CN111131155A - Wireless network security assessment method, system and terminal - Google Patents

Wireless network security assessment method, system and terminal Download PDF

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CN111131155A
CN111131155A CN201911136347.8A CN201911136347A CN111131155A CN 111131155 A CN111131155 A CN 111131155A CN 201911136347 A CN201911136347 A CN 201911136347A CN 111131155 A CN111131155 A CN 111131155A
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CN111131155B (en
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陈智明
黄科
李子龙
王远雄
钟敏
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Guangdong Power Grid Co Ltd
Meizhou Power Supply Bureau of Guangdong Power Grid Co Ltd
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Abstract

The invention discloses a wireless network security assessment method, a wireless network security assessment system and a wireless network security assessment terminal, wherein the method comprises the steps of scanning a wireless local area network, and acquiring parameter information and a client identifier of the wireless local area network; acquiring behavior information of a plurality of clients connected with the wireless local area network; establishing a wireless network security assessment index system according to the acquired behavior information, performing subjective and objective evaluation on assessment indexes, and calculating a comprehensive weight value of the assessment indexes; establishing a BP neural network model, taking the evaluation index as an input signal of an input layer in the BP neural network model, and taking the comprehensive weight value as an initial weight value of the BP neural network model for learning and training; and calculating the safety evaluation value of the wireless local area network by adopting the trained BP neural network model. The invention can help the user to quickly, objectively and fairly verify and evaluate the security of the wireless network, and provides a scientific evaluation means for improving the management and maintenance level of the user on the wireless network.

