly added Radius private extended attribute.
In a third aspect, an embodiment of the present application further provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the method according to any one of the above first aspects when executing the computer program.
In a fourth aspect, the present embodiments also provide a computer-readable medium having a non-volatile program code executable by a processor, where the program code causes the processor to execute the method of any one of the above first aspects.
In the embodiment of the application, firstly, a Radius interactive message is obtained, and then, the characteristic extraction is carried out on the Radius interactive message to obtain a target attribute field for representing the Radius private extended attribute; and finally, inputting the target attribute field into a target neural network to determine whether the Radius private extended attribute corresponding to the target attribute field is the newly added Radius private extended attribute, wherein if the determination is yes, the newly added Radius private extended attribute is added into a Radius private dictionary.
As can be seen from the above description, in this embodiment, the newly added Radius private extension attribute is automatically identified through the neural network, and the Radius private extension attribute is automatically added to the Radius Server private dictionary for subsequent use. According to the method, the Radius private extended attribute does not need to be added manually, related equipment does not need to pay attention to the Radius private extended attribute of each company, the newly added Radius private extended attribute of each company can be identified and added, labor is saved, meanwhile, the maintenance cost of a project is reduced, the technical problems that in the prior art, due to the fact that the Radius private extended attribute is added manually, timeliness is poor and efficiency is low are solved, and the technical effects that the equipment learns automatically and the Radius private extended attribute is added automatically are achieved.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the detailed description of the present application or the technical solutions in the prior art, the drawings needed to be used in the detailed description of the present application or the prior art description will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a Radius attribute expansion method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a networking system according to an embodiment of the present application;
fig. 3 is a flowchart of a first optional Radius attribute expansion method according to an embodiment of the present application;
fig. 4 is a flowchart of a second alternative Radius attribute expansion method according to an embodiment of the present application;
fig. 5 is a schematic diagram of a Radius attribute expanding apparatus according to an embodiment of the present application;
fig. 6 is a schematic diagram of an electronic device according to an embodiment of the application.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The first embodiment is as follows:
in accordance with an embodiment of the present application, there is provided an embodiment of a method for Radius attribute expansion, where it is noted that the steps illustrated in the flowchart of the figure may be performed in a computer system such as a set of computer-executable instructions, and that while a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a flowchart of a Radius attribute expanding method according to an embodiment of the present application, and as shown in fig. 1, the method includes the following steps:
step S102, obtaining a Radius interactive message, wherein the Radius interactive message is an authentication request message, an authentication response message, a charging start request message, a charging start response message, a charging stop request message or a charging end response message;
step S104, extracting the characteristics of the Radius interactive message to obtain a target attribute field for representing the Radius private extended attribute;
step S106, inputting the target attribute field into a target neural network to determine whether the Radius private extended attribute represented by the target attribute field is a newly added Radius private extended attribute;
and S108, if the determination is positive, adding the newly added Radius private extension attribute into a Radius private dictionary.
As can be seen from the above description, in this embodiment, the newly added Radius private extension attribute is automatically identified through the neural network, and the Radius private extension attribute is automatically added to the Radius Server private dictionary for subsequent use. According to the method, the Radius private extended attribute does not need to be added manually, related equipment does not need to pay attention to the Radius private extended attribute of each company, the newly added Radius private extended attribute of each company can be identified and added, labor is saved, meanwhile, the maintenance cost of a project is reduced, the technical problems that in the prior art, due to the fact that the Radius private extended attribute is added manually, timeliness is poor and efficiency is low are solved, and the technical effects that the equipment learns automatically and the Radius private extended attribute is added automatically are achieved.
Specifically, the Radius client provides services for accessing and interacting with a Radius server to a remote access user; the Radius server stores the identity information, authorization information and access records of the user, and performs authentication, authorization and charging services for the user.
The interaction process between the Radius client and the Radius server is described as follows:
the user accesses a Radius client, and the Radius client sends an authentication request message to a Radius server, wherein the authentication request message contains user information, and the user information comprises: user name, password, etc. The Radius server checks the validity of the user name and the password; and if the authentication response message is legal, returning an authentication response message to the Radius client, allowing the user to perform the next work, and otherwise, returning a data packet which is denied to access, and denying the user to access. If the access is allowed, the Radius client side puts forward a charging request message (comprising a charging start request message and a charging stop request message) to the Radius server, the Radius server feeds back a charging response message (a charging start response message and a charging stop response message) to the Radius client side, and meanwhile, the user can perform relevant operations.
