Internet of Things information security access control method and system

Abstract

The invention relates to the technical field of Internet of Things access control, and provides an Internet of Things information security access control method and system, and the method comprises the steps: collecting a vehicle position, a vehicle direction, and the remaining memory capacity and access volume of an edge server; determining an abnormal access moment and an abnormal access moment set; marking a target abnormal access moment, the first vehicle and the second vehicle, determining the vehicle information similarity of the first vehicle and the second vehicle in a time period corresponding to the abnormal access moment set, and dividing similar information vehicles and similar information vehicle groups, determining the vehicle abnormity possibility of any vehicle in a time period corresponding to the abnormal access moment set, determining abnormal nodes and normal nodes, and stopping authorization of all edge servers to all the abnormal nodes; and determining a matching priority, and realizing Internet of Things information security access control at the current acquisition moment according to the matching priority. According to the method, malicious nodes hidden in the Internet of Vehicles are screened out, and safe access of Internet of Things information is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things access control, and in particular to an Internet of Things information security access control method and system. Background Art

[0002] The Internet of Vehicles is a technology that uses the Internet of Things technology to exchange information between vehicles and infrastructure and provide services for vehicle operation. Therefore, the Internet of Vehicles system extracts a large amount of customer privacy information. In order to prevent customer privacy information from being stolen or maliciously attacked, access control of the Internet of Vehicles system is required.

[0003] In order to relieve the computing pressure of the vehicle computing unit, the Internet of Vehicles system usually transmits part of the data to the cloud or nearby edge servers for processing. When malicious nodes appear, some edge servers may be overloaded or fail, which may easily lead to the leakage of customer privacy information. Therefore, it is necessary to screen and identify malicious nodes in the Internet of Vehicles system, deny access to malicious nodes, protect customer privacy information, and ensure the stable and safe operation of the Internet of Vehicles access control system. Summary of the invention

[0004] The present invention provides an Internet of Things information security access control method and system to solve the problem of Internet of Things information leakage during access caused by malicious nodes hidden in the Internet of Vehicles. The technical solution adopted is as follows:

[0005] In a first aspect, an embodiment of the present invention provides an Internet of Things information security access control method, the method comprising the following steps:

[0006] Collect the vehicle position, vehicle direction, remaining memory capacity and access volume of the edge server at the current collection time and a preset number of collection times before;

[0007] Record any collection time as a target collection time, and determine an abnormal access time and an abnormal access time set according to the access volume of all edge servers at the target collection time and the collection time adjacent to the target collection time;

[0008] Record any abnormal access moment in the abnormal access moment set as the target abnormal access moment, record any two different vehicles in the vehicle network as the first vehicle and the second vehicle, determine the vehicle information similarity of the first vehicle and the second vehicle in the time period corresponding to the abnormal access moment set according to the difference and distance between the vehicle positions of the first vehicle and the second vehicle at all abnormal access moments included in the abnormal access moment set and adjacent moments of the abnormal access moment, and the angle between the vehicle directions of the first vehicle and the second vehicle at all abnormal access moments included in the abnormal access moment set, divide similar information vehicles and similar information vehicle groups according to the vehicle information similarity, determine the vehicle abnormality possibility of any vehicle in the time period corresponding to the abnormal access moment set according to the difference between the vehicle directions of any vehicle and other vehicles in the similar information vehicle group, and the difference between the vehicle information similarities, determine the abnormal node and the normal node according to the vehicle abnormality possibility, and stop all edge servers from authorizing all abnormal nodes;

[0009] Any normal node is recorded as the target normal node, and any edge server is recorded as the target edge server. According to the position relationship of the target normal node relative to the target edge server at the current collection time and the adjacent collection time, the remaining memory capacity of all edge servers, and the possibility of vehicle abnormality of the target normal node in the time period corresponding to the abnormal access time set, the security access control of the Internet of Things information at the current collection time is realized.

[0010] Further, the specific method of determining the abnormal access time and the abnormal access time set according to the access volume of all edge servers at the target collection time and the adjacent collection time of the target collection time is as follows:

[0011] The average of the visits to all edge servers at all collection times before the target collection time is recorded as the historical visit average at the target collection time;

[0012] The cumulative sum of the visits to all edge servers at the target collection time is recorded as the cumulative visits at the target collection time, the difference between the cumulative visits at the target collection time and the collection time before the target collection time is recorded as the differential cumulative visits at the target collection time, and the product of the differential cumulative visits at the target collection time and the cumulative visits is recorded as the first product at the target collection time;

[0013] The normalized value of the ratio of the first product of the target collection time to the average of the historical access volume is recorded as the malicious attack possibility at the target collection time;

[0014] When the malicious attack possibility of the target collection moment is greater than the preset abnormal access threshold, the target collection moment is marked as an abnormal access moment, and the set of continuous abnormal access moments including the current collection moment is recorded as the abnormal access moment set.