Description

Wireless network security assessment method, system and terminal
Technical Field
The embodiment of the invention relates to the technical field of wireless networks, in particular to a wireless network security assessment method, a wireless network security assessment system and a wireless network security assessment terminal.
Background
With the generation and application of wireless internet technologies such as wireless local area networks, mobile internet and the like, the wireless network enables people to live online more easily and freely, and people who hold a notebook computer and walk anywhere in life are not in the scenes of movies for a long time, such as downloading data, printing files and the like anytime and anywhere. Among many computer networking technologies, wireless networks have irreplaceable roles for other networking technologies in many applications, relying on their incomparable flexibility, mobility, and great scalability. However, network security problems are increasingly prominent when people enjoy the convenience that wireless internet brings to them and network security events related to wireless internet are frequently occurring.
In actual life, when people open electronic equipment such as a mobile phone and the like to perform wireless network search, a large number of wireless access points can be found, however, not every wireless access point is safe and reliable, for example, some wireless access points are forged by hackers, once people access the wireless access points forged by the hackers, only user information is stolen if the wireless access points are light, and privacy passwords of users are cracked if the wireless access points are heavy, so that the users are harmed.
Therefore, how to help people identify or evaluate the security of wireless networks has become an important research topic in the day ahead.
Disclosure of Invention
The invention provides a wireless network security assessment method, a wireless network security assessment system and a wireless network security assessment terminal, which aim to overcome the defects of the prior art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
in a first aspect, an embodiment of the present invention provides a wireless network security assessment method, including:
scanning a wireless local area network, and acquiring parameter information and a client identifier of the wireless local area network;
acquiring behavior information of a plurality of clients connected with the wireless local area network;
establishing a wireless network security assessment index system according to the acquired behavior information, performing subjective and objective evaluation on assessment indexes, and calculating a comprehensive weight value of the assessment indexes;
establishing a BP neural network model, taking the evaluation index as an input signal of an input layer in the BP neural network model, and taking the comprehensive weight value as an initial weight value of the BP neural network model for learning and training;
and calculating the safety evaluation value of the wireless local area network by adopting the trained BP neural network model.
Further, in the wireless network security assessment method, the behavior information includes: the time of the client sending the operation request, the time of the client responding to the operation, the transmission quantity of the data packets and the transmission rate.
Further, in the wireless network security assessment method, the parameter information includes: whether the data signal is encrypted, the encryption mode of the data signal, the vulnerability condition and the virus firewall;
the client identification comprises: the service set identifies the SSID, the media access control layer MAC and the received signal strength indication RSSI.
Further, in the wireless network security assessment method, the step of performing subjective and objective evaluation on the assessment index includes:
determining subjective weight of the evaluation index by adopting an analytic hierarchy process; and determining the objective weight of the evaluation index by adopting an entropy weight method.
Further, in the wireless network security assessment method, the step of determining the subjective weight of the assessment index by using an analytic hierarchy process includes:
establishing an evaluation index according to the behavior information, the parameter information and the client identification;
establishing a judgment matrix, and listing the judgment matrix by pairwise comparison of the evaluation indexes as follows:
Vi：Vj＝aij；
A＝(aij)n*m；
in the formula, ViIs an evaluation index, VjAs another evaluation index, aijFor scale, i is 1, 2, 3, …, n; j is 1, 2, 3, …, m, and n and m are any natural numbers respectively, and the judgment is carried out by adopting the following standards: when V isiAnd VjWith equal importance, the scale is 1; when V isiRatio VjSlightly important, the scale is 3; when V isiRatio VjWhen significant, the scale is 5; when V isiRatio VjWhen strongly important, the scale is 7; when V isiRatio VjWhen extremely important, the scale is 9;
calculating the eigenvector of the maximum characteristic root of the judgment matrix A, wherein the eigenvector is a weight vector W1。
Further, in the wireless network security assessment method, the step of determining the objective weight of the assessment index by using an entropy weight method includes:
dividing the danger level of the evaluation index by an expert scoring method;
establishing factor domain U-U according to the danger level1，u2，…，un}；
Establishing a comment domain V ═ V { V } according to the evaluation index1，V2，…，Vm}；
And performing single-factor evaluation between the factor domain U and the comment domain V, and establishing the following index level matrix:
Figure BDA0002279701250000031
wherein i is 1, 2, 3, …, m; j is 1, 2, 3, …, n;
and normalizing the characteristic value of the index level matrix by adopting the following formula:
rij＝Xij/maxXij；
rij＝maxXij/Xij；
according to the normalization processing result of the characteristic value, obtaining the following normalization matrix R:
Figure BDA0002279701250000041
calculating the specific gravity P of the jth evaluation value under the ith factorij：
Figure BDA0002279701250000042
In the formula, rijThe characteristic value of the normalized matrix is shown, and n is an evaluation index;
calculating the entropy e of the ith factori：
Figure BDA0002279701250000043
Figure BDA0002279701250000044
Calculating the difference coefficient g of the ith factori：gi＝1-ei；
Defining the weight wij:
Figure BDA0002279701250000045
wijweights determined for entropy weighting, i.e. weight vectors w2。
Further, in the wireless network security assessment method, the step of calculating the comprehensive weight value of the assessment index adopts a calculation method that:
W＝α*w1+β*w2in the formula, α represents the relative importance of the analytic hierarchy process,β is the relative degree of importance of the entropy weight method, and W is the composite weight value.
In a second aspect, an embodiment of the present invention provides a wireless network security evaluation system, configured to execute the wireless network security evaluation method in the first aspect, where the system includes:
the scanning module is used for scanning a wireless local area network and acquiring parameter information and client identification of the wireless local area network;
the acquisition module is used for acquiring behavior information of a plurality of clients connected with the wireless local area network;
the calculation module is used for establishing a wireless network security assessment index system according to the acquired behavior information, performing subjective and objective evaluation on assessment indexes, and calculating a comprehensive weight value of the assessment indexes;
the training module is used for establishing a BP neural network model, taking the evaluation index as an input signal of an input layer in the BP neural network model, and taking the comprehensive weight value as an initial weight value of the BP neural network model for learning and training;
and the evaluation module is used for calculating the safety evaluation value of the wireless local area network by adopting the trained BP neural network model.
