CN109474463B - IoT edge equipment trust evaluation method, device and system and proxy server - Google Patents
IoT edge equipment trust evaluation method, device and system and proxy server Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/04—Network management architectures or arrangements
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
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Abstract
The invention discloses an IoT edge device trust evaluation method, which adopts a time window method to obtain a predicted trust value in the current period according to the trust value of the device in the past period, because the trust has time correlation, the predicted trust value defines the possible range of the current trust value to a certain extent, the difference value between the received actual trust value and the predicted trust value is compared with an error threshold value to judge whether the interactive trust value is credible, if the interactive trust value is not credible, the trust value is corrected by implementing a punishment system, and then the aggregate calculation of feedback trust is carried out after the correction, so that a more reliable overall trust value can be obtained, the malicious evaluation caused by malicious behaviors such as false feedback, malicious attack, collusion cheat and the like is avoided, and the reliability of the overall trust and the safety of the system are improved. The invention also discloses an IoT edge device trust evaluation device, an edge proxy server and an IoT edge device trust evaluation system, which have the beneficial effects.
Description
Technical Field
The invention relates to the field of internet of things, in particular to an IoT edge device trust evaluation method, an IoT edge device trust evaluation device, an edge proxy server and an IoT edge device trust evaluation system.
Background
The integration of the internet of things and edge computing is one of the hot spots of current research, and the edge computing service can significantly reduce the amount of data to be transmitted, reduce network delay and respond to service requests more quickly. Due to lack of trust among the devices at the edge of the Internet of things, people are prevented from using the edge computing of the Internet of things as outsourcing computing service. It is therefore desirable to use reliable, lightweight trust mechanisms to guarantee quality of service collaboration and establish reliable trust between internet of things edge devices.
The architecture of the current internet of things edge computing system based on a trust computing mechanism of feedback is shown in fig. 1: the system mainly comprises a network layer, a proxy layer and a device layer. The network layer adopts a traditional cloud computing platform, and the agent layer is used for monitoring the service behavior of the equipment of the Internet of things and aggregating the feedback from the equipment of the Internet of things, so that the trust computing overhead on the equipment layer is reduced. The device layer mainly submits the trust values evaluated with each other to the agent in the service cooperation process. While the overall trust of a device includes direct trust between devices, feedback trust from other edge devices, and feedback trust from service agents. And trust calculation is completely finished by an agent layer and a device layer without participation of a central network. For the current network bandwidth and reliability, the trust calculation at the network edge can obtain shorter response time, higher execution efficiency and less network load pressure.
The existing method for solving the trust problem among the edge devices of the Internet of things mainly obtains the integral trust of the devices by aggregating the direct trust among the devices and the objective rating of the devices by an agent, so that an effective trust mechanism is established. In an environment with an edge device of the internet of things, if service collaboration is performed between two devices, one of the devices sends a trust value request of a partner to its agent. Because various security risks and attacks in the internet of things and various malicious behaviors such as false feedback, malicious attacks and collusion cheating are suffered by edge equipment of the internet of things, false trust values may exist in direct trust among the equipment and all feedback received by an agent from other equipment of an equipment layer, so that certain errors exist in feedback trust obtained after aggregation calculation of the agent layer and calculation of the overall trust of the equipment is influenced, the accuracy rate of the overall trust is low, and the security and the reliability of a system are influenced.
Therefore, how to improve the reliability of the evaluation of the edge device in the trust mechanism and improve the security and reliability of the environment of the edge device of the internet of things is a technical problem to be solved by technical personnel in the field.
Disclosure of Invention
The invention aims to provide an IoT edge device trust evaluation method, which can detect and judge the trust value of an edge device and correct the trust value of an untrusted device through a punishment system so as to cope with malicious behaviors such as false feedback, malicious attack, collusion cheating and the like, thereby improving the accuracy of the integral trust and the safety and reliability of a system; another object of the present invention is to provide an IoT edge device trust evaluation apparatus, an edge proxy server and an IoT edge device trust evaluation system.
To solve the above technical problem, the present invention provides an IoT edge device trust evaluation method, including:
the method comprises the steps that when an edge proxy server receives a trust value request of first equipment, edge equipment which is interacted with the first equipment at present is screened out, and second equipment is obtained;
sending a first device trust value calculation instruction to the second device;
predicting the trust value of the second equipment to the first equipment according to the stored historical trust value to obtain a predicted trust value;
calculating the difference value between the received actual trust value and the corresponding predicted trust value;
comparing the difference value with an error threshold value, and dividing the direct trust value into an abnormal trust value and a normal trust value according to a comparison result;
correcting the abnormal trust value through a punishment mechanism to obtain a corrected trust value;
aggregating the corrected trust value and the normal trust value to obtain a feedback trust value;
and feeding back the feedback trust value to a request initiator so that the request initiator performs fusion calculation on the feedback trust value and a direct trust value obtained by evaluating the first equipment by the request initiator to obtain an overall trust value of the first equipment.
Preferably, predicting the trust value of the first device by the second device from the stored historical trust value comprises:
screening out a trust value of the second equipment to the first equipment within a preset time period span to obtain a historical trust value;
and calculating the average value of the historical trust values, and taking the calculated average value as a prediction trust value.
Preferably, the method for calculating the direct trust value includes:
acquiring the total number of positive scores and the total number of negative scores of the capability of the opposite side to finish the requested task in the interaction process of the two devices within the preset time; wherein the positive score and the negative score are obtained by scoring the service quality of the opposite party by the two interactive parties;
and counting the proportion of the total number of the positive scores to the total number of the positive scores and the negative scores to obtain a direct trust value.
Preferably, the method for calculating the error threshold includes:
when in use
Presentation device diAnd djWhen n is the time cycle span, calculating an error threshold according to a threshold calculation formula to obtain an error threshold;
the threshold calculation formula specifically includes:
preferably, the fusion calculation of the feedback trust value and the direct trust value evaluated by the request initiator on the first device by itself includes:
acquiring preset weight factors corresponding to direct trust and feedback trust to obtain direct weight and feedback weight;
and summing the product value of the direct trust value and the direct weight and the product value of the feedback trust value and the feedback weight to obtain an integral trust value.
Preferably, the method for determining the weighting factor includes:
when in use
For devices d during Δ t timeiTo djWhen the total number of the negative scores of the task capacity of the request is finished, calculating according to a factor calculation formula, and taking the obtained data as direct weight;
calculating the difference between 1 and the direct weight, and taking the obtained result as the feedback weight;
wherein, the factor calculation formula specifically comprises:
preferably, the correcting the abnormal trust value through a penalty mechanism includes:
when in use
For devices d during Δ t timeiTo djThe total number of negative scores for the ability to complete the requested task,
presentation device diAnd djWhen the direct trust value is within the time delta t, correcting the abnormal trust value according to a correction formula to obtain a corrected trust value;
wherein, the correction formula is specifically as follows:
wherein the beta is a correction factor, and the correction factor is,
the invention discloses an IoT edge device trust evaluation device, which comprises:
the edge device screening unit is used for screening the edge device which is interacted with the first device at present to obtain a second device when receiving a trust value request to the first device;
a calculation instruction sending unit, configured to send a first device trust value calculation instruction to the second device;
the trust prediction unit is used for predicting the trust value of the second equipment to the first equipment according to the stored historical trust value to obtain a predicted trust value;
a difference value calculating unit for calculating the difference value between the received actual trust value and the corresponding predicted trust value;
the difference comparison unit is used for comparing the difference with an error threshold value and dividing the direct trust value into an abnormal trust value and a normal trust value according to a comparison result;
the abnormal correction unit is used for correcting the abnormal trust value through a punishment mechanism to obtain a corrected trust value;
the feedback trust aggregation unit is used for aggregating the corrected trust value and the normal trust value to obtain a feedback trust value;
and the feedback trust sending unit is used for feeding back the feedback trust value to the request initiator so that the request initiator performs fusion calculation on the feedback trust value and a direct trust value obtained by evaluating the first equipment by the request initiator to obtain an overall trust value of the first equipment.
The invention discloses an edge proxy server, comprising:
a memory for storing a program;
a processor configured to implement the steps of the IoT edge device trust evaluation method when executing the program.
The invention discloses an IoT edge device trust evaluation system, which comprises:
a request initiator, configured to send a trust value request for a first device to an edge proxy server; calculating a trust value of the first equipment according to the interaction condition of the first equipment and the first equipment to obtain a direct trust value of the first equipment; when a feedback trust value is received, performing fusion calculation on the feedback trust value and the direct trust value to obtain an overall trust value of the first device;
the edge proxy server is used for screening out the edge equipment which is interacted with the first equipment at present to obtain second equipment when receiving a trust value request to the first equipment; sending a first device trust value calculation instruction to the second device; predicting the trust value of the second equipment to the first equipment according to the stored historical trust value to obtain a predicted trust value; calculating the difference value between the received actual trust value and the corresponding predicted trust value; comparing the difference value with an error threshold value, and dividing the direct trust value into an abnormal trust value and a normal trust value according to a comparison result; correcting the abnormal trust value through a punishment mechanism to obtain a corrected trust value; aggregating the corrected trust value and the normal trust value to obtain a feedback trust value; feeding back the feedback trust value to a request initiator;
the second device is used for calculating the trust value of the first device according to the interaction condition with the first device when receiving the trust value calculation instruction of the first device, so as to obtain an actual trust value; returning the actual trust value to the edge proxy server.
The IoT edge equipment trust evaluation method provided by the invention adopts a time window method to obtain the predicted trust value in the current period according to the trust value of the equipment in the past time period under the structure of an Internet of things edge calculation system based on a feedback trust calculation mechanism, because the trust has time correlation, the predicted trust value defines the possible range of the current trust value to a certain extent, the difference value between the actual trust value and the predicted trust value received currently is compared with a set error threshold value to judge whether the interactive trust value in information interaction is credible, if the interactive trust value is the trust value of the equipment which is not credible, the trust value is corrected by implementing a punishment system to calibrate the untrusted equipment, and the aggregated calculation of feedback trust is carried out after correction, so that a more reliable integral trust value can be obtained, and malicious evaluation caused by malicious behaviors such as false feedback, malicious attack, serial communication cheating and the like is avoided, the reliability of the overall trust and the security of the system can be improved. In addition, the IoT edge device trust evaluation method provided by the embodiment can also be applied to trust calculation in a recommendation system, so that accurate recommendation of devices is realized, and the working efficiency is improved.
The invention also provides an IoT edge device trust evaluation device, an edge proxy server and an IoT edge device trust evaluation system, which have the beneficial effects and are not described herein again.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic diagram of an existing trusted internet of things edge computing architecture based on a cloud platform trusted computing mechanism;
fig. 2 is a signaling diagram of an IoT edge device trust evaluation method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a trust value prediction according to an embodiment of the present invention;
FIG. 4 is a diagram of an edge device d according to an embodiment of the present invention1、d2、d3、d4An edge device interaction diagram;
fig. 5 is a block diagram illustrating a structure of an IoT edge device trust evaluation apparatus according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an edge proxy server according to an embodiment of the present invention;
fig. 7 is a block diagram illustrating an IoT edge device trust evaluation system according to an embodiment of the present invention.
Detailed Description
The core of the invention is to provide an IoT edge device trust evaluation method, which can detect and judge the trust value of the edge device and correct the trust value of the untrusted device through a punishment system so as to cope with malicious behaviors such as false feedback, malicious attack, collusion cheat and the like, thereby improving the accuracy of the integral trust and the safety and reliability of the system; the invention also provides an IoT edge device trust evaluation device, an edge proxy server and an IoT edge device trust evaluation system.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic diagram of an existing trusted internet of things edge computing architecture based on a cloud platform trusted computing mechanism, where the architecture mainly includes three layers: a network layer, a proxy layer, and a device layer. Such as device DiRequest D to its agentjThe trust value mainly comes from the device DiAnd device DjDirect trust between and proxy for other multiple devices with respect to DjAnd feeding back the trust value after aggregation. DiCalculating the direct trust and the feedback trust in a fusion way to obtain DjThe overall trust of. The trust computing mechanism can know the trusted condition of the interaction equipment before interaction, effectively reduces the network risk and has higher reliability.
However, due to various security risks and attacks in the internet of things, the edge devices of the internet of things are subjected to various malicious behaviors such as false feedback, malicious attacks and collusion cheating, and false trust values may exist in direct trust among the devices and all feedback received by the agent from other devices in the device layer, so that the calculation of the overall trust of the devices is influenced. The IoT edge device trust evaluation method provided by the application can detect and judge the edge device trust value and correct the trust value of the untrusted device through a punishment system so as to cope with malicious behaviors such as false feedback, malicious attack, collusion cheating and the like, thereby improving the accuracy of the whole trust and the safety and reliability of the system.
In the present application, existing architectures of an internet of things architecture are basically the same, and in the present embodiment, the architecture shown in fig. 1 is used for description, and fig. 2 is a signaling diagram of an IoT edge device trust evaluation method provided in the present embodiment; the method mainly comprises the following steps:
and step s111, the request initiator sends a trust value request for the first device to the edge proxy server.
The request initiator and the first device are both edge devices. When a request initiator needs to interact with a first device, or the request initiator needs to filter interactive devices, and the like, the trust level of an edge device is required to be acquired, that is, a trust value request for the device is sent to an edge proxy server to which the device belongs.
The trust value of the first device mainly comprises a feedback trust value of other devices to the first device and a direct trust value of the request initiator to the first device, and the integral trust value of the first device can be obtained after fusion.
And step s121, the edge proxy server screens out the edge device which is interacted with the first device at present to obtain a second device.
Of course, the influence of the device that has interacted with the first device in the past on the trust value of the first device may also be considered, and a certain weight may be assigned to the trust value generated in the past interaction to perform the calculation of the overall trust value of the first device.
And step s121, the edge proxy server sends a first device trust value calculation instruction to the second device.
And step s131, the second device calculates the trust value of the first device according to the interaction condition with the first device, and obtains the actual trust value.
And after receiving the instruction, each second device carries out direct trust value calculation on the second device according to the condition that the first device completes the task within a current period of time.
In this embodiment, the calculation process of the direct trust value is not limited (including the calculation of the direct trust value of the first device by each second device and the calculation of the direct trust value of the first device by the request initiator), and the existing calculation method of the direct trust value may be referred to.
Preferably, the total number of positive scores and the total number of negative scores of the capability of the opposite side to complete the requested task in the interaction process of the two devices within the preset time can be obtained; wherein, the positive scoring and the negative scoring are obtained by scoring the service quality of the opposite side by the two interactive sides; and counting the proportion of the total number of the positive scores to the total number of the positive scores and the negative scores to obtain a direct trust value.
In particular, assume that
For devices d during Δ t time1To d2The total number of positive scores for the ability to complete the requested task,
for devices d during Δ t time1To d2The total number of negative scores for the ability to complete the requested task, the direct confidence value, which may be calculated with reference to the following equation:
the above calculation method of the direct trust value is only described as an example, and other calculation methods are not described herein.
Step s132, the second device returns the actual trust value to the edge proxy server.
And step s123, the edge proxy server predicts the trust value of the second device to the first device according to the stored historical trust value to obtain a predicted trust value.
With the increasing frequency of network attacks in the environment of the edge device of the internet of things, the edge device may be attacked by malicious devices, the trust value may change, and it may occur that an untrusted device provides a false trust value to an agent of an edge layer in the service cooperation process, and if the trust value from the device layer is not judged in the agent layer, an unreliable overall trust may be obtained.
Since trust has a temporal correlation, in the case of a network-of-things edge computing architecture based on a feedback-based trust computing mechanism, in order to avoid malicious evaluation caused by malicious behaviors such as false feedback, malicious attack, collusion cheating and the like, in the embodiment, a time window method is used for obtaining a trust prediction value in the current period according to a trust value of equipment in the past time period, the difference value between the obtained actual trust value and the trust prediction value is compared with a set error threshold value, whether the trust value is credible in the interaction process is judged, the detection judgment on the trust value of the edge equipment is realized, if the error is larger, the equipment is proved to be incredible, the trust value of the untrusted device is corrected by a punishment system so as to cope with malicious behaviors such as false feedback, malicious attack, collusion cheating and the like, and the safety and the reliability of the system are improved.
Due to the time correlation of trust, a plurality of historical trust values in a time period closest to the current period can be taken for predicting the trust value, and the specific process of predicting the trust value according to the obtained historical trust value is not limited, wherein preferably, the trust value of the second equipment to the first equipment in a preset time period span can be screened out to obtain the historical trust value; and calculating the average value of the historical trust values, and taking the calculated average value as a prediction trust value. The average value can reflect the current conventional trust value condition of the equipment, if the difference between a certain trust value and the average value is larger, the possibility of abnormity is higher, the average value can be used as a judgment standard, and the calculation process of the average value is simple and quick. Of course, other calculation methods may be used to calculate the predicted value, for example, the historical trust values with gradually increasing weights are multiplied by the historical trust values with gradually approaching intervals, the total weight is 1, and the specific calculation process of the predicted trust values is not limited here and may be set by itself.
The average value is taken as an example for description, and a schematic diagram of the trust value prediction is shown in fig. 3.
Setting the time period span as n (the value of n is generally the nearest few times to the current, for example, n can be 3), and predicting the trust value at t
And then, extracting the trust value at the t-1, t-2
Computing
Step s124, the edge proxy server calculates the difference between the received actual trust value and the corresponding predicted trust value.
And step s125, the edge proxy server compares the difference value with the error threshold value, and divides the direct trust value into an abnormal trust value and a normal trust value according to the comparison result.
Comparing the difference value between the actual trust value and the predicted trust value with a set error threshold value to obtain a reliable trust value, wherein the reliability of the whole trust value can be improved; and if the certain difference is not larger than the error threshold, judging that the direct trust value corresponding to the difference is a normal trust value.
The abnormal trust value indicates that the access between the trust value and the trust value recorded in the history is large, and possibly malicious behaviors such as false feedback, malicious attack, collusion cheating and the like can be avoided in the transmission process, and the feedback trust value needs to be calculated by using the corrected trust value. The normal trust value indicates that the trust value is almost different from the trust value in the history extreme force, the trust is judged, and the trust value can be directly used for calculating the feedback trust value.
The specific setting of the error threshold is not limited, the error threshold may be set to a fixed value after statistics according to interaction conditions of multiple devices, or may be set to a corresponding floating value according to a change condition of a trust value of a fitting device, a uniform error threshold may be set for different devices, or corresponding error thresholds may be set for different devices (for example, setting of the error threshold is performed by counting the change condition of the trust value within a period of time), and the like, and may be automatically set according to requirements.
Wherein, preferably, when
Presentation device diAnd djWhen n is the time period span, the error threshold value can be calculated according to a threshold value calculation formula to obtain an error threshold value;
the threshold calculation formula is specifically as follows:
the error threshold determined by the method is more suitable for the change situation of the actual trust value, the false detection situation is avoided as much as possible, and the extreme accuracy of the overall trust value is improved.
And step s126, the edge proxy server corrects the abnormal trust value through a punishment mechanism to obtain a corrected trust value.
The actual trust value received currently
With corresponding trust predictors
And comparing the difference value with a set error threshold epsilon, judging whether the trust value is credible, and if the trust value is the trust value of the equipment which cannot be credible, correcting the trust value by a punishment system and then performing feedback trust aggregation calculation.
The method for correcting the abnormal trust value is not limited, and may be set to reduce a preset value from the obtained trust value (for example, once the abnormal trust value is detected, the trust value is reduced by 10), or may be set to correct the device according to the previous degree of trust of the device and the difference between the actual trust value and the predicted value (for example, set the difference between the actual trust value and the predicted value of the device/2)
Wherein, preferably, when
For devices d during Δ t timeiTo djThe total number of negative scores for the ability to complete the requested task,
presentation device diAnd djWhen the direct trust value is within the time delta t, the abnormal trust value can be corrected according to a correction formula to obtain a corrected trust value;
wherein, the correction formula is specifically as follows:
wherein the beta is a correction factor, and the correction factor is,
through tests, the abnormal degree of the trust value is considered through the correction mode, correction is carried out according to the trust degree of the previous equipment, and if the equipment is fake in the previous interaction, the trust value is reduced to a greater extent so as to avoid the selection of distrusted equipment.
And step s127, the edge proxy server aggregates the corrected trust value with the normal trust value to obtain a feedback trust value.
The feedback trust value aggregation mode can refer to the existing aggregation mode, wherein preferably, the feedback trust can be obtained through information entropy theory aggregation calculation, and the importance degree of each trust value can be fully considered through the information entropy theory aggregation mode, so that the high-efficiency statistics of the equipment trust condition is realized.
For example, edge proxy server b1，b1Device d3，d4And device d2Direct trust value generated by interaction
And
and directly obtaining feedback trust through information entropy theory aggregation calculation. Feedback trust
The calculation of (c) can be referred to the following formula:
wherein WiAs a weighting factor, EiIs the information entropy of the trust value.
And step s128, the edge proxy server feeds back the trust value to the request initiator.
And step s112, the request initiator performs fusion calculation on the feedback trust value and a direct trust value obtained by the request initiator by evaluating the first device to obtain an overall trust value of the first device.
The actual trust value is fused with the direct trust value and the feedback trust value, so that the corresponding condition of the task of the equipment can be comprehensively evaluated, the fusion process of the trust values is not limited, preferably, certain weights can be respectively distributed to the direct trust value and the feedback trust value, and the trust values are multiplied by the corresponding weights to obtain the integral trust. The specific distribution of the weight is not limited, and the weight can be set according to the previous trusted degree of each device, for example, if the task completion capability of the device is high, a large weight is set, and if the task completion capability of the device is low, a small weight is set; in addition, the direct trust value reflects the trust condition of the interaction process between the trust value requesting party and the trust value requested party, and the weight of the direct trust value can be set to be slightly larger than the feedback trust value. Preferably, the weight setting by the former can more reasonably reflect the trust degrees of different devices, and a more accurate fusion trust value can be obtained when the weight is determined according to the trust degrees of different devices
The determination method of the weight factor specifically comprises the following steps: when in use
For devices d during Δ t timeiTo djWhen the total number of the negative scores of the task capacity of the request is finished, calculating according to a factor calculation formula, and taking the obtained data as direct weight; calculating the difference between 1 and the direct weight, and taking the obtained result as the feedback weight; wherein, the factor calculation formula is specifically as follows:
here with the apparatus d1Will trust directly
And feedback trust
Performing fusion calculation to obtain d2Global trust of
For example. The overall trust value may be calculated as:
wherein omega is a weight factor, wherein,
for devices d during Δ t timeiTo djTotal number of negative scores for ability to complete requested tasks. The above data fusion method is only described as an example, and other data fusion methods can be referred to above.
Through tests, under the condition of using the same system architecture, the method provided by the embodiment is applied to predict the trust value and set the corresponding error threshold value, so that the accuracy of the whole trust can be improved, and the service cooperation between the edge devices of the Internet of things is more reliable.
Based on the above description, the IoT edge device trust evaluation method provided in this embodiment obtains the predicted trust value in the current period according to the trust value of the device in the past time period by using a time window method under the internet of things edge calculation architecture based on the feedback trust calculation mechanism, since trust has time correlation, the predicted trust value defines the possible range of the current trust value to a certain extent, compares the difference between the currently received actual trust value and the predicted trust value with a set error threshold, determines whether the trust value interacted in information interaction is trusted, if the trust value is the trust value of an untrusted device, corrects the trust value by implementing a penalty system to calibrate an untrusted device, performs feedback trust aggregation calculation after correction, can obtain a more reliable overall trust value, and avoids false feedback, And the reliability of the integral trust and the safety of the system can be improved by malicious evaluation caused by malicious behaviors such as malicious attack, collusion cheating and the like. In addition, the IoT edge device trust evaluation method provided by the embodiment can also be applied to trust calculation in a recommendation system, so that accurate recommendation of devices is realized, and the working efficiency is improved.
To further the understanding of the IoT edge device trust evaluation method provided by the present invention, assume that the edge device includes d1、d2、d3、d4、d5Wherein, the device d1Want to obtain device d2Overall trust value of d3And d4Current and d2In an interactive state, d5Current and d2There is no interaction, the whole process is described in this embodiment by taking the above case as an example, and the edge device d1、d2、d3、d4An edge device interaction diagram is shown in fig. 4, and the trust evaluation process mainly includes the following steps:
device d1Want to obtain device d2Sends a request to its edge proxy server.
After the edge proxy server receives the request, the edge proxy server screens the equipment information to discover d3、d4Is in existence with device d2The interaction state of (2).
Device d1And d3、d4According to and d2The direct trust values generated by the interaction conditions are respectively calculated
Device d1、d3、d4The trust value is fed back to the edge proxy server, and the actual trust value received by the edge proxy server is the device d1、d3、d4To d2Direct trust value of
Edge proxy server according to device d3、d4To d2Historical trust fed back
Where n is the span of a time period, each respective device d is calculated2Predicted trust value of
Actual trust value to be received
Respectively calculating respective predicted trust values
Is compared with a set error threshold
Comparing the received actual message with the received actual messageAnd if the arbitrary value is the credible trust value, correcting the trust value of the equipment if the arbitrary value is not in the error threshold range. The trusting value correction rule is as follows:
corrected trust value:
wherein the beta is a correction factor, and the correction factor is,
The edge proxy server will correct the trust value
Wherein i ═ 3,4]I ∈ Z. Performing aggregation calculation to obtain feedback trust
And sent to device d1And the feedback trust is mainly the device d3、d4To d2The trust value of (c).
Device d1Will receive feedback trust from the edge proxy server
With respect to itself by device d2Direct trust value of
Performing fusion calculation to obtain a device d2Overall trust value of
With the increasing frequency of network attacks in the environment of the edge device of the internet of things, the edge device may be attacked by malicious devices, and the trust value may change. The untrusted device provides a false trust value to the edge layer proxy during service collaboration. If these trust values from the device layer are not judged at the proxy layer, an unreliable overall trust is finally obtained. But a reliable trust value is obtained by comparing the difference between the actual trust value and the predicted trust value with a set error threshold. And finally, combining the overall trust of the directly trusted computing device.
According to the method, a time window method is adopted to predict the trust value according to historical interaction records in a past time period, an edge proxy server of a proxy layer compares the actually obtained trust value with the predicted trust value, whether the trust value is credible or not is judged by calculating whether the absolute value of the difference value of the two trust values is larger than a set error threshold value, and if the trust value is judged to be untrustworthy equipment, the trust value is corrected through a punishment system. Thus, a more reliable overall trust value is calculated.
Referring to fig. 5, fig. 5 is a block diagram of an IoT edge device trust evaluation apparatus according to the present embodiment; the method can comprise the following steps: an edge device screening unit 510, a calculation instruction sending unit 520, a trust prediction unit 530, a difference calculation unit 540, a difference comparison unit 550, an exception correction unit 560, a feedback trust aggregation unit 570, and a feedback trust sending unit 580. The IoT edge device trust evaluation apparatus provided in this embodiment may be contrasted with the IoT edge device trust evaluation method described above.
The edge device screening unit 510 is mainly configured to, when receiving a trust value request for a first device, screen an edge device that is currently interacting with the first device to obtain a second device;
the calculation instruction sending unit 520 is mainly configured to send a first device trust value calculation instruction to the second device;
the trust prediction unit 530 is mainly configured to predict a trust value of the second device to the first device according to the stored historical trust value, so as to obtain a predicted trust value;
the difference calculating unit 540 is mainly configured to calculate a difference between the received actual trust value and the corresponding predicted trust value;
the difference comparison unit 550 is mainly configured to compare the difference with an error threshold, and divide the direct trust value into an abnormal trust value and a normal trust value according to a comparison result;
the abnormal correction unit 560 is mainly used for correcting the abnormal trust value through a punishment mechanism to obtain a corrected trust value;
the feedback trust aggregating unit 570 is mainly configured to aggregate the corrected trust value with the normal trust value to obtain a feedback trust value;
the feedback trust sending unit 580 is mainly configured to feed back the feedback trust value to the request initiator, so that the request initiator performs fusion calculation on the feedback trust value and a direct trust value obtained by evaluating the first device by itself, so as to obtain an overall trust value of the first device.
The IoT edge device trust evaluation apparatus provided in this embodiment may detect and determine the edge device trust value, and modify the trust value from the untrusted device through a punishment system, so as to cope with malicious behaviors such as false feedback, malicious attack, and collusion cheating, thereby improving the accuracy of the overall trust and the security and reliability of the system.
Preferably, the trusted prediction unit in this embodiment may specifically include:
the trust value screening subunit is used for screening out the trust value of the second device to the first device within a preset time period span to obtain a historical trust value;
and the average value operator unit is used for calculating the average value of the historical trust value and taking the calculated average value as the prediction trust value.
Preferably, the error threshold calculation unit in the IoT edge device trust evaluation apparatus may be specifically configured to: when in use
Presentation device diAnd djWhen the direct trust value in the time of delta t between the two is n, the direct trust value is counted according to a threshold value when n is the time cycle spanCalculating an error threshold value by using a calculation formula to obtain the error threshold value; the threshold calculation formula is specifically as follows:
preferably, the abnormality correction unit may be specifically configured to: when in use
For devices d during Δ t timeiTo djThe total number of negative scores for the ability to complete the requested task,
presentation device diAnd djWhen the direct trust value is within the time delta t, correcting the abnormal trust value according to a correction formula to obtain a corrected trust value; wherein, the correction formula is specifically as follows:
wherein the beta is a correction factor, and the correction factor is,
the present embodiment provides an edge proxy server, including: a memory and a processor.
Wherein, the memory is used for storing programs;
the processor, when executing the program, implements the steps of the IoT edge device trust evaluation method described above, and may specifically refer to the description of the IoT edge device trust evaluation method in the above embodiments.
Referring to fig. 6, a schematic structural diagram of an edge proxy server provided in this embodiment is shown, where the edge proxy server may generate a large difference due to different configurations or performances, and may include one or more processors (CPUs) 322 (e.g., one or more processors) and a memory 332, and one or more storage media 330 (e.g., one or more mass storage devices) storing applications 342 or data 344. Memory 332 and storage media 330 may be, among other things, transient storage or persistent storage. The program stored on the storage medium 330 may include one or more modules (not shown), each of which may include a series of instructions operating on a data processing device. Still further, the central processor 322 may be configured to communicate with the storage medium 330 to execute a series of instruction operations in the storage medium 330 on the edge proxy server 301.
The edge proxy 301 may also include one or more power supplies 326, one or more wired or wireless network interfaces 350, one or more input-output interfaces 358, and/or one or more operating systems 341, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, and the like.
The steps in the IoT edge device trust evaluation method described above in fig. 1 may be implemented by the structure of an edge proxy server.
Referring to fig. 7, fig. 7 is a block diagram of an IoT edge device trust evaluation system according to the present embodiment; the system mainly comprises: a request originator 710, an edge proxy server 720, and a second device 730.
The request initiator 710 and the second device 730 are both edge devices under the control of the edge proxy server 720, and the device types are not limited, and may be, for example, a mobile phone, a computer, and the like.
The request initiator 710 is mainly configured to send a trust value request for the first device to the edge proxy server; calculating a trust value of the first equipment according to the interaction condition of the first equipment and the first equipment to obtain a direct trust value of the first equipment; and when the feedback trust value is received, performing fusion calculation on the feedback trust value and the direct trust value to obtain an overall trust value of the first device.
Any edge device capable of realizing the above functions can be used as a request initiator, and can perform trust judgment on the edge device to be coordinated to judge whether the edge device is trusted or not.
The module for performing fusion computation in the request initiator 710 is specifically configured to: acquiring preset weight factors corresponding to direct trust and feedback trust to obtain direct weight and feedback weight; and summing the product value of the direct trust value and the direct weight and the product value of the feedback trust value and the feedback weight to obtain an integral trust value.
Wherein the weighting factor may be determined by a weighting factor determination module, in particular the weighting factor determination module is configured to: when in use
For devices d during Δ t timeiTo djWhen the total number of the negative scores of the task capacity of the request is finished, calculating according to a factor calculation formula, and taking the obtained data as direct weight; calculating the difference between 1 and the direct weight, and taking the obtained result as the feedback weight; wherein, the factor calculation formula is specifically as follows:
the edge proxy server 720 is mainly used for screening out the edge devices currently interacting with the first device to obtain a second device when receiving a trust value request for the first device; sending a first equipment trust value calculation instruction to second equipment; predicting the trust value of the second equipment to the first equipment according to the stored historical trust value to obtain a predicted trust value; calculating the difference value between the received actual trust value and the corresponding predicted trust value; comparing the difference value with an error threshold value, and dividing the direct trust value into an abnormal trust value and a normal trust value according to a comparison result; correcting the abