CN116311628B - Method and system for detecting safety performance of intelligent door lock - Google Patents
Method and system for detecting safety performance of intelligent door lock Download PDFInfo
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- CN116311628B CN116311628B CN202310584859.0A CN202310584859A CN116311628B CN 116311628 B CN116311628 B CN 116311628B CN 202310584859 A CN202310584859 A CN 202310584859A CN 116311628 B CN116311628 B CN 116311628B
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C9/00—Individual registration on entry or exit
- G07C9/00174—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys
- G07C9/00182—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys operated with unidirectional data transmission between data carrier and locks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/45—Structures or tools for the administration of authentication
- G06F21/46—Structures or tools for the administration of authentication by designing passwords or checking the strength of passwords
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The application provides a detection method and a detection system for security performance of an intelligent door lock, which relate to the technical field of intelligent detection, wherein door lock setting control information is read through data interaction equipment, control analysis is carried out to determine complexity level of password setting, encryption influence values are generated according to data quantity of a door lock database, data correlation evaluation of the password setting is carried out, correlation influence values are generated, complexity level optimization is carried out, complexity level optimization is generated, unlocking interface image acquisition of a target door lock is carried out through image acquisition equipment, security added value is generated, and security performance evaluation results are generated through optimization of the complexity level and the security added value. The intelligent door lock detection method solves the technical problems that the detection of the intelligent door lock is not comprehensive enough in the prior art, so that the detection effect of the safety performance of the intelligent door lock is poor, realizes the multi-aspect detection of the intelligent door lock from the complexity of passwords and unlocking marks, and achieves the technical effects of improving the accuracy and comprehensiveness of the detection of the safety performance of the intelligent door lock.
Description
Technical Field
The application relates to the technical field of intelligent detection, in particular to a method and a system for detecting safety performance of an intelligent door lock.
Background
With the continuous development of science and technology, the requirements of people on the safety performance of article keeping are higher and higher, in ordinary life and work, the safety of a residence is usually solved by using a mechanical lock, so that users need to carry a plurality of keys every day, various inconveniences are caused, the safety is also greatly reduced, and in order to meet the increasing demands of people on the lock, intelligent door locks are slowly developed. The door lock is used as a first defense line of families, and the safety of the door lock becomes important, so the door lock is particularly important for detecting the safety performance of the intelligent door lock.
In the prior art, the detection of the intelligent door lock is not comprehensive enough, so that the technical problem of poor detection effect on the safety performance of the intelligent door lock is solved.
Disclosure of Invention
The embodiment of the application provides a method and a system for detecting the safety performance of an intelligent door lock, which are used for solving the technical problems that the detection of the intelligent door lock is not comprehensive enough in the prior art, so that the detection effect of the safety performance of the intelligent door lock is poor.
In view of the above problems, the embodiment of the application provides a method and a system for detecting the safety performance of an intelligent door lock.
In a first aspect, an embodiment of the present application provides a method for detecting security performance of an intelligent door lock, where the method includes: the target door lock is communicated through the data interaction equipment, and door lock setting control information is read; performing control analysis according to the door lock setting control information, and determining the complexity level of password setting; obtaining a door lock database of the target door lock, and generating an encryption influence value according to the data volume of the door lock database; performing data related evaluation of password setting based on the door lock database and the door lock setting control information, and generating a related influence value based on an evaluation result; optimizing the complexity level through the encryption influence value and the related influence value to generate an optimized complexity level; the unlocking interface image acquisition of the target door lock is carried out through the image acquisition equipment, and a safety added value is generated based on an image acquisition result; and generating a security performance evaluation result through the optimization complexity level and the security added value.