In a third aspect, an embodiment of the present invention provides a wireless network security evaluation terminal, where the wireless network security evaluation terminal is configured to operate the wireless network security evaluation system according to the second aspect.
The wireless network security assessment method, the wireless network security assessment system and the wireless network security assessment terminal provided by the embodiment of the invention can help a user to quickly, objectively and fairly verify and assess the security of a wireless network, and provide a scientific assessment means for improving the management and maintenance level of the user on the wireless network.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a flowchart illustrating a wireless network security assessment method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a wireless network security evaluation system according to a second embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Referring to fig. 1, a flow chart of a wireless network security assessment method according to an embodiment of the present invention is shown. The method specifically comprises the following steps:
s101, scanning a wireless local area network, and collecting parameter information and client identification of the wireless local area network.
Specifically, the wireless local area network is scanned, parameter information of the wireless local area network is collected, and meanwhile, an operation request received by the wireless local area network is obtained, so that a client identifier carried by the operation request is obtained.
S102, acquiring behavior information of a plurality of clients connected with the wireless local area network.
S103, establishing a wireless network security assessment index system according to the acquired behavior information, performing subjective and objective evaluation on assessment indexes, and calculating a comprehensive weight value of the assessment indexes.
S104, establishing a BP neural network model, taking the evaluation index as an input signal of an input layer in the BP neural network model, and taking the comprehensive weight value as an initial weight value of the BP neural network model for learning and training.
And S105, calculating the safety evaluation value of the wireless local area network by adopting the trained BP neural network model.
Preferably, the behavior information includes: the time of the client sending the operation request, the time of the client responding to the operation, the transmission quantity of the data packets and the transmission rate.
Preferably, the parameter information includes: whether the data signal is encrypted, the encryption mode of the data signal, the vulnerability condition and the virus firewall;
the client identification comprises: the service set identifies the SSID, the media access control layer MAC and the received signal strength indication RSSI.
Preferably, the step of subjectively and objectively evaluating the evaluation index includes:
determining subjective weight of the evaluation index by adopting an analytic hierarchy process;
and determining the objective weight of the evaluation index by adopting an entropy weight method.
Preferably, the step of determining the subjective weight of the evaluation index by using an analytic hierarchy process includes:
establishing an evaluation index according to the behavior information, the parameter information and the client identification;
establishing a judgment matrix, and listing the judgment matrix by pairwise comparison of the evaluation indexes as follows:
Vi：Vj＝aij；
A＝(aij)n*m；
in the formula, ViIs an evaluation index, VjAs another evaluation index, aijFor scale, i is 1, 2, 3, …, n; j is 1, 2, 3, …, m, and n and m are any natural numbers respectively, and the judgment is carried out by adopting the following standards: when V isiAnd VjWith equal importance, the scale is 1; when V isiRatio VjSlightly important, the scale is 3; when V isiRatio VjWhen significant, the scale is 5; when V isiRatio VjWhen strongly important, the scale is 7; when V isiRatio VjWhen extremely important, the scale is 9;
calculating the eigenvector of the maximum characteristic root of the judgment matrix A, wherein the eigenvector is a weight vectorw1。
Preferably, the step of determining the objective weight of the evaluation index by using an entropy weight method includes:
dividing the danger level of the evaluation index by an expert scoring method;
establishing factor domain U-U according to the danger level1，u2，…，un}；
Establishing a comment domain V ═ V { V } according to the evaluation index1，V2，…，Vm}；
And performing single-factor evaluation between the factor domain U and the comment domain V, and establishing the following index level matrix:
Figure BDA0002279701250000071
wherein i is 1, 2, 3, …, m; j is 1, 2, 3, …, n;
and normalizing the characteristic value of the index level matrix by adopting the following formula:
rij＝Xij/maxXij；
rij＝maxXij/Xij；
according to the normalization processing result of the characteristic value, obtaining the following normalization matrix R:
Figure BDA0002279701250000081
calculating the specific gravity P of the jth evaluation value under the ith factorij：
Figure BDA0002279701250000082
In the formula, rijThe characteristic value of the normalized matrix is shown, and n is an evaluation index;
calculating the entropy e of the ith factori：
Figure BDA0002279701250000083
Figure BDA0002279701250000084
Calculating the difference coefficient g of the ith factori：gi＝1-ei；
Definition weight wij：
Figure BDA0002279701250000085
WijWeights determined for entropy weighting, i.e. weight vectors w2。
Preferably, the step of calculating the comprehensive weight value of the evaluation index adopts a calculation mode that:
W＝α*w1+β*w2in the formula, α is the relative importance of the analytic hierarchy process, β is the relative importance of the entropy weight process, and W is the comprehensive weight value.
The wireless network security assessment method provided by the embodiment of the invention can help a user to quickly, objectively and fairly verify and assess the security of the wireless network, and provides a scientific assessment means for improving the management and maintenance level of the user on the wireless network.
Example two
Referring to fig. 2, a second embodiment of the present invention provides a wireless network security evaluation system for performing the wireless network security evaluation method according to the first embodiment, the system including:
the scanning module 21 is configured to scan a wireless local area network, and acquire parameter information and a client identifier of the wireless local area network;
an obtaining module 22, configured to obtain behavior information of multiple clients connected to the wireless local area network;
the calculation module 23 is configured to establish a wireless network security assessment index system according to the acquired behavior information, perform subjective and objective evaluation on assessment indexes, and calculate a comprehensive weight value of the assessment indexes;
the training module 24 is configured to establish a BP neural network model, use the evaluation indicator as an input signal of an input layer in the BP neural network model, and use the comprehensive weight value as an initial weight value of the BP neural network model for learning and training;
and the evaluation module 25 is configured to calculate a security evaluation value of the wireless local area network by using the trained BP neural network model.
The wireless network security evaluation system provided by the embodiment of the invention can help a user to quickly, objectively and fairly verify and evaluate the security of the wireless network, and provides a scientific evaluation means for improving the management and maintenance level of the user on the wireless network.
EXAMPLE III
The third embodiment of the invention provides a wireless network security evaluation terminal, which is used for operating the wireless network security evaluation system of the second embodiment.
The wireless network security evaluation terminal provided by the embodiment of the invention can help a user to quickly, objectively and fairly verify and evaluate the security of a wireless network, and provides a scientific evaluation means for improving the management and maintenance level of the user on the wireless network.
The above embodiments are merely to illustrate the technical solutions of the present invention, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (9)