Fig. 2 is a schematic diagram of a networking according to an embodiment of the present application, and as shown in fig. 2, it is assumed that the Radius client is a Switch, and the Switch is connected to the Radius server through a Switch a. As shown in fig. 2, the system further includes a Radius function platform, where the Radius function platform may be disposed in a Radius server, may also be disposed in a Radius client, or may also be disposed on another device except the Radius client and the Radius server, where the device accesses a networking to which the Radius client and the Radius server belong through a bypass mount mode, which is not specifically limited in this embodiment.
The Radius interaction packet in step S102 refers to an interaction packet of the Radius client and the Radius server in the interaction process, and specifically includes interaction packets in the authentication stage and the charging stage. For example, in the authentication and authorization stage, a user sends an authentication-request (access-request) message to a Radius server through a Radius client; after obtaining the authentication request message, the Radius server verifies the validity of the user, and if the user is verified to be a valid user, the Radius server sends the authority information of the user to the Radius client through an authentication-response message (access-accept), wherein the authentication-response message includes the relevant configuration parameters of the user. At this time, the authentication request message and the authentication response message are interaction messages between the Radius client and the Radius server in the authentication and authorization stage. In the charging stage, if the user can be accessed, the Radius client sends a charging start request message (counting-request) to the Radius server, the Radius server returns a charging start response message (counting-response), the Radius client sends a charging stop request message (counting-request) to the Radius server, and the Radius server returns a charging end response message (counting-response). At this time, the charging start request message, the charging start response message, the charging stop request message, and the charging end response message are the interaction messages between the Radius client and the Radius server in the charging stage.
That is to say, in this embodiment, the Radius function platform shown in fig. 2 obtains the interaction packet of the Radius client and the Radius server in the authentication phase and the charging phase, and performs the methods described in step S104 to step S108 on the Radius interaction packet.
After the Radius interaction message is acquired, feature extraction can be performed on the Radius interaction message to extract a target attribute field, wherein the target attribute field is used for representing the Radius private extended attribute.
After the target attribute field is extracted, the target attribute field can be input into a target neural network to determine whether the Radius private extended attribute represented by the target attribute field is the newly added Radius private extended attribute. And if so, adding the newly added Radius private extension attribute into the Radius private dictionary. In this embodiment, the target attribute field input into the target neural network includes: attribute type, attribute value, and attribute name.
The Radius attribute can be divided into two types, namely a Radius standard attribute and a Radius private extension attribute. The standard attributes specified in the Radius standard attributes are basically supported by all mainstream device manufacturers. The attribute number 26 (Vendor-Specific) defined in the Radius standard attribute is used for the device Vendor to extend the Radius attribute to implement functions not defined in the Radius standard attribute (i.e., private extended attributes). For example, a peak rate of user access to a NAS (Network Attached Storage) server, an average rate of user access to a NAS server, and so on.
As is apparent from the description of the background art, in the related art, the Radius private extension attribute of each company is generally added to the Radius private dictionary (i.e., the Radius private dictionary in step S108) in a manual manner. Since the Radius private extended attribute set in the attribute No. 26 (Vendor-Specific) of each company is different, the manual adding method requires a relevant person to add the newly added Radius private extended attribute of each company. When there are many companies and the number of the Radius private extended attributes added by each company is large, the added Radius private extended attributes cannot be timely and accurately added to the Radius private dictionary. In order to solve the problem, the embodiment provides a method for expanding the Radius private attributes, by which the newly added Radius private expanded attributes of each company can be automatically identified and added through a Radius function platform, so that the labor is saved, and the maintenance cost of a project is reduced.
It should be noted that, in this embodiment, before the target attribute field is analyzed and processed by using the target neural network, the initial neural network of the target neural network needs to be trained to obtain the target neural network. After the training precision of the initial neural network meets the requirement, the target neural network can be adopted to carry out prediction analysis on the target attribute field. The learning (or training) process of the initial neural network will be described below.