In a second aspect, an embodiment of the present application provides a system for detecting security performance of an intelligent door lock, where the system includes: the control information reading module is used for communicating a target door lock through the data interaction equipment and reading door lock setting control information; the control analysis module is used for performing control analysis according to the door lock setting control information and determining the complexity level of password setting; the influence value generation module is used for obtaining a door lock database of the target door lock and generating an encryption influence value according to the data volume of the door lock database; the data correlation evaluation module is used for performing data correlation evaluation of password setting based on the door lock database and the door lock setting control information, and generating a correlation influence value based on an evaluation result; the level optimization module is used for optimizing the complexity level through the encryption influence value and the related influence value to generate an optimized complexity level; the image acquisition module is used for carrying out unlocking interface image acquisition of the target door lock through the image acquisition equipment and generating a safety added value based on an image acquisition result; and the evaluation result generation module is used for generating a safety performance evaluation result through the optimization complexity level and the safety added value.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
the embodiment of the application provides a detection method for the safety performance of an intelligent door lock, which relates to the technical field of intelligent detection, and comprises the steps of reading door lock setting control information through data interaction equipment, performing control analysis to determine the complexity level of password setting, generating encryption influence values according to the data quantity of a door lock database, performing data correlation evaluation of the password setting, generating correlation influence values, optimizing the complexity level, generating an optimized complexity level, performing unlocking interface image acquisition of a target door lock through image acquisition equipment, generating a safety additional value, and generating a safety performance evaluation result through optimizing the complexity level and the safety additional value. The intelligent door lock detection device solves the technical problems that detection of an intelligent door lock is not comprehensive enough in the prior art, so that the detection effect of the safety performance of the intelligent door lock is poor, the intelligent door lock is detected in multiple aspects from password complexity and unlocking marks, and the technical effects of improving the detection accuracy and comprehensiveness of the safety performance of the intelligent door lock are achieved.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
Fig. 1 is a schematic flow chart of a method for detecting safety performance of an intelligent door lock according to an embodiment of the present application;
fig. 2 is a schematic diagram of a security added value flow generated in a method for detecting security performance of an intelligent door lock according to an embodiment of the present application;
fig. 3 is a schematic diagram of a complexity level determining process in a method for detecting security performance of an intelligent door lock according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a detection system for safety performance of an intelligent door lock according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a control information reading module 10, a control analysis module 20, an influence value generation module 30, a data correlation evaluation module 40, a level optimization module 50, an image acquisition module 60 and an evaluation result generation module 70.
Detailed Description
The embodiment of the application provides a detection method for the safety performance of an intelligent door lock, which is used for solving the technical problems that the detection of the intelligent door lock is not comprehensive enough in the prior art, so that the detection effect on the safety performance of the intelligent door lock is poor.
Example 1
As shown in fig. 1, an embodiment of the present application provides a method for detecting security performance of an intelligent door lock, where the method is applied to an intelligent detection system, and the intelligent detection system is communicatively connected with an image acquisition device and a data interaction device, and the method includes:
step S100: the target door lock is communicated through the data interaction equipment, and door lock setting control information is read;
specifically, the detection method of the safety performance of the intelligent door lock is applied to an intelligent detection system, the intelligent detection system is in communication connection with an image acquisition device and a data interaction device, the image acquisition device is used for acquiring an unlocking interface image of a target door lock, and the data interaction device is used for reading door lock setting control information. Firstly, acquiring a target door lock permission, communicating the target door lock by adopting data interaction equipment, and reading source data through a door lock background to serve as door lock setting control information, wherein the door lock setting control information comprises password setting length constraint, repetition constraint, adjacent number constraint and the like.
Step S200: performing control analysis according to the door lock setting control information, and determining the complexity level of password setting;
further, as shown in fig. 3, step S200 of the present application further includes:
step S210: obtaining repeated total quantity constraint data of a repeated password, and determining a first password complexity level based on the repeated total quantity constraint data and the total password length;
step S220: obtaining adjacent repeated constraint data, and generating a second password complexity level through the adjacent repeated constraint data;
step S230: the complexity level is determined based on the first and second password complexity levels.