[0015] Further, the similarity of vehicle information of the first vehicle and the second vehicle in the time period corresponding to the abnormal access time set is determined based on the difference and distance between the vehicle positions of the first vehicle and the second vehicle at all abnormal access times included in the abnormal access time set and adjacent times of the abnormal access times, and the angle between the vehicle directions of the first vehicle and the second vehicle at all abnormal access times included in the abnormal access time set, including the specific method of:

[0016] The distance between the vehicle position at the target abnormal access time and the vehicle position at the moment before the target abnormal access time is recorded as the displacement of the vehicle at the target abnormal access time; the absolute value of the difference between the displacements of the first vehicle and the second vehicle at the target abnormal access time is recorded as the displacement difference between the first vehicle and the second vehicle at the target abnormal access time; the mean of the displacement differences of all different vehicles in the Internet of Vehicles at all abnormal access times included in the abnormal access time set is recorded as the mean of the displacement differences in the time period corresponding to the abnormal access time set;

[0017] The angle between the vehicle directions of the first vehicle and the second vehicle at the target abnormal access time is recorded as the vehicle direction difference between the first vehicle and the second vehicle at the target abnormal access time;

[0018] The distance between the vehicle positions of the first vehicle and the second vehicle at the target abnormal access time is recorded as the vehicle distance between the first vehicle and the second vehicle at the target abnormal access time; the mean of the vehicle distances between the first vehicle and the second vehicle at all abnormal access times included in the abnormal access time set is recorded as the average vehicle distance between the first vehicle and the second vehicle in the time period corresponding to the abnormal access time set; the mean of the vehicle distances between all different vehicles in the Internet of Vehicles at the same abnormal access time included in the abnormal access time set is recorded as the comparative vehicle average distance in the time period corresponding to the abnormal access time set;

[0019] Based on the displacement differences and vehicle direction differences of the first vehicle and the second vehicle in all abnormal access moments included in the abnormal access moment set, the average vehicle distance between the first vehicle and the second vehicle in the time period corresponding to the abnormal access moment set, the mean of the displacement differences in the time period corresponding to the abnormal access moment set and the compared vehicle average distance, the vehicle information similarity of the first vehicle and the second vehicle in the time period corresponding to the abnormal access moment set is determined.

[0020] Further, the similarity of vehicle information between the first vehicle and the second vehicle in the time period corresponding to the abnormal access time set is determined based on the displacement difference and vehicle direction difference of all abnormal access times included in the abnormal access time set between the first vehicle and the second vehicle, the average vehicle distance between the first vehicle and the second vehicle in the time period corresponding to the abnormal access time set, the mean of the displacement difference in the time period corresponding to the abnormal access time set, and the compared average vehicle distance, including the specific method of:

[0021] The maximum value of the displacement difference between the first vehicle and the second vehicle at all abnormal access moments included in the abnormal access moment set is recorded as the maximum displacement difference between the first vehicle and the second vehicle in the time period corresponding to the abnormal access moment set, and the ratio of the mean displacement difference in the time period corresponding to the abnormal access moment set to the maximum displacement difference between the first vehicle and the second vehicle in the time period corresponding to the abnormal access moment set is recorded as the first ratio;

[0022] The ratio of the average distance of the comparison vehicles in the time period corresponding to the abnormal access time set to the average distance of the vehicles between the first vehicle and the second vehicle in the time period corresponding to the abnormal access time set is recorded as a second ratio;

[0023] The ratio of the number 1 to the average of the vehicle direction differences between the first vehicle and the second vehicle at all abnormal access moments included in the abnormal access moment set is recorded as a third ratio;

[0024] The normalized value of the product of the first ratio, the second ratio and the third ratio is recorded as the vehicle information similarity between the first vehicle and the second vehicle in the time period corresponding to the abnormal access time set.

[0025] Furthermore, the method of dividing similar information vehicles and similar information vehicle groups according to vehicle information similarity includes:

[0026] Two different vehicles corresponding to vehicle information similarities greater than a preset similarity division threshold are divided into a group of similar information vehicles, and all similar information vehicles divided from the same abnormal access time set are recorded as a similar information vehicle group.

[0027] Further, the method of determining the possibility of vehicle abnormality of any one vehicle in the time period corresponding to the abnormal access time set according to the difference between the vehicle directions of any one vehicle in the similar information vehicle group and other vehicles, and the difference between the vehicle information similarities, includes the following specific methods:

[0028] The mean of the vehicle directions of all the similar information vehicles included in the similar information vehicle group at all the abnormal access moments included in the abnormal access moment set is recorded as the first overall heading direction of the similar information vehicle group in the time period corresponding to the abnormal access moment set, the mean of the vehicle directions of all the abnormal access moments included in the abnormal access moment set of the target similar information vehicle is recorded as the second overall heading direction of the target similar information vehicle in the time period corresponding to the abnormal access moment set, and the angle between the first overall heading direction of the similar information vehicle group in the time period corresponding to the abnormal access moment set and the second overall heading direction of the target similar information vehicle in the time period corresponding to the abnormal access moment set is recorded as the direction deviation of the target similar information vehicle in the time period corresponding to the abnormal access moment set;

[0029] The maximum value of the vehicle information similarity between the target similar information vehicle and other vehicles in the time period corresponding to the abnormal access time set is recorded as the first vehicle information similarity of the target similar information vehicle; the maximum value of the vehicle information similarity in the time period corresponding to the abnormal access time set of all two different vehicles is recorded as the second vehicle information similarity; the ratio of the second vehicle information similarity to the first vehicle information similarity of the target similar information vehicle is recorded as the third vehicle information similarity of the target similar information vehicle in the time period corresponding to the abnormal access time set;

[0030] The vehicle abnormality possibility of the target similar information vehicle in the time period corresponding to the abnormal access time set is determined based on the direction deviation of the target similar information vehicle in the time period corresponding to the abnormal access time set and the third vehicle information similarity.