In an alternative embodiment, as shown in FIG. 3, the initial neural network is learned (or trained) in the manner described by the following steps:
step S301, deploying an initial neural network;
step S302, obtaining a training sample message, wherein the training sample message is a Radius customer interaction message;
step S303, extracting a Radius private extended attribute field from the training sample message, where the Radius private extended attribute field includes: attribute type, attribute name and attribute value;
and step S304, taking the extracted Radius private extended attribute field as the input of an initial neural network, and training the initial neural network to obtain the target neural network.
In this embodiment, a neural network is deployed first, which is called as an initial neural network, and after the initial neural network is deployed, the initial neural network may be set in a Radius function platform, and the Radius function platform is set in a Radius client or a Radius server; or the Radius function platform is arranged on other equipment except the Radius client and the Radius server, wherein the equipment is accessed to the networking to which the Radius client and the Radius server belong through a bypass mounting mode. Then, the operations of authentication, authorization, charging and the like of the user can be realized through the Radius client or the Radius server. In the process of executing the operation, a Radius interactive message (namely, a training sample message) between a Radius client and a Radius server in the process of executing the operation is obtained through a Radius function platform. Then, the Radius function platform firstly preprocesses the acquired Radius interactive message to extract Radius private extended attribute words which can be used for classification from the Radius interactive message.
And then, taking the extracted Radius private extended attribute field and a label corresponding to the field as input of an initial neural network, and training the initial neural network, wherein the label is a label of a function (for example, a speed limit function) for representing the Radius private extended attribute. Through an initial neural network full-connection layer (a plurality of layers), nonlinear combination is carried out between input feature vectors, and local features are completely reassembled; and finally, carrying out function classification on the characteristics after the characteristics are completely reassembled through a Softmax function of an initial neural network output layer, thereby learning the Radius private extended attribute field related to each functional model. Such as: the Radius private extended attribute fields included in the speed limit function learned by the initial neural network are as follows: Input-Peak-Rate, Input-Average-Rate, Input-Basic-Rate.
Specifically, the training (or learning) process of the initial neural network is described as follows:
1. selecting a sample set (Ai, Ci), wherein Ai is a Radius private extended attribute field (including attribute type, attribute name and attribute value), and Ci is a label of the Radius private extended attribute field, for example, a label of a function (for example, speed limit function) for representing the Radius private extended attribute;
2. sending the sample into an initial neural network, and calculating the actual output Y of the initial neural network;
3. calculating D-Ci-Y (namely, the difference between a predicted value and an actual value, Ci is the actual value, and Y is the predicted value);
4. adjusting a weight matrix W of the initial neural network according to the error D;
5. the above process is repeated for each sample in the sample set until the error does not exceed the specified range for the entire sample set.
It should be noted that, in this embodiment, after the initial neural network is trained through the training process, the attribute fields of each extended attribute can be initially learned through the network, so as to obtain the target neural network.
And then, inputting the target attribute field into a target neural network, so that the target neural network analyzes the input target attribute field, and the label (namely, the functional model) of the target attribute field can be determined. And further, inquiring the recorded Radius private extended attribute field corresponding to the label, and comparing the target attribute field with the recorded Radius private extended attribute field corresponding to the label to determine whether the recorded Radius private extended attribute field corresponding to the label has the same Radius private extended attribute field as the target attribute field. If the attribute exists, the newly added Radius private extension attribute is determined, and the newly added Radius private extension attribute can be added into a Radius private dictionary (Radius Server private dictionary).
Suppose that a company adds a Radius private extended attribute to the attribute No. 26 (Vendor-Specific), for example, adds a Radius private extended attribute Input-Low-Rate to the speed limit function. And determining the corresponding speed-limiting function model through the target neural network, and determining that the Input-Low-Rate is the newly added Radius private extended attribute if the field Input-Low-Rate does not exist in the existing Radius private extended attribute corresponding to the speed-limiting function model.