Specifically, the constraint of the repetition amount is a constraint of repeated digits, that is, how many repeated digits are in a password at most, and the number of times that a digit is repeated, and the constraint data of the repetition amount is used for constraining the first password complexity, and is exemplified by setting the initial value of the first password complexity to 100%, reducing the first password complexity by 10% when each repeated digit is in a group, and reducing the first password complexity by 20% when each repeated digit is in a group. And obtaining the total length of the password, for example, for a 6-bit password, when the password is completely repeated, calculating to obtain the first password complexity of-10% according to the constraint data of the total repeated amount, which indicates that the first password complexity is extremely low. Grading the first password complexity, wherein the grade is highest and the security is best if the first password complexity is 80% -100% and is set as S grade; 50% -80% of the water is set as grade A, the grade is centered, and the safety is good; 0% -50% is set as B level, the level is lower, and the safety is poor; setting lower than 0% to be C-level, the grade is too low, and the safety is too bad. And adjusting the repeated total quantity constraint data according to the total length of the password, properly reducing the repeated total quantity constraint data when the total length of the password is increased, ensuring the complexity of the first password to be between 0 and 100 percent, and grading the complexity of the first password so as to determine the complexity level of the first password.
The adjacent repetition constraint is a constraint on adjacent numbers, that is, a repetition condition of adjacent numbers in one password, an adjacent number condition of adjacent numbers, and the adjacent repetition constraint data is used for constraining the second password complexity, and by way of example, the initial value of the second password complexity is set to 100%, and each time a group of repeated adjacent numbers occur, or each time a group of adjacent numbers are adjacent numbers, the second password complexity is reduced by 20%, and the adjacent numbers are two numbers adjacent to each other in the natural numbers which are arranged in sequence from small to large. The second password complexity is classified into the S level, the A level, the B level and the C level by the same method, so as to determine the second password complexity level.
And integrating the first password complexity level and the second password complexity level, such as carrying out average value calculation on the two levels, or determining the complexity level of the password based on the lower level, wherein the higher the complexity level is, the better the safety of the door lock is represented.
Step S300: obtaining a door lock database of the target door lock, and generating an encryption influence value according to the data volume of the door lock database;
specifically, a door lock database is constructed according to the input information of the door lock user, the door lock database comprises common information such as a user name, a date of birth, a telephone number and the like and is used for limiting the door lock password, namely when the door lock password is the common information of the user, the password is easily guessed, the security of the password is reduced, and the user is prompted to adjust the password to be a safer password. The data in the door lock database is counted to obtain the data quantity, the more the data in the door lock database is, the larger the data quantity is, the lower the encryption influence value is, and for example, the encryption influence value is reduced by 5% when one piece of data is added, so that the encryption influence value is obtained. The encryption influence value is an influence value of the door lock database on the password security, and the encryption influence value is inversely related to the password security, namely, the higher the encryption influence value is, the lower the password security is.
Step S400: performing data related evaluation of password setting based on the door lock database and the door lock setting control information, and generating a related influence value based on an evaluation result;
specifically, the first data is called according to the door lock database, the first data and the password are compared, the proportion of the repeated numbers of the first data and the password is used as a related evaluation result, if the first data is the birthday of a user, the password is 6 digits, 4 digits are repeated with the birthday of the user, the repetition rate is 66.7%, and the correlation degree of the first data and the password is determined to be 66.7%. Traversing the password in the door lock database, namely comparing the password with all data in the database, acquiring a plurality of correlations between all data in the database and the password, sequencing the correlations, taking the highest correlation as a correlation influence value, wherein the correlation influence value is inversely correlated with the password security, namely the higher the correlation influence value is, the lower the password security is.
Step S500: optimizing the complexity level through the encryption influence value and the related influence value to generate an optimized complexity level;
specifically, the encryption influence value and the related influence value are classified by the same method as in step S210, and the third and fourth password complexity levels are obtained. And adding the third password complexity level and the fourth password complexity level to the complexity level to optimize the complexity level, for example, performing mean value calculation on the third password complexity level, the fourth password complexity level and the complexity level, taking a calculation result as an optimized complexity level, or taking the smallest of the third password complexity level, the fourth password complexity level and the complexity level as an optimized complexity level. The initial password complexity level is S level, but after comparing the door lock database, the initial password is determined to be identical to the user' S birthday, that is, the correlation is 100%, the fourth password complexity level is determined to be the lowest level, and the initial password complexity level is optimized according to the fourth password complexity level, so as to generate an optimized complexity level.
Step S600: the unlocking interface image acquisition of the target door lock is carried out through the image acquisition equipment, and a safety added value is generated based on an image acquisition result;
specifically, the image acquisition equipment is a high-definition camera, the unlocking interface of the target door lock is a password input area when password unlocking is carried out, when the password unlocking is carried out, input marks are inevitably remained on the unlocking interface, when the input marks are too clear, password leakage is caused, and the safety of the door lock is reduced. The residual trace of the unlocking interface is collected through the image collection equipment, the definition of the residual trace is identified, and the safety added value is generated according to the definition.
Further, as shown in fig. 2, step S600 of the present application further includes:
step S610: constructing a trace identification feature set;
step S620: performing trace recognition on the image acquisition result through the trace recognition feature set, determining a recognition result, and generating a first influence value according to the definition of the trace;
step S630: performing error comparison based on the identification result and the input result, and generating a second influence value according to the error comparison result;
step S640: and generating the safety added value through the first influence value and the second influence value.
Specifically, a plurality of images which remain traces when password unlocking is acquired, characteristic recognition is carried out on the images of the plurality of traces based on a convolution kernel, illustratively, gray processing is carried out on the images, trace edges in the acquired images are recognized according to the change condition of gray values, the trace edges are taken as image recognition characteristics, and a trace recognition characteristic set is constructed.
And carrying out definition recognition on marks in the image acquisition result, wherein the definition refers to the definition degree of each fine shadow and the boundary of each fine shadow in the image, the clearer the marks are, the higher the definition is, the definition of the marks is used as a first influence value, the higher the first influence value is, the clearer the marks left when password unlocking is carried out are, and the lower the safety of the door lock is.
And performing edge recognition on the trace in the image acquisition result to acquire acquired trace recognition features, traversing the acquired trace recognition features in a trace recognition feature set, namely comparing the acquired trace recognition features with a plurality of trace recognition features in the trace recognition feature set, setting a similarity threshold value, for example, setting to 80%, and judging that the trace recognition features in the trace recognition feature set are consistent with the acquired trace recognition features in the image acquisition result when the comparison result meets the similarity threshold value, namely reaches more than 80%, and extracting features which are consistent in recognition in the trace recognition feature set as recognition results.
And acquiring a plurality of numbers on the unlocking interface according to the identification result, freely combining the numbers into a plurality of groups of passwords, inputting the passwords input during unlocking as a result, comparing the obtained numbers with the input passwords in error, namely calculating the ratio of the numbers to the same numbers in the input passwords, and taking the ratio as a second influence value, wherein the higher the second influence value is, the more the numbers of the passwords which can be identified according to the residual trace are, the worse the safety of the door lock is.
Further, step S640 of the present application further includes:
step S641: judging whether the target door lock is a fixed key position door lock or not;
step S642: when the target door lock is a door lock at the position of the non-fixed key, regularly analyzing the key generation of the non-fixed key, and generating a weakening coefficient according to an analysis result;
step S643: and carrying out weakening adjustment on the first influence value through the weakening coefficient, and generating the safety added value based on the first influence value and the second influence value after weakening adjustment.
Specifically, when the fixed key position is the password verification of the door lock, the numbers of the unlocking interface are arranged to be fixed positions, and if the fixed key position is the fixed key position, marks are more easily left on common numbers when the password is unlocked for a plurality of times, so that the safety of the door lock is reduced.
When the target door lock is a door lock with a non-fixed key position, namely, the numbers of the unlocking interface are arranged at random positions, the key generation rule of the non-fixed keys is analyzed, and a plurality of continuous non-fixed key positions are obtained, and the association is obtained, wherein the higher the association is, the more regular the key generation rule of the non-fixed keys of the target door lock is, the easier the key generation rule of the non-fixed keys of the target door lock is identified, and the higher the association is as a weakening coefficient, namely, the more regular the non-fixed key positions of the target door lock are, the higher the weakening coefficient is.
And performing weakening adjustment on the first influence value through the weakening coefficient, and illustratively calculating the product of the weakening coefficient and the first influence value, and taking the product calculation result as the first influence value after weakening adjustment. The first influence value is the definition of the residual trace on the target door lock, and when the non-fixed key positions of the target door lock are more regular, the definition of the residual trace on the door lock has greater influence on safety, so that the weakening degree is low; when the non-fixed key positions of the target door lock are irregular, whether the residual traces on the door lock and whether the residual traces are clear have smaller influence on the safety of the door lock, so that the weakening degree is high. And generating the safety added value based on the first influence value and the second influence value after weakening adjustment.
Step S700: and generating a security performance evaluation result through the optimization complexity level and the security added value.
Specifically, the security when the password is set is evaluated according to the level of the optimized complexity, and the higher the level is, the better the security is; the safety of residual traces during unlocking is evaluated according to the safety added value, the higher the safety added value is, the clearer the trace is, the lower the safety is, the multi-aspect evaluation results of the optimization complexity level and the safety added value are integrated, and the safety performance evaluation result is calculated and generated.
Further, the application also comprises:
step S810: judging whether the target door lock comprises a verification early warning prompt or not;
step S820: when the target door lock comprises a verification early warning prompt, early warning prompt verification is executed on the target door lock, and a security performance basic value is generated;
step S830: and obtaining an early warning prompt verification result, correcting the safety performance basic value based on the early warning prompt verification result, and generating the safety performance evaluation result according to the correction result, the optimization complexity level and the safety added value.
Specifically, when the verification early warning prompt is that the password is unlocked, someone appears in a certain range, so that the password is prevented from being seen, the password safety is guaranteed, and the door lock sends out the early warning prompt.
When the target door lock comprises an authentication early warning prompt, the safety is improved, a safety performance basic value is generated by comparing the door lock without the early warning prompt, the safety performance basic value is set to be 100%, the target door lock is subjected to early warning prompt authentication, namely, the distance between an authentication person and the door lock is adjusted, a plurality of authentication scenes are constructed, a plurality of early warning prompt sensitivities under the plurality of authentication scenes are obtained, the average value of the plurality of early warning prompt sensitivities is calculated, a calculation result is used as the early warning prompt authentication result, the higher the early warning prompt sensitivity is, the better the safety is, the safety performance basic value is corrected based on the early warning prompt authentication result, namely, the product of the calculation sensitivity and the safety performance basic value is taken as a correction result, and if the sensitivity is 80%, the correction result of the safety performance basic value is. And the correction result is used for evaluating the safety of early warning of suspicious personnel when unlocking, and the evaluation result is added to the safety performance evaluation result.
Further, step S830 of the present application further includes:
step S831: setting scene verification density, and constructing M verification scenes according to the scene verification density, wherein the M verification scenes comprise visible password scenes and invisible password scenes;
step S832: performing early warning verification on the target door lock through the M verification scenes to obtain early warning verification accuracy data and early warning verification sensitivity data;
step S833: and obtaining the early warning prompt verification result through the early warning verification accuracy data and the early warning verification sensitivity data.
Specifically, a verification scene is set, the verification scene is used for unlocking by unlocking personnel, a certain distance from the verification personnel to the door lock is set, and the sensitivity of the early warning prompt of the door lock is verified. Scene verification density, that is, the distance of a verification person from a door lock, is larger, the more scenes are constructed, the better the verification effect is, and an exemplary verification range is set to be 5 cm per distance, so that M verification scenes are constructed. The M verification scenes comprise a plurality of distances, and the normal vision (1.0) can see objects at the distance of 33 cm, so that 33 cm is used as a critical value, and the distance between a verification person and a door lock is within 33 cm as a visible password scene, namely the scene that the verification person can see the password directly; the non-visual password scene with the distance between the verifier and the door lock being more than 33 cm is used as the non-visual password scene, namely the scene that the verifier cannot directly see the password, and in the non-visual password scene, the password can be seen by people with good eyesight is not excluded, so that the non-visual password scene verification prompt is also important.