[0031] Further, the method of determining the possibility of vehicle abnormality of the target similar information vehicle in the time period corresponding to the abnormal access time set according to the direction deviation of the target similar information vehicle in the time period corresponding to the abnormal access time set and the third vehicle information similarity includes the following specific methods:

[0032] The normalized value of the product of the direction deviation of the target similar information vehicle in the time period corresponding to the abnormal access time set and the third vehicle information similarity is recorded as the vehicle abnormality possibility of the target similar information vehicle in the time period corresponding to the abnormal access time set.

[0033] Further, the method of determining abnormal nodes and normal nodes according to the possibility of vehicle abnormality and stopping the authorization of all edge servers to all abnormal nodes includes:

[0034] Record vehicles whose abnormal probability is greater than or equal to the preset abnormal classification threshold as abnormal nodes, and stop all edge servers from authorizing all abnormal nodes;

[0035] Vehicles whose abnormality probability is less than the preset abnormal classification threshold are recorded as normal nodes, and the edge server maintains authorization for all normal nodes.

[0036] Further, the method of implementing security access control of IoT information at the current collection moment according to the position relationship of the target normal node at the current collection moment and the adjacent collection moment relative to the target edge server, the remaining memory capacity of all edge servers, and the possibility of vehicle abnormality of the target normal node in the time period corresponding to the abnormal access moment set includes the following specific methods:

[0037] The calculation formula for the matching priority between the target normal node and the target edge server at the current collection time is:

[0038]

[0039] In the formula, q k,w,i represents the matching priority between the target normal node k and the target edge server w at the current collection time i; s k,w,i-1 represents the distance between the target normal node k and the target edge server w at the previous collection time i-1 before the current collection time; s k,w,i represents the distance between the target normal node k and the target edge server w at the current collection time i; M w represents the remaining memory capacity of the target edge server w; represents the mean value of the remaining memory capacity of all edge servers in the Internet of Vehicles; V k represents the probability of vehicle abnormality at the target normal node k in the time period corresponding to the abnormal access time set;

[0040] Obtain the matching priority between the target normal node and any edge server, and use the edge server corresponding to the largest matching priority as the edge server matching the target normal node;

[0041] Obtain the edge server that matches any normal node, and send the vehicle information of the normal node at the current collection time to the matching edge server to achieve IoT information security access control.

[0042] In a second aspect, an embodiment of the present invention further provides an Internet of Things information security access control system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned methods when executing the computer program.

[0043] The beneficial effects of the present invention are:

[0044] Based on the characteristic that malicious attacks often disguise themselves as a large number of vehicles transmitting vehicle information to the same edge server, causing a sudden increase in the number of visits to the edge server, the present application divides the abnormal access moments and abnormal access moment sets in the collection moments according to the target collection moment and the number of visits to all edge servers at the collection moments adjacent to the target collection moment; considering that normally operating vehicles are restricted by roads when moving on roads, the displacement and movement directions of vehicles in the vicinity are similar, while the vehicle information disguised and generated by malicious attacks is random, which will cause mutations in vehicle information, and cannot guarantee the similarity between vehicle information and the information of real vehicles in the vicinity, based on the abnormal access moments, combined with the information of vehicle positions, vehicles with similar information and vehicle groups with similar information are divided, and further combined with any vehicle in the similar information vehicle group The difference between the vehicle directions of the target vehicle and other vehicles, as well as the difference between the vehicle information similarities, is used to determine the possibility of malicious attacks on any vehicle in the time period corresponding to the abnormal access time set, and the vehicle abnormality possibility is obtained. The abnormal nodes and normal nodes are determined according to the vehicle abnormality possibility, and the authorization of all edge servers to all abnormal nodes is stopped to avoid the risk of information leakage caused by hidden malicious nodes; finally, according to the position relationship of the target normal node at the current collection time and the adjacent collection time relative to the target edge server, the remaining memory capacity of all edge servers, and the vehicle abnormality possibility of the target normal node in the time period corresponding to the abnormal access time set, the security access control of the Internet of Things information at the current collection time is realized, and the problem of Internet of Things information leakage caused by the hidden malicious nodes in the Internet of Vehicles during the access process is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0046] Figure 1 A flowchart of an Internet of Things information security access control method provided by an embodiment of the present invention;

[0047] Figure 2 A flowchart for obtaining the possibility of malicious attacks provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] See also Figure 1 , which shows a flow chart of an Internet of Things information security access control method provided by an embodiment of the present invention, the method comprising the following steps:

[0050] Step S001, collecting the vehicle position, vehicle direction, remaining memory capacity and access volume of the edge server at the current collection time and a preset number of collection times before.

[0051] All vehicles included in the Internet of Vehicles are equipped with wireless communication on-board units, and information exchange between vehicles in the Internet of Vehicles can be achieved through the wireless communication on-board units. Specifically, the wireless communication on-board unit is needed to realize communication between the vehicle and the edge server. The edge server uploads the received vehicle information to the cloud computing machine. The cloud computing machine selects the edge server where another vehicle that needs to exchange information between vehicles is located, and uploads the vehicle information to the edge server selected by the cloud computing machine. After receiving the information, the edge server transmits the vehicle information to another vehicle that needs to exchange information between vehicles.

[0052] The vehicle position and direction are collected using a wireless communication vehicle-mounted unit.

[0053] Collect the remaining memory capacity and access volume of each edge server included in the Internet of Vehicles.

[0054] It should be understood that before a vehicle included in the Internet of Vehicles accesses each edge server included in the Internet of Vehicles and other vehicles included in the Internet of Vehicles, authorization from the edge server is required.