In an optional implementation manner, as shown in fig. 4, in step S108, adding the newly added Radius private extension attribute to the Radius private dictionary includes the following steps:
step S401, verifying the correctness of the newly added Radius private extended attribute;
step S402, if the verification is passed, adding the newly added Radius private extension attribute into a Radius private dictionary.
If the target attribute field is the newly added Radius private extended attribute, the attribute type, the attribute value and the attribute name of the target attribute field are not stored in the Radius private dictionary of the Radius server. Therefore, the Radius server cannot identify the target attribute field, and a new feature function of the device cannot be used, and the Radius interaction message is an illegal message.
According to the scheme provided by the embodiment of the application, after the target attribute field is determined to be the Radius private extended attribute field, the newly added Radius private extended attribute needs to be added into the Radius private dictionary. However, before adding the learned new Radius private extension attribute to the Radius private dictionary, the correctness of the learned target attribute field needs to be checked. In this embodiment, the Radius server may be simulated to interact with the Radius client through the Radius function platform to check the correctness of the target attribute field, where the correctness means that after the target attribute field is added to the Radius private dictionary of the Radius server, the Radius server can identify the target attribute field and provide a new feature function of the target attribute field for the user. And if the verification is passed, adding the newly added Radius private extension attribute into the Radius private dictionary.
In an optional embodiment, the verifying the correctness of the newly added Radius private extension attribute includes the following steps:
step S4011, obtaining a simulated Radius interaction packet carrying the target attribute field sent by a Radius client, where the simulated Radius interaction packet includes at least one of: simulating an authentication request message, a charging start request message and a charging stop request message;
in order to verify the correctness of the newly added Radius private extended attribute, the interaction process of the Radius client and the Radius server can be simulated. For example, the authentication phase of the interactive process may be simulated, or the charging phase of the interactive process may be simulated, and the authentication request message in the simulated authentication phase, the charging start request message in the simulated charging phase, and the charging stop request message in the simulated charging phase are the simulated Radius interactive messages. In order to distinguish from the authentication request message, the charging start request message, and the charging stop request message in the normal authentication phase and the charging phase, they are respectively referred to as a simulated authentication request message, a simulated charging start request message, and a simulated charging stop request message.
Step S4012, verifying validity of a target attribute field carried in a simulated Radius interaction message based on an attribute field in a simulated Radius private dictionary, wherein the target attribute field is stored in the simulated Radius private dictionary in advance, and legally indicates that an attribute type, an attribute name and an attribute value of the target attribute field can be found in the simulated Radius private dictionary;
step S4013, if the verification is legal, the target attribute field is verified to be correct.
In this embodiment, the Radius function platform includes an analog Radius private dictionary, and the analog Radius private dictionary is constructed by a Radius private dictionary in an analog Radius server. If it is determined in the verification step S106 that the Radius private extended attribute represented by the target attribute field is the newly added Radius private extended attribute, the target attribute field may be added to the simulated Radius private dictionary. When the target attribute field is verified, the Radius function platform firstly acquires a simulated Radius interaction message carrying the target attribute field, wherein the simulated Radius interaction message can be a message acquired in an authentication stage and a charging stage, and comprises at least one of the following: the method comprises the steps of simulating an authentication request message, a charging starting request message and a charging stopping request message. That is, one or more messages in the Radius interaction message carry the target attribute field.
And then, checking the legality of the target attribute field carried in the simulated Radius interaction message. The process for verifying the validity of the target attribute field in the simulated Radius interaction message comprises the following steps: and the Radius function platform acquires the attribute domain data of the simulated Radius interactive message, further acquires a target attribute field from the attribute domain, and then can verify the target attribute field based on the attribute field in the simulated Radius private dictionary. And if the verification is passed, verifying that the target attribute field is correct, wherein the verification is passed that the Radius function platform can identify the target attribute field based on the simulated Radius private dictionary. A list of attribute types, a list of attribute values, and a list of attribute names are specified in the simulated Radius private word. When checking a target attribute field in the simulated Radius interaction message, checking that an attribute value of the target attribute field in the simulated Radius interaction message is in a corresponding value list, an attribute type of the target attribute field is in a corresponding attribute type list, and an attribute name of the target attribute field is in a corresponding attribute name list. If the check is passed, the Radius server can identify the target attribute field, which indicates that the target attribute field can be normally used between the user and the Radius server. At this point, the target attribute field is indicated to be correct.
Optionally, if the target attribute field is extracted from a Radius interaction packet (i.e., an authentication request packet) in the authentication phase, when it is determined through the target neural network that the Radius private extended attribute represented by the target attribute field is the newly added Radius private extended attribute, verifying the newly added Radius private extended attribute includes:
the Radius client sends an authentication request message (access-request) carrying a target attribute field to the Radius functional platform, and then the Radius functional platform checks the validity of the authentication request message carrying the target attribute field, wherein the validity refers to: the Radius client sending the authentication request message is a valid client, and the target attribute field in the authentication request message is valid (or correct).
Optionally, if the target attribute field is extracted from a Radius interaction packet (e.g., a charging start request packet or a charging stop request packet) in a charging stage, when it is determined through a target neural network that the Radius private extended attribute represented by the target attribute field is the newly added Radius private extended attribute, verifying the newly added Radius private extended attribute includes:
the Radius client sends an authentication request message to the Radius functional platform, then the Radius functional platform authenticates the authentication request message, and if the authentication is passed (namely the client sending the authentication request message is a legal client), an authentication response message is sent to the Radius client. And acquiring a charging request message carrying a target attribute field sent by the Radius client, and checking the legitimacy of the target attribute field in the charging request message based on the attribute field in the analog Radius private dictionary (since the client is verified to be legitimate by the authentication request message, the legitimacy of the client of the charging request message is not required to be verified, and only the legitimacy of the target attribute field is required to be verified), wherein the charging request message comprises a charging start request message and a charging end request message. And if the verification is legal, verifying that the attribute of the newly added Radius private extension is correct.
In an optional embodiment, the verifying the correctness of the newly added Radius private extension attribute further includes: and if the target neural network detects the target attribute field for the first time, verifying the correctness of the target attribute field.
Since there may be a plurality of clients sending messages to the Radius server at the same time, there may be messages containing the same target attribute field between any two or more clients. For this situation, in this embodiment, if the target attribute field is detected for the first time, the correctness of the target attribute field is verified. If the correctness of the target attribute field is not first detected, the correctness of the target attribute field is not verified.
In an optional embodiment, adding the newly added Radius private extension attribute to the Radius private dictionary includes:
and sending notification information to the Radius server so that the Radius server adds the newly added Radius private extended attribute to a Radius private dictionary.
Specifically, after the Radius function platform verifies that the newly added Radius private extension attribute is correct, the newly added Radius private extension attribute can be written into a Radius Server private dictionary for subsequent use.
As can be seen from the above description, in this embodiment, a Radius attribute extension method is provided, and the method combines Radius packet feature extraction and a neural network technology to identify a newly added Radius private extension attribute. In the method, in a Radius authentication and charging process, Radius private attribute fields in a Radius message are analyzed, the mutually independent Radius private attribute fields are sent into a neural network, the neural network carries out function classification on the characteristics, and the Radius private attribute fields related to each function are learned. And subsequently, for the newly added Radius private extended attribute, the neural network learns the newly added Radius private extended attribute according to the trained function model, simulates message interaction, verifies the correctness of the newly added Radius private extended attribute, and adds the newly added Radius private extended attribute into a private dictionary of a Radius Server for subsequent use under the condition that the verification is passed.
As can be seen from the above description, in this embodiment, the newly added Radius private extension attribute is automatically identified through the neural network, and the Radius private extension attribute is automatically added to the Radius Server private dictionary for subsequent use. According to the method, the Radius private extended attribute does not need to be added manually, related equipment does not need to pay attention to the Radius private extended attribute of each company, the newly added Radius private extended attribute of each company can be identified and added, labor is saved, meanwhile, the maintenance cost of a project is reduced, the technical problems that in the prior art, due to the fact that the Radius private extended attribute is added manually, timeliness is poor and efficiency is low are solved, and the technical effects that the equipment learns automatically and the Radius private extended attribute is added automatically are achieved.