And respectively carrying out early warning verification on M verification scenes, namely unlocking by an unlocking person, standing at a verification position by the verification person, enabling the target door lock to carry out verification prompt, acquiring early warning prompt accuracy and sensitivity of the target door lock under different distances, taking the early warning prompt accuracy and sensitivity as early warning verification accuracy data and early warning verification sensitivity data, calculating the average value of the early warning verification accuracy data and the early warning verification sensitivity data under the M verification scenes, and taking the average value calculation result as the early warning prompt verification result.
Further, the application also comprises:
step S910: judging whether the target door lock supports intelligent auxiliary identification or not;
step S920: when intelligent auxiliary identification is supported, generating input information based on the generated countermeasure network;
step S930: performing door lock testing of the target door lock according to the input information generating result, and generating a newly-added security performance analysis value according to the testing result and the input information generating result;
step S940: and generating a security performance evaluation result through the newly-added security performance analysis value, the optimization complexity level and the security added value.
Specifically, the intelligent auxiliary identification is an unlocking mode except password unlocking, such as fingerprint unlocking and face unlocking. When intelligent auxiliary identification is supported, acquiring face and fingerprint images which can be unlocked as input information. Based on the generation countermeasure network, the face image is adjusted according to the structural characteristics of the input information, such as changing the face image into a nose, adjusting the organ proportion and the like, a face similar to the input face is generated, and therefore a plurality of false input information similar to the input information in structure are generated and serve as input information generation results. In brief, the generation of the countermeasure network is an unsupervised learning method, the training process is realized through mutual restriction of the generation network and the discrimination network, the generation network is used for generating similar information according to the input information, the generated similar information is closer to the input information, the discrimination network is used for striving to distinguish the similar information from the input information, and the model accuracy of the generation network and the discrimination network is finally improved through countermeasure.
And testing the target door lock according to the input information generation result, judging whether the target door lock can accurately identify similar faces and fingerprints, taking the identification accuracy as a newly-added security performance analysis value, and indicating that the higher the newly-added security performance analysis value is, the higher the identification accuracy of the door lock on the faces and fingerprints is, so that the similar faces and fingerprints can be accurately identified, and the better the security of the door lock is. And adding the newly added safety performance analysis value to a safety performance evaluation result.
In summary, the method and the system for detecting the security performance of the intelligent door lock provided by the embodiment of the application have the following technical effects:
the method comprises the steps of reading door lock setting control information through data interaction equipment, performing control analysis to determine the complexity level of password setting, generating encryption influence values according to the data volume of a door lock database, performing data correlation evaluation of the password setting, generating correlation influence values, optimizing the complexity level, generating an optimized complexity level, performing unlocking interface image acquisition of a target door lock through image acquisition equipment, generating a safety additional value, and generating a safety performance evaluation result through optimizing the complexity level and the safety additional value. The intelligent door lock detection device solves the technical problems that detection of an intelligent door lock is not comprehensive enough in the prior art, so that the detection effect of the safety performance of the intelligent door lock is poor, the intelligent door lock is detected in multiple aspects from password complexity and unlocking marks, and the technical effects of improving the detection accuracy and comprehensiveness of the safety performance of the intelligent door lock are achieved.
Example two
Based on the same inventive concept as the detection method of the security performance of an intelligent door lock in the foregoing embodiment, as shown in fig. 4, the present application provides a detection system of the security performance of an intelligent door lock, the system includes:
the control information reading module 10 is used for communicating a target door lock through the data interaction equipment and reading door lock setting control information;
the control analysis module 20 is used for performing control analysis according to the door lock setting control information, and determining the complexity level of password setting;
the influence value generation module 30 is configured to obtain a door lock database of the target door lock, and generate an encrypted influence value according to a data amount of the door lock database;
a data-related evaluation module 40, wherein the data-related evaluation module 40 is used for performing data-related evaluation of password setting based on the door lock database and the door lock setting control information, and generating a related influence value based on an evaluation result;
a level optimization module 50, where the level optimization module 50 is configured to optimize the complexity level by using the encryption impact value and the correlation impact value, and generate an optimized complexity level;
the image acquisition module 60 is used for carrying out unlocking interface image acquisition of the target door lock through the image acquisition equipment, and generating a safety added value based on an image acquisition result;
and an evaluation result generation module 70, wherein the evaluation result generation module 70 is used for generating a security performance evaluation result through the optimization complexity level and the security added value.
Further, the system further comprises:
the trace identification feature construction module is used for constructing a trace identification feature set;
the trace identification module is used for carrying out trace identification on the image acquisition result through the trace identification feature set, determining an identification result and generating a first influence value according to the definition of the trace;
the error comparison module is used for comparing errors based on the identification result and the input result and generating a second influence value according to the error comparison result;
and the safety added value generation module is used for generating the safety added value through the first influence value and the second influence value.
Further, the system further comprises:
the first judging module is used for judging whether the target door lock is a fixed key position door lock or not;
the rule analysis module is used for generating rule analysis on the keys of the non-fixed keys when the target door lock is a door lock at the position of the non-fixed keys and generating weakening coefficients according to analysis results;
and the weakening adjustment module is used for carrying out weakening adjustment on the first influence value through the weakening coefficient and generating the safety added value based on the first influence value and the second influence value after weakening adjustment.
Further, the system further comprises:
the first complexity level determining module is used for obtaining repeated total quantity constraint data of the repeated passwords and determining a first password complexity level based on the repeated total quantity constraint data and the total password length;
the second complexity level generating module is used for obtaining adjacent repeated constraint data and generating a second password complexity level through the adjacent repeated constraint data;
and the complexity level determining module is used for determining the complexity level based on the first password complexity level and the second password complexity level.
Further, the system further comprises:
the second judging module is used for judging whether the target door lock comprises a verification early warning prompt or not;
the early warning prompt verification module is used for executing early warning prompt verification on the target door lock and generating a security performance basic value when the target door lock contains verification early warning prompts;
the correction module is used for obtaining an early warning prompt verification result, correcting the safety performance basic value based on the early warning prompt verification result, and generating the safety performance evaluation result according to the correction result, the optimization complexity level and the safety added value.
Further, the system further comprises:
the verification scene construction module is used for setting scene verification density and constructing M verification scenes according to the scene verification density, wherein the M verification scenes comprise visible password scenes and invisible password scenes;
the early warning verification module is used for carrying out early warning verification on the target door lock through the M verification scenes to obtain early warning verification accuracy data and early warning verification sensitivity data;
and the verification result acquisition module is used for acquiring the early warning prompt verification result through the early warning verification accuracy data and the early warning verification sensitivity data.
Further, the system further comprises:
the third judging module is used for judging whether the target door lock supports intelligent auxiliary identification or not;
the input information generation module is used for generating input information based on the generation countermeasure network when intelligent auxiliary identification is supported;
the door lock testing module is used for carrying out door lock testing of the target door lock through the input information generating result and generating a newly-added security performance analysis value according to the testing result and the input information generating result;
the safety performance evaluation result generation module is used for generating a safety performance evaluation result through the newly-added safety performance analysis value, the optimization complexity level and the safety added value.
Through the foregoing detailed description of the method for detecting the security performance of the intelligent door lock, those skilled in the art can clearly know the method and the system for detecting the security performance of the intelligent door lock in this embodiment, and for the device disclosed in the embodiment, the description is relatively simple because it corresponds to the method disclosed in the embodiment, and relevant places refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. The method is characterized by being applied to an intelligent detection system, wherein the intelligent detection system is in communication connection with image acquisition equipment and data interaction equipment, and the method comprises the following steps:
the target door lock is communicated through the data interaction equipment, and door lock setting control information is read;
performing control analysis according to the door lock setting control information, and determining the complexity level of password setting;
obtaining a door lock database of the target door lock, and generating an encryption influence value according to the data volume of the door lock database;
performing data related evaluation of password setting based on the door lock database and the door lock setting control information, and generating a related influence value based on an evaluation result;
optimizing the complexity level through the encryption influence value and the related influence value to generate an optimized complexity level;
the unlocking interface image acquisition of the target door lock is carried out through the image acquisition equipment, and a safety added value is generated based on an image acquisition result;
generating a security performance evaluation result through the optimization complexity level and the security added value;
the method further comprises the steps of:
constructing a trace identification feature set;
performing trace recognition on the image acquisition result through the trace recognition feature set, determining a recognition result, and generating a first influence value according to the definition of the trace;
performing error comparison based on the identification result and the input result, and generating a second influence value according to the error comparison result;
and generating the safety added value through the first influence value and the second influence value.
2. The method of claim 1, wherein the method further comprises:
judging whether the target door lock is a fixed key position door lock or not;
when the target door lock is a door lock at the position of the non-fixed key, regularly analyzing the key generation of the non-fixed key, and generating a weakening coefficient according to an analysis result;
and carrying out weakening adjustment on the first influence value through the weakening coefficient, and generating the safety added value based on the first influence value and the second influence value after weakening adjustment.
3. The method of claim 1, wherein the performing control analysis based on the door lock setting control information further comprises:
obtaining repeated total quantity constraint data of a repeated password, and determining a first password complexity level based on the repeated total quantity constraint data and the total password length;
obtaining adjacent repeated constraint data, and generating a second password complexity level through the adjacent repeated constraint data;
the complexity level is determined based on the first and second password complexity levels.
4. The method of claim 1, wherein the method further comprises:
judging whether the target door lock comprises a verification early warning prompt or not;
when the target door lock comprises a verification early warning prompt, early warning prompt verification is executed on the target door lock, and a security performance basic value is generated;
and obtaining an early warning prompt verification result, correcting the safety performance basic value based on the early warning prompt verification result, and generating the safety performance evaluation result according to the correction result, the optimization complexity level and the safety added value.
5. The method of claim 4, wherein the method further comprises:
setting scene verification density, and constructing M verification scenes according to the scene verification density, wherein the M verification scenes comprise visible password scenes and invisible password scenes;
performing early warning verification on the target door lock through the M verification scenes to obtain early warning verification accuracy data and early warning verification sensitivity data;
and obtaining the early warning prompt verification result through the early warning verification accuracy data and the early warning verification sensitivity data.
6. The method of claim 1, wherein the method further comprises:
judging whether the target door lock supports intelligent auxiliary identification or not;
when intelligent auxiliary identification is supported, generating input information based on the generated countermeasure network;
performing door lock testing of the target door lock according to the input information generating result, and generating a newly-added security performance analysis value according to the testing result and the input information generating result;
and generating a security performance evaluation result through the newly-added security performance analysis value, the optimization complexity level and the security added value.
7. The utility model provides a detecting system of intelligent lock security performance, its characterized in that, system and image acquisition equipment, data interaction equipment communication connection, the system includes:
the control information reading module is used for communicating a target door lock through the data interaction equipment and reading door lock setting control information;
the control analysis module is used for performing control analysis according to the door lock setting control information and determining the complexity level of password setting;
the influence value generation module is used for obtaining a door lock database of the target door lock and generating an encryption influence value according to the data volume of the door lock database;
the data correlation evaluation module is used for performing data correlation evaluation of password setting based on the door lock database and the door lock setting control information, and generating a correlation influence value based on an evaluation result;
the level optimization module is used for optimizing the complexity level through the encryption influence value and the related influence value to generate an optimized complexity level;
the image acquisition module is used for carrying out unlocking interface image acquisition of the target door lock through the image acquisition equipment and generating a safety added value based on an image acquisition result;
the evaluation result generation module is used for generating a safety performance evaluation result through the optimization complexity level and the safety added value;
the system further comprises:
the trace identification feature construction module is used for constructing a trace identification feature set;
the trace identification module is used for carrying out trace identification on the image acquisition result through the trace identification feature set, determining an identification result and generating a first influence value according to the definition of the trace;
the error comparison module is used for comparing errors based on the identification result and the input result and generating a second influence value according to the error comparison result;
and the safety added value generation module is used for generating the safety added value through the first influence value and the second influence value.
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