[0055] Preferably, in one embodiment of the present application, when collecting the vehicle position, vehicle direction, remaining memory capacity of the edge server and the number of visits, the data collection time interval in this embodiment is 1 second, and the data within the current collection time and the previous day are collected. In the actual application process, as other implementation methods, the implementer can decide the collection time interval and sampling number of the vehicle position, vehicle direction, remaining memory capacity of the edge server and the number of visits according to the actual situation, and this application does not impose any special restrictions.

[0056] At this point, the vehicle location, vehicle direction, remaining memory capacity of the edge server, and access volume are obtained.

[0057] Step S002: record any collection time as a target collection time, and determine an abnormal access time and an abnormal access time set according to the access volume of all edge servers at the target collection time and the collection time adjacent to the target collection time.

[0058] When the Internet of Vehicles is attacked by malicious attacks, the malicious attacks often disguise themselves as a large number of vehicles transmitting vehicle information to the same edge server. The number of visits to the edge server will suddenly increase, causing the edge server to be overloaded and the data processing capacity to drop significantly, affecting the normal information transmission of the Internet of Vehicles. However, not all sudden increases in the number of visits to edge servers correspond to malicious attacks. When the traffic volume suddenly increases, such as during the morning and evening rush hours on weekdays, the same situation will occur. Therefore, it is necessary to identify the situation of malicious attacks on the Internet of Vehicles based on the number of visits to the edge server.

[0059] Any collection time is recorded as the target collection time.

[0060] The malicious attack possibility at the target collection time is determined according to the access volume of all edge servers at the target collection time and the collection time adjacent to the target collection time.

[0061] Preferably, as an embodiment of the present application, the average of the visits to all edge servers at all collection moments before the target collection moment is recorded as the average of the historical visits to the target collection moment; the cumulative sum of the visits to all edge servers at the target collection moment is recorded as the cumulative visits to the target collection moment; the difference between the cumulative visits to the target collection moment and the previous collection moment is recorded as the differential cumulative visits to the target collection moment; the product of the differential cumulative visits to the target collection moment and the cumulative visits is recorded as the first product to the target collection moment; the normalized value of the ratio of the first product to the historical visits to the target collection moment is recorded as the possibility of malicious attack at the target collection moment.

[0062] It should be noted that this embodiment uses the Z-Score standard normalization method to calculate the normalized value. In actual application, the implementer can use other methods of the prior art such as maximum and minimum normalization method, sigmoid function, etc. to calculate the normalized value, which is not limited here. In the process of calculating the ratio, in order to avoid the situation where the denominator is zero, it is necessary to add a preset value to the denominator. The preset value in the embodiment is 1.

[0063] It is understandable that the malicious attack possibility at the first collection moment cannot be calculated according to the above method, so the malicious attack possibility at the first collection moment is directly assigned a value of 0.

[0064] The greater the difference between the access volume of all edge servers at the target collection time and the adjacent collection time of the target collection time, the greater the possibility of malicious attack at the target collection time, and the greater the possibility of the Internet of Vehicles being attacked by malicious attacks at the target collection time. The flowchart for obtaining the possibility of malicious attacks is shown in Figure 2. Figure 2 shown.

[0065] An abnormal access threshold is set. In this implementation, the abnormal access threshold is set to 0.75. When the malicious attack possibility at the target collection moment is greater than the abnormal access threshold, the target collection moment is marked as an abnormal access moment.

[0066] The same method can be used to determine abnormal access moments among all collection moments.

[0067] The set of continuous abnormal access time that includes the current collection time is recorded as the abnormal access time set.

[0068] It should be noted that the latest collection time included in the abnormal access time set is the current collection time.

[0069] It is understandable that there may be multiple sets of continuous abnormal access moments. For example, all the collection moments from 10:20 to 11:00 are abnormal access moments, and all the collection moments from 11:30 to 11:40 are abnormal access moments. Then all the collection moments from 10:20 to 11:00 constitute a set of continuous abnormal access moments, and all the collection moments from 11:30 to 11:40 constitute another different set of continuous abnormal access moments. It is also understandable that, if the current collection moment is 11:00, all the collection moments from 10:20 to 11:00 constitute the time period corresponding to the abnormal access moment set, that is, the time period from 10:20 to 11:00.

[0070] At this point, the abnormal access time and the abnormal access time set are determined.

[0071] Step S003, record any abnormal access moment in the abnormal access moment set as the target abnormal access moment, record any two different vehicles in the vehicle network as the first vehicle and the second vehicle, determine the vehicle information similarity of the first vehicle and the second vehicle in the time period corresponding to the abnormal access moment set according to the difference and distance between the vehicle positions of the first vehicle and the second vehicle at all abnormal access moments included in the abnormal access moment set and adjacent moments of the abnormal access moment, and the angle between the vehicle directions of the first vehicle and the second vehicle at all abnormal access moments included in the abnormal access moment set, divide similar information vehicles and similar information vehicle groups according to the vehicle information similarity, determine the vehicle abnormality possibility of any vehicle in the time period corresponding to the abnormal access moment set according to the difference between the vehicle directions of any vehicle and other vehicles in the similar information vehicle group, and the difference between the vehicle information similarities, determine the abnormal node and the normal node according to the vehicle abnormality possibility, and stop all edge servers from authorizing all abnormal nodes.

[0072] When a normal vehicle moves on the road, due to the limitation of the road, the displacement and movement direction of vehicles in the vicinity are similar. However, the vehicle information generated by malicious attack disguise is random, which will cause the mutation of vehicle information and cannot guarantee the similarity between the vehicle information and the real vehicle information in the vicinity. Therefore, the analysis is based on the abnormal access time and combined with the vehicle location information.

[0073] Any abnormal access time in the abnormal access time set is recorded as the target abnormal access time, and any two different vehicles in the vehicle network are recorded as the first vehicle and the second vehicle.

[0074] The distance between the vehicle position at the target abnormal access time and the vehicle position at the moment before the target abnormal access time is recorded as the displacement of the vehicle at the target abnormal access time. The absolute value of the difference between the displacements of the first vehicle and the second vehicle at the target abnormal access time is recorded as the displacement difference between the first vehicle and the second vehicle at the target abnormal access time. The mean of the displacement differences of all abnormal access times included in the abnormal access time set of all different vehicles in the Internet of Vehicles is recorded as the mean of the displacement differences in the time period corresponding to the abnormal access time set.

[0075] The angle between the vehicle directions of the first vehicle and the second vehicle at the target abnormal access time is recorded as the vehicle direction difference between the first vehicle and the second vehicle at the target abnormal access time.

[0076] The distance between the vehicle positions of the first vehicle and the second vehicle at the target abnormal access time is recorded as the vehicle distance between the first vehicle and the second vehicle at the target abnormal access time, and the mean of the vehicle distances between the first vehicle and the second vehicle at all abnormal access times included in the abnormal access time set is recorded as the average vehicle distance between the first vehicle and the second vehicle in the time period corresponding to the abnormal access time set. The mean of the vehicle distances between all different vehicles in the Internet of Vehicles at the same abnormal access time included in the abnormal access time set is recorded as the comparative vehicle average distance in the time period corresponding to the abnormal access time set.

[0077] Based on the displacement differences and vehicle direction differences of the first vehicle and the second vehicle in all abnormal access moments included in the abnormal access moment set, the average vehicle distance between the first vehicle and the second vehicle in the time period corresponding to the abnormal access moment set, the mean of the displacement differences in the time period corresponding to the abnormal access moment set and the compared vehicle average distance, the vehicle information similarity of the first vehicle and the second vehicle in the time period corresponding to the abnormal access moment set is determined.

[0078] The maximum value of the displacement difference between the first vehicle and the second vehicle at all abnormal access moments included in the abnormal access moment set is recorded as the maximum displacement difference between the first vehicle and the second vehicle in the time period corresponding to the abnormal access moment set; the ratio of the mean displacement difference in the time period corresponding to the abnormal access moment set to the maximum displacement difference between the first vehicle and the second vehicle in the time period corresponding to the abnormal access moment set is recorded as the first ratio; the ratio of the average distance of the compared vehicles in the time period corresponding to the abnormal access moment set to the average distance of the vehicles between the first vehicle and the second vehicle in the time period corresponding to the abnormal access moment set is recorded as the second ratio; the ratio of the number 1 to the mean value of the vehicle direction difference between the first vehicle and the second vehicle at all abnormal access moments included in the abnormal access moment set is recorded as the third ratio; the normalized value of the product of the first ratio, the second ratio and the third ratio is recorded as the vehicle information similarity between the first vehicle and the second vehicle in the time period corresponding to the abnormal access moment set.

[0079] It should be noted that this embodiment uses the Z-Score standard normalization method to calculate the normalized value. In actual application, the implementer may use other methods of the prior art such as the maximum and minimum normalization method, the sigmoid function, etc. to calculate the normalized value, which is not limited here. In the process of calculating the ratio of the first ratio, the second ratio, and the third ratio, in order to avoid the situation where the denominator is zero, it is necessary to add a preset value to the denominator. The preset value in this embodiment is 0.01.

[0080] The first ratio and the second ratio become larger when the vehicle displacement information and the vehicle direction information of the first vehicle and the second vehicle at all abnormal access moments included in the abnormal access time set are more similar. At the same time, the third ratio becomes larger when the vehicle direction difference between the first vehicle and the second vehicle at all abnormal access moments included in the abnormal access time set is smaller. At this time, the similarity between the vehicle information at all abnormal access moments included in the abnormal access time set and the information of real vehicles in the nearby range becomes greater.

[0081] A similarity division threshold is set. In this implementation, the similarity division threshold is set to 0.8. For an abnormal access time set, when the vehicle information similarity is greater than the similarity division threshold, two different vehicles corresponding to the vehicle information similarity are divided into a group of similar information vehicles. All similar information vehicles divided from the same abnormal access time set are recorded as a similar information vehicle group.

[0082] Any vehicle in the similar information vehicle group is recorded as a target similar information vehicle.

[0083] The mean of the vehicle directions of all the similar information vehicles in the similar information vehicle group at all the abnormal access moments in the abnormal access moment set is recorded as the first overall heading direction of the similar information vehicle group in the time period corresponding to the abnormal access moment set, the mean of the vehicle directions of all the abnormal access moments in the target similar information vehicle in the abnormal access moment set is recorded as the second overall heading direction of the target similar information vehicle in the time period corresponding to the abnormal access moment set, and the angle between the first overall heading direction of the similar information vehicle group in the time period corresponding to the abnormal access moment set and the second overall heading direction of the target similar information vehicle in the time period corresponding to the abnormal access moment set is recorded as the direction deviation of the target similar information vehicle in the time period corresponding to the abnormal access moment set. The maximum value of the vehicle information similarity between the target similar information vehicle and other vehicles in the time period corresponding to the abnormal access moment set is recorded as the first vehicle information similarity of the target similar information vehicle; the maximum value of the vehicle information similarity in the time period corresponding to the abnormal access moment set of all two different vehicles is recorded as the second vehicle information similarity. The ratio of the second vehicle information similarity to the first vehicle information similarity of the target similar information vehicle is recorded as the third vehicle information similarity of the target similar information vehicle in the time period corresponding to the abnormal access moment set.

[0084] The vehicle abnormality possibility of the target similar information vehicle in the time period corresponding to the abnormal access time set is determined based on the direction deviation of the target similar information vehicle in the time period corresponding to the abnormal access time set and the third vehicle information similarity.

[0085] Preferably, as an embodiment of the present application, the normalized value of the product of the directional deviation of the target similar information vehicle in the time period corresponding to the abnormal access time set and the third vehicle information similarity is recorded as the vehicle abnormality possibility of the target similar information vehicle in the time period corresponding to the abnormal access time set.

[0086] It should be noted that this embodiment uses the Z-Score standard normalization method to calculate the normalized value. In actual application, the implementer may use other methods in the prior art such as the maximum and minimum value normalization method, the sigmoid function, etc. to calculate the normalized value, which is not limited here.

[0087] The possibility of vehicle abnormality is a significance evaluation of the malicious attack characteristics presented by the target similar information vehicle in the time period corresponding to the abnormal access time set. The greater the possibility of vehicle abnormality, the greater the possibility that the target similar information vehicle corresponds to virtual information generated by malicious attacks.

[0088] Set an abnormal classification threshold. In this implementation, the abnormal classification threshold is set to 0.5. Vehicles with a probability of abnormality greater than or equal to the abnormal classification threshold are recorded as abnormal nodes, and all edge servers are stopped from authorizing all abnormal nodes. All abnormal nodes cannot access all edge servers included in the Internet of Vehicles. Vehicles with a probability of abnormality less than the abnormal classification threshold are recorded as normal nodes, and the edge server is kept authorized to all normal nodes. All normal nodes can normally access all edge servers included in the Internet of Vehicles.

[0089] At this point, abnormal nodes and normal nodes are determined, and all edge servers are stopped from authorizing all abnormal nodes.

[0090] Step S004, record any normal node as the target normal node, record any edge server as the target edge server, and implement security access control of IoT information at the current collection moment based on the position relationship of the target normal node relative to the target edge server at the current collection moment and the adjacent collection moment, the remaining memory capacity of all edge servers, and the possibility of vehicle abnormality of the target normal node in the time period corresponding to the abnormal access moment set.

[0091] When a normal node accesses an edge server included in the Internet of Vehicles, it is necessary to determine the edge server that matches the normal node. When the distance between the normal node and the edge server is getting closer, the movement trend of the normal node relative to the edge server is more stable. At the same time, when the remaining memory capacity of the edge server is smaller, the edge server should be selected as the edge server that matches the normal node.

[0092] Any normal node is recorded as the target normal node, and any edge server is recorded as the target edge server. The matching priority of the target normal node and the target edge server at the current collection time is determined according to the position relationship of the target normal node relative to the target edge server at the current collection time and the adjacent collection time, the remaining memory capacity of all edge servers, and the possibility of vehicle abnormality in the time period corresponding to the target normal node in the abnormal access time set.

[0093]

[0094] In the formula, q k,w,i represents the matching priority between the target normal node k and the target edge server w at the current collection time i; s k,w,i-1 represents the distance between the target normal node k and the target edge server w at the previous collection time i-1 before the current collection time; s k,w,i represents the distance between the target normal node k and the target edge server w at the current collection time i; M w represents the remaining memory capacity of the target edge server w; represents the mean value of the remaining memory capacity of all edge servers in the Internet of Vehicles; V k Indicates the possibility of vehicle abnormality at the target normal node k in the time period corresponding to the abnormal access time set.

[0095] Among them, the location of the edge server is fixed and is known information when the vehicle network is constructed. Therefore, according to the location of the edge server and the vehicle location of the normal node, the distance between the normal node and the edge server can be obtained.

[0096] The matching priority between the target normal node and any edge server can be obtained in the same manner, and the edge server corresponding to the maximum matching priority is used as the edge server matching the target normal node.

[0097] The same method can be used to obtain an edge server that matches any normal node.

[0098] It should be noted that when the remaining memory capacity of the edge server matching the normal node is 0, the edge server corresponding to the second largest matching priority is selected as the edge server matching the target normal node. When the remaining memory capacity of the edge server corresponding to the largest matching priority and the edge server corresponding to the second largest matching priority are both 0, the edge server corresponding to the third largest matching priority is selected as the edge server matching the target normal node. And so on, the edge server matching any normal node is determined.

[0099] The vehicle information of the normal node at the current collection time is sent to the matching edge server to realize the security access control of IoT information.

[0100] Based on the same inventive concept as the above method, an embodiment of the present invention also provides an Internet of Things information security access control system, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above-mentioned Internet of Things information security access control methods are implemented.

[0101] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for controlling information security access to the Internet of Things, characterized in that: The method comprises the following steps: Collect the vehicle position, vehicle direction, remaining memory capacity and access volume of the edge server at the current collection time and a preset number of collection times before; Record any collection time as a target collection time, and determine an abnormal access time and an abnormal access time set according to the access volume of all edge servers at the target collection time and the collection time adjacent to the target collection time; Record any abnormal access moment in the abnormal access moment set as the target abnormal access moment, record any two different vehicles in the vehicle network as the first vehicle and the second vehicle, determine the vehicle information similarity of the first vehicle and the second vehicle in the time period corresponding to the abnormal access moment set according to the difference and distance between the vehicle positions of the first vehicle and the second vehicle at all abnormal access moments included in the abnormal access moment set and adjacent moments of the abnormal access moment, and the angle between the vehicle directions of the first vehicle and the second vehicle at all abnormal access moments included in the abnormal access moment set, divide similar information vehicles and similar information vehicle groups according to the vehicle information similarity, determine the vehicle abnormality possibility of any vehicle in the time period corresponding to the abnormal access moment set according to the difference between the vehicle directions of any vehicle and other vehicles in the similar information vehicle group, and the difference between the vehicle information similarities, determine the abnormal node and the normal node according to the vehicle abnormality possibility, and stop all edge servers from authorizing all abnormal nodes; Any normal node is recorded as the target normal node, and any edge server is recorded as the target edge server. According to the position relationship of the target normal node relative to the target edge server at the current collection time and the adjacent collection time, the remaining memory capacity of all edge servers, and the possibility of vehicle abnormality of the target normal node in the time period corresponding to the abnormal access time set, the security access control of the Internet of Things information at the current collection time is realized.

2. The method for controlling information security access to the Internet of Things according to claim 1, characterized in that: The specific method of determining the abnormal access time and the abnormal access time set according to the access volume of all edge servers at the target collection time and the collection time adjacent to the target collection time is as follows: The average of the visits to all edge servers at all collection times before the target collection time is recorded as the historical visit average at the target collection time; The cumulative sum of the visits to all edge servers at the target collection time is recorded as the cumulative visits at the target collection time, the difference between the cumulative visits at the target collection time and the collection time before the target collection time is recorded as the differential cumulative visits at the target collection time, and the product of the differential cumulative visits at the target collection time and the cumulative visits is recorded as the first product at the target collection time; The normalized value of the ratio of the first product of the target collection time to the average of the historical access volume is recorded as the malicious attack possibility at the target collection time; When the malicious attack possibility of the target collection moment is greater than the preset abnormal access threshold, the target collection moment is marked as an abnormal access moment, and the set of continuous abnormal access moments including the current collection moment is recorded as the abnormal access moment set.

3. The method for controlling information security access to the Internet of Things according to claim 1, characterized in that: The method of determining the similarity of vehicle information of the first vehicle and the second vehicle in the time period corresponding to the abnormal access time set according to the difference and distance between the vehicle positions of the first vehicle and the second vehicle at all abnormal access times included in the abnormal access time set and the adjacent times of the abnormal access times, and the angle between the vehicle directions of the first vehicle and the second vehicle at all abnormal access times included in the abnormal access time set, includes the following specific methods: The distance between the vehicle position at the target abnormal access time and the vehicle position at the moment before the target abnormal access time is recorded as the displacement of the vehicle at the target abnormal access time; the absolute value of the difference between the displacements of the first vehicle and the second vehicle at the target abnormal access time is recorded as the displacement difference between the first vehicle and the second vehicle at the target abnormal access time; the mean of the displacement differences of all different vehicles in the Internet of Vehicles at all abnormal access times included in the abnormal access time set is recorded as the mean of the displacement differences in the time period corresponding to the abnormal access time set; The angle between the vehicle directions of the first vehicle and the second vehicle at the target abnormal access time is recorded as the vehicle direction difference between the first vehicle and the second vehicle at the target abnormal access time; The distance between the vehicle positions of the first vehicle and the second vehicle at the target abnormal access time is recorded as the vehicle distance between the first vehicle and the second vehicle at the target abnormal access time; the mean of the vehicle distances between the first vehicle and the second vehicle at all abnormal access times included in the abnormal access time set is recorded as the average vehicle distance between the first vehicle and the second vehicle in the time period corresponding to the abnormal access time set; the mean of the vehicle distances between all different vehicles in the Internet of Vehicles at the same abnormal access time included in the abnormal access time set is recorded as the comparative vehicle average distance in the time period corresponding to the abnormal access time set; Based on the displacement differences and vehicle direction differences of the first vehicle and the second vehicle in all abnormal access moments included in the abnormal access moment set, the average vehicle distance between the first vehicle and the second vehicle in the time period corresponding to the abnormal access moment set, the mean of the displacement differences in the time period corresponding to the abnormal access moment set and the compared vehicle average distance, the vehicle information similarity of the first vehicle and the second vehicle in the time period corresponding to the abnormal access moment set is determined.