Example 2:
the Radius attribute extension method is further described below in conjunction with fig. 2. Fig. 2 is a schematic diagram of a networking according to an embodiment of the present application, and as shown in fig. 2, it is assumed that the Radius client is a Switch, and the Switch is connected to the Radius server through a Switch a. As shown in fig. 2, the system further includes a Radius function platform, where the Radius function platform may be disposed in a Radius server, and may also be disposed in a Radius client, and this embodiment is not specifically limited.
1. In this embodiment, a Radius function platform is introduced and set in a Radius client or a Radius server, or the Radius function platform is set on other devices except the Radius client and the Radius server, where the device is accessed to a networking to which the Radius client and the Radius server belong through a bypass mounting mode, and processes a Radius interaction message in the networking; the Radius function platform mainly comprises three functions: IP message processing, neural networks, notification management, etc.;
2. radius interactive message processing: the Radius function platform firstly preprocesses a Radius interactive message and extracts an attribute field from the Radius interactive message, wherein the attribute field comprises a standard attribute field and a Radius private extended attribute field, such as a source IP address, a destination IP address, a transport layer protocol type, a service type and the like;
3. constructing an initial neural network: inputting the extracted Radius private extended attribute field (including extended attributes in a No. 26 Vendor-Specific) as a feature vector into the constructed initial neural network, wherein the Radius private extended attribute field comprises: attribute type, attribute name and attribute value;
4. carrying out nonlinear combination on the input feature vectors through a full connection layer of the initial neural network, and completely reassembling the local features;
5. performing function classification on the reassembled and integrated features through a Softmax function of an output layer in the initial neural network, so that the initial neural network is trained to attribute fields related to each Radius private extended attribute, and a target neural network is obtained after training; such as: the Radius private extended attribute included in the speed limit function learned by the initial neural network is as follows: Input-Peak-Rate, Input-Average-Rate, Input-Basic-Rate;
6. a company adds a Radius private extended attribute in a property No. 26 (Vendor-Specific), for example, adds a Radius private extended attribute Input-Low-Rate in the speed limit function. Then, at this time, the Radius private extended attribute can be found to be the newly added Radius private extended attribute through the target neural network.
7. The Radius function platform can simulate the newly-added Radius private extended attribute to perform message interaction with the Radius client according to the learned characteristics of the interactive message so as to verify the correctness of the newly-learned newly-added Radius private extended attribute;
8. after the Radius function platform verifies that the newly added Radius private extended attribute is correct, the newly added Radius private extended attribute can be written into a Radius Server private dictionary for subsequent use.
Example 3:
the embodiment of the present application further provides a Radius attribute extension apparatus, which is mainly used for executing the Radius attribute extension method provided in the foregoing content of the embodiment of the present application, and the Radius attribute extension apparatus provided in the embodiment of the present application is specifically described below.
Fig. 5 is a schematic diagram of a Radius attribute expanding apparatus according to an embodiment of the present application, and as shown in fig. 5, the Radius attribute expanding apparatus mainly includes an obtaining unit 10, a feature extracting unit 20, a determining unit 30, and an adding unit 40, where:
an obtaining unit 10, configured to obtain a Radius interaction message, where the Radius interaction message is an authentication request message, an authentication response message, a charging start request message, a charging start response message, a charging stop request message, or a charging end response message;
a feature extraction unit 20, configured to perform feature extraction on the Radius interaction packet to obtain a target attribute field for representing a Radius private extended attribute;
a determining unit 30, configured to input the attribute field into a target neural network, so as to determine whether a Radius private extended attribute represented by the target attribute field is a newly added Radius private extended attribute;
and the adding unit 40 is configured to add the newly added Radius private extension attribute to the Radius private dictionary if the determination is yes.
In the embodiment of the application, firstly, a Radius interactive message is obtained, and then, the characteristic extraction is carried out on the Radius interactive message to obtain an attribute field in the Radius interactive message; and finally, inputting the attribute field into the target neural network to determine whether the extended attribute corresponding to the attribute field is the newly added Radius private extended attribute, wherein if the extended attribute is determined to be the newly added Radius private extended attribute, the newly added Radius private extended attribute is added into a Radius private dictionary.
As can be seen from the above description, in this embodiment, the newly added Radius private extension attribute is automatically identified through the neural network, and the Radius private extension attribute is automatically added to the Radius Server private dictionary for subsequent use. According to the method, the Radius private extended attribute does not need to be added manually, related equipment does not need to pay attention to the Radius private extended attribute of each company, the newly added Radius private extended attribute of each company can be identified and added, labor is saved, meanwhile, the maintenance cost of a project is reduced, the technical problems that in the prior art, due to the fact that the Radius private extended attribute is added manually, timeliness is poor and efficiency is low are solved, and the technical effects that the equipment learns automatically and the Radius private extended attribute is added automatically are achieved.
Optionally, the adding unit includes: the verification module is used for verifying the correctness of the newly added Radius private extended attribute; and the adding module is used for adding the newly added Radius private extension attribute into the Radius private dictionary if the verification is passed.
Optionally, the verification module is configured to: acquiring a simulated Radius interaction message carrying the target attribute field, which is sent by a Radius client, wherein the simulated Radius interaction message comprises at least one of the following: simulating an authentication request message, a charging start request message and a charging stop request message; verifying the legality of the target attribute field carried in the simulated Radius interactive message based on the attribute field in the simulated Radius private dictionary, wherein the target attribute field legally represents that the attribute type, the attribute name and the attribute value of the target attribute field can be found in the simulated Radius private dictionary; and if the verification is legal, verifying that the target attribute field is correct.
Optionally, the apparatus is further configured to determine the target neural network by: deploying an initial neural network; acquiring a training sample message, wherein the training sample message is a Radius interaction message; extracting an attribute field of a Radius private extended attribute from the training sample message, wherein the Radius private extended attribute field comprises: attribute type, attribute name and attribute value; and taking the extracted Radius private extended attribute field and the corresponding label as the input of an initial neural network, and training the initial neural network to obtain the target neural network.
Optionally, the determining unit is configured to: inputting the target attribute field into a target neural network to obtain a corresponding label; inquiring recorded Radius private extended attribute fields corresponding to the tags, and determining whether the recorded Radius private extended attribute fields same as the target attribute fields exist or not; and if the attribute does not exist, determining that the Radius private extended attribute represented by the target attribute field is the newly added Radius private extended attribute.
The device provided by the embodiment of the present application has the same implementation principle and technical effect as the foregoing method embodiments, and for the sake of brief description, reference may be made to the corresponding contents in the foregoing method embodiments where no part of the device embodiments is mentioned.
Example 4:
referring to fig. 6, an embodiment of the present application further provides an electronic device 100, including: a processor 60, a memory 61, a bus 62 and a communication interface 63, wherein the processor 60, the communication interface 63 and the memory 61 are connected through the bus 62; the processor 60 is arranged to execute executable modules, such as computer programs, stored in the memory 61.
The Memory 61 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 63 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The bus 62 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 6, but that does not indicate only one bus or one type of bus.
The memory 61 is used for storing a program, and the processor 60 executes the program after receiving an execution instruction, and the method executed by the apparatus defined by the flow process disclosed in any of the foregoing embodiments of the present application may be applied to the processor 60, or implemented by the processor 60.
The processor 60 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 60. The Processor 60 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory 61, and the processor 60 reads the information in the memory 61 and, in combination with its hardware, performs the steps of the above method.
In addition, in the description of the embodiments of the present application, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present application can be understood in a specific case by those of ordinary skill in the art.
In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be through some communication interfaces, indirect coupling or communication connection between devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the exemplary embodiments of the present application, and are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (6)
1. A Radius attribute expansion method is characterized by comprising the following steps:
acquiring a Radius interactive message, wherein the Radius interactive message is an authentication request message, an authentication response message, a charging start request message, a charging start response message, a charging stop request message or a charging end response message; the Radius interactive message is an interactive message of a Radius client and a Radius server in an authentication stage and a charging stage;
performing feature extraction on the Radius interactive message to obtain a target attribute field for representing the Radius private extended attribute; wherein the target attribute field includes: attribute type, attribute value, and attribute name;
inputting the target attribute field into a target neural network, and determining a corresponding label of the target attribute field; inquiring recorded Radius private extended attribute fields corresponding to the tags, and determining whether the recorded Radius private extended attribute fields same as the target attribute fields exist or not; the target neural network is obtained after functional classification of the reassembled and integrated features is carried out through an output layer Softmax function in the initial neural network, so that the initial neural network is trained to attribute fields related to each Radius private extended attribute;
if the attribute does not exist, determining that the Radius private extended attribute represented by the target attribute field is a newly added Radius private extended attribute, and acquiring a simulated Radius interaction message carrying the target attribute field and sent by a Radius client, wherein the simulated Radius interaction message comprises at least one of the following: simulating an authentication request message, a charging start request message and a charging stop request message;
verifying the legality of the target attribute field carried in the simulated Radius interactive message based on an attribute field in a simulated Radius private dictionary, wherein the Radius functional platform comprises the simulated Radius private dictionary, the simulated Radius private dictionary is constructed by a Radius private dictionary in a simulated Radius server, and the target attribute field legally indicates that the attribute type, the attribute name and the attribute value of the target attribute field can be found in the simulated Radius private dictionary;
and if the verification is legal, verifying that the target attribute field is correct, and adding the newly added Radius private extended attribute into a Radius private dictionary.
2. The method of claim 1, wherein the target neural network is determined by:
deploying an initial neural network;
acquiring a training sample message, wherein the training sample message is a Radius interaction message;
extracting a Radius private extended attribute field from the training sample message, wherein the Radius private extended attribute field comprises: attribute type, attribute name and attribute value;
and taking the extracted Radius private extended attribute field and the corresponding label as the input of an initial neural network, and training the initial neural network to obtain the target neural network.
3. A Radius attribute expansion apparatus, comprising:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a Radius interaction message, wherein the Radius interaction message is an authentication request message, an authentication response message, a charging start request message, a charging start response message, a charging stop request message or a charging end response message; the Radius interactive message is an interactive message of a Radius client and a Radius server in an authentication stage and a charging stage;
the characteristic extraction unit is used for extracting the characteristics of the Radius interactive message to obtain a target attribute field for representing the Radius private extended attribute; wherein the target attribute field includes: attribute type, attribute value, and attribute name;
the determining unit is used for inputting the attribute field into a target neural network and determining a corresponding label of the target attribute field; inquiring recorded Radius private extended attribute fields corresponding to the tags, and determining whether the recorded Radius private extended attribute fields same as the target attribute fields exist or not; the target neural network is obtained after functional classification of the reassembled and integrated features is carried out through an output layer Softmax function in the initial neural network, so that the initial neural network is trained to attribute fields related to each Radius private extended attribute;
an adding unit, configured to determine that the Radius private extended attribute represented by the target attribute field is a newly added Radius private extended attribute if the attribute does not exist, and add the newly added Radius private extended attribute to a Radius private dictionary;
the adding unit includes:
the verification module is configured to obtain a simulated Radius interaction message carrying the target attribute field and sent by a Radius client, where the simulated Radius interaction message includes at least one of the following: simulating an authentication request message, a charging start request message and a charging stop request message; verifying the legality of the target attribute field carried in the simulated Radius interactive message based on an attribute field in a simulated Radius private dictionary, wherein the Radius functional platform comprises the simulated Radius private dictionary, the simulated Radius private dictionary is constructed by a Radius private dictionary in a simulated Radius server, and the target attribute field legally indicates that the attribute type, the attribute name and the attribute value of the target attribute field can be found in the simulated Radius private dictionary; if the verification is legal, verifying that the target attribute field is correct;
and the adding module is used for adding the newly added Radius private extension attribute into the Radius private dictionary if the verification is passed.
4. The apparatus of claim 3, wherein the apparatus is further configured to determine the target neural network by:
deploying an initial neural network;
acquiring a training sample message, wherein the training sample message is a Radius interaction message;
extracting an attribute field of a Radius private extended attribute from the training sample message, wherein the Radius private extended attribute field comprises: attribute type, attribute name and attribute value;
and taking the extracted Radius private extended attribute field and the corresponding label as the input of an initial neural network, and training the initial neural network to obtain the target neural network.
5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1 and 2 when executing the computer program.
6. A computer-readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the method of any of claims 1 and 2.
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