1. A wireless network security assessment method, comprising:
scanning a wireless local area network, and acquiring parameter information and a client identifier of the wireless local area network;
acquiring behavior information of a plurality of clients connected with the wireless local area network;
establishing a wireless network security assessment index system according to the acquired behavior information, performing subjective and objective evaluation on assessment indexes, and calculating a comprehensive weight value of the assessment indexes;
establishing a BP neural network model, taking the evaluation index as an input signal of an input layer in the BP neural network model, and taking the comprehensive weight value as an initial weight value of the BP neural network model for learning and training;
and calculating the safety evaluation value of the wireless local area network by adopting the trained BP neural network model.
2. The wireless network security assessment method of claim 1, wherein the behavior information comprises: the time of the client sending the operation request, the time of the client responding to the operation, the transmission quantity of the data packets and the transmission rate.
3. The wireless network security assessment method of claim 2, wherein said parameter information comprises: whether the data signal is encrypted, the encryption mode of the data signal, the vulnerability condition and the virus firewall;
the client identification comprises: the service set identifies the SSID, the media access control layer MAC and the received signal strength indication RSSI.
4. The wireless network security evaluation method according to claim 3, wherein the step of performing subjective and objective evaluation on the evaluation index comprises:
determining subjective weight of the evaluation index by adopting an analytic hierarchy process;
and determining the objective weight of the evaluation index by adopting an entropy weight method.
5. The wireless network security assessment method of claim 4, wherein said step of determining subjective weight of said assessment index by using analytic hierarchy process comprises:
establishing an evaluation index according to the behavior information, the parameter information and the client identification;
establishing a judgment matrix, and listing the judgment matrix by pairwise comparison of the evaluation indexes as follows:
Vi：Vj＝aij；
A＝(aij)n*m；
in the formula, ViIs an evaluation index, VjAs another evaluation index, aijFor scale, i is 1, 2, 3, …, n; j is 1, 2, 3, …, m, and n and m are any natural numbers respectively, and the judgment is carried out by adopting the following standards: when V isiAnd VjWith equal importance, the scale is 1; when V isiRatio VjSlightly important, the scale is 3; when V isiRatio VjWhen significant, the scale is 5; when V isiRatio VjWhen strongly important, the scale is 7; when V isiRatio VjWhen extremely important, the scale is 9;
calculating the eigenvector of the maximum characteristic root of the judgment matrix A, wherein the eigenvector is a weight vector w1。
6. The wireless network security assessment method according to claim 5, wherein said step of determining the objective weight of said assessment index by using entropy weight method comprises:
dividing the danger level of the evaluation index by an expert scoring method;
establishing factor domain U-U according to the danger level1，u2，…，un}；
Establishing a comment domain V ═ V { V } according to the evaluation index1，V2，…，Vm}；
And performing single-factor evaluation between the factor domain U and the comment domain V, and establishing the following index level matrix:
Figure FDA0002279701240000021
wherein i is 1, 2, 3, …, m; j is 1, 2, 3, …, n;
and normalizing the characteristic value of the index level matrix by adopting the following formula:
rij＝Xij/maxXij
rij＝maxXij/Xij；
according to the normalization processing result of the characteristic value, obtaining the following normalization matrix R:
Figure FDA0002279701240000031
calculating the specific gravity P of the jth evaluation value under the ith factorij：
Figure FDA0002279701240000032
In the formula, rijThe characteristic value of the normalized matrix is shown, and n is an evaluation index;
calculating the entropy e of the ith factori：
Figure FDA0002279701240000033
Figure FDA0002279701240000034
Calculating the difference coefficient g of the ith factori：gi＝1-ei；
Definition weight wij：
Figure FDA0002279701240000035
wijWeights determined for entropy weighting, i.e. weight vectors w2。
7. The wireless network security evaluation method according to claim 6, wherein the step of calculating the comprehensive weight value of the evaluation index adopts a calculation method of:
W＝α*w1+β*w2in the formula, α is the relative importance of the analytic hierarchy process, β is the relative importance of the entropy weight process, and W is the comprehensive weight value.
8. A wireless network security evaluation system for performing the wireless network security evaluation method of any one of claims 1 to 7, the system comprising:
the scanning module is used for scanning a wireless local area network and acquiring parameter information and client identification of the wireless local area network;
the acquisition module is used for acquiring behavior information of a plurality of clients connected with the wireless local area network;
the calculation module is used for establishing a wireless network security assessment index system according to the acquired behavior information, performing subjective and objective evaluation on assessment indexes, and calculating a comprehensive weight value of the assessment indexes;
the training module is used for establishing a BP neural network model, taking the evaluation index as an input signal of an input layer in the BP neural network model, and taking the comprehensive weight value as an initial weight value of the BP neural network model for learning and training;
and the evaluation module is used for calculating the safety evaluation value of the wireless local area network by adopting the trained BP neural network model.
9. A wireless network security evaluation terminal, wherein the wireless network security evaluation terminal is configured to operate the wireless network security evaluation system according to claim 8.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112561305A (en) * 2020-12-10 2021-03-26 上海对外经贸大学 Enterprise data privacy protection evaluation method based on hierarchical model

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102902882A (en) * 2012-09-14 2013-01-30 安徽南瑞继远软件有限公司 Method for evaluating operation quality of information systems
CN105007170A (en) * 2015-05-11 2015-10-28 大连理工大学 WLAN load comprehensive evaluation method based on FAHP-SVM theory
CN106330935A (en) * 2016-08-30 2017-01-11 上海交通大学 Phishing Wi-Fi detection method
CN107634866A (en) * 2017-10-27 2018-01-26 朱秋华 A kind of distribution network communication system performance estimating method and device
CN107862455A (en) * 2017-11-02 2018-03-30 国网天津市电力公司 A kind of subjective and objective mixing appraisal procedure of power system cloud platform construction
CN108112016A (en) * 2016-11-24 2018-06-01 腾讯科技（深圳）有限公司 Wireless LAN safety appraisal procedure and device
US20180219891A1 (en) * 2017-02-02 2018-08-02 Aetna Inc. Individualized cybersecurity risk detection using multiple attributes

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102902882A (en) * 2012-09-14 2013-01-30 安徽南瑞继远软件有限公司 Method for evaluating operation quality of information systems
CN105007170A (en) * 2015-05-11 2015-10-28 大连理工大学 WLAN load comprehensive evaluation method based on FAHP-SVM theory
CN106330935A (en) * 2016-08-30 2017-01-11 上海交通大学 Phishing Wi-Fi detection method
CN108112016A (en) * 2016-11-24 2018-06-01 腾讯科技（深圳）有限公司 Wireless LAN safety appraisal procedure and device
US20180219891A1 (en) * 2017-02-02 2018-08-02 Aetna Inc. Individualized cybersecurity risk detection using multiple attributes
CN107634866A (en) * 2017-10-27 2018-01-26 朱秋华 A kind of distribution network communication system performance estimating method and device
CN107862455A (en) * 2017-11-02 2018-03-30 国网天津市电力公司 A kind of subjective and objective mixing appraisal procedure of power system cloud platform construction

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112561305A (en) * 2020-12-10 2021-03-26 上海对外经贸大学 Enterprise data privacy protection evaluation method based on hierarchical model

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