4. The method for controlling information security access to the Internet of Things according to claim 3, characterized in that: The method of determining the vehicle information similarity between the first vehicle and the second vehicle in the time period corresponding to the abnormal access time set according to the displacement differences and vehicle direction differences of the first vehicle and the second vehicle at all abnormal access time moments included in the abnormal access time set, the average vehicle distance between the first vehicle and the second vehicle in the time period corresponding to the abnormal access time set, the mean of the displacement differences in the time period corresponding to the abnormal access time set, and the comparison of the average vehicle distance, includes the following specific methods: The maximum value of the displacement difference between the first vehicle and the second vehicle at all abnormal access moments included in the abnormal access moment set is recorded as the maximum displacement difference between the first vehicle and the second vehicle in the time period corresponding to the abnormal access moment set, and the ratio of the mean displacement difference in the time period corresponding to the abnormal access moment set to the maximum displacement difference between the first vehicle and the second vehicle in the time period corresponding to the abnormal access moment set is recorded as the first ratio; The ratio of the average distance of the comparison vehicles in the time period corresponding to the abnormal access time set to the average distance of the vehicles between the first vehicle and the second vehicle in the time period corresponding to the abnormal access time set is recorded as a second ratio; The ratio of the number 1 to the average of the vehicle direction differences between the first vehicle and the second vehicle at all abnormal access moments included in the abnormal access moment set is recorded as a third ratio; The normalized value of the product of the first ratio, the second ratio and the third ratio is recorded as the vehicle information similarity between the first vehicle and the second vehicle in the time period corresponding to the abnormal access time set.

5. The method for controlling information security access to the Internet of Things according to claim 1, characterized in that: The specific method of dividing similar information vehicles and similar information vehicle groups according to vehicle information similarity includes: Two different vehicles corresponding to vehicle information similarities greater than a preset similarity division threshold are divided into a group of similar information vehicles, and all similar information vehicles divided from the same abnormal access time set are recorded as a similar information vehicle group.

6. The method for controlling information security access to the Internet of Things according to claim 1, characterized in that: The method of determining the possibility of vehicle abnormality of any one vehicle in the time period corresponding to the abnormal access time set according to the difference between the vehicle directions of any one vehicle in the similar information vehicle group and other vehicles and the difference between the vehicle information similarities includes: The mean of the vehicle directions of all the similar information vehicles included in the similar information vehicle group at all the abnormal access moments included in the abnormal access moment set is recorded as the first overall heading direction of the similar information vehicle group in the time period corresponding to the abnormal access moment set, the mean of the vehicle directions of all the abnormal access moments included in the abnormal access moment set of the target similar information vehicle is recorded as the second overall heading direction of the target similar information vehicle in the time period corresponding to the abnormal access moment set, and the angle between the first overall heading direction of the similar information vehicle group in the time period corresponding to the abnormal access moment set and the second overall heading direction of the target similar information vehicle in the time period corresponding to the abnormal access moment set is recorded as the direction deviation of the target similar information vehicle in the time period corresponding to the abnormal access moment set; The maximum value of the vehicle information similarity between the target similar information vehicle and other vehicles in the time period corresponding to the abnormal access time set is recorded as the first vehicle information similarity of the target similar information vehicle; the maximum value of the vehicle information similarity in the time period corresponding to the abnormal access time set of all two different vehicles is recorded as the second vehicle information similarity; The ratio of the second vehicle information similarity to the first vehicle information similarity of the target similar information vehicle is recorded as the third vehicle information similarity of the target similar information vehicle in the time period corresponding to the abnormal access time set; The vehicle abnormality possibility of the target similar information vehicle in the time period corresponding to the abnormal access time set is determined based on the direction deviation of the target similar information vehicle in the time period corresponding to the abnormal access time set and the third vehicle information similarity.

7. The method for controlling information security access to the Internet of Things according to claim 6, characterized in that: The method of determining the possibility of vehicle abnormality of the target similar information vehicle in the time period corresponding to the abnormal access time set according to the direction deviation of the target similar information vehicle in the time period corresponding to the abnormal access time set and the third vehicle information similarity includes the following specific methods: The normalized value of the product of the direction deviation of the target similar information vehicle in the time period corresponding to the abnormal access time set and the third vehicle information similarity is recorded as the vehicle abnormality possibility of the target similar information vehicle in the time period corresponding to the abnormal access time set.

8. The method for controlling information security access to the Internet of Things according to claim 1, characterized in that: The specific method of determining abnormal nodes and normal nodes according to the possibility of vehicle abnormality and stopping the authorization of all edge servers to all abnormal nodes includes: Record vehicles whose abnormal probability is greater than or equal to the preset abnormal classification threshold as abnormal nodes, and stop all edge servers from authorizing all abnormal nodes; Vehicles whose abnormality probability is less than the preset abnormal classification threshold are recorded as normal nodes, and the edge server maintains authorization for all normal nodes.

9. The method for controlling information security access to the Internet of Things according to claim 1, characterized in that: The method of implementing security access control of IoT information at the current collection moment according to the position relationship of the target normal node at the current collection moment and the adjacent collection moment relative to the target edge server, the remaining memory capacity of all edge servers, and the possibility of vehicle abnormality of the target normal node in the time period corresponding to the abnormal access moment set includes the following specific methods: The calculation formula for the matching priority between the target normal node and the target edge server at the current collection time is: In the formula, q k,w,i represents the matching priority between the target normal node k and the target edge server w at the current collection time i; s k,w,i-1 represents the distance between the target normal node k and the target edge server w at the previous collection time i-1 before the current collection time; s k,w,i represents the distance between the target normal node k and the target edge server w at the current collection time i; M w represents the remaining memory capacity of the target edge server w; represents the mean value of the remaining memory capacity of all edge servers in the Internet of Vehicles; V k represents the probability of vehicle abnormality at the target normal node k in the time period corresponding to the abnormal access time set; Obtain the matching priority between the target normal node and any edge server, and use the edge server corresponding to the largest matching priority as the edge server matching the target normal node; Obtain the edge server that matches any normal node, and send the vehicle information of the normal node at the current collection time to the matching edge server to achieve IoT information security access control.

10. An Internet of Things information security access control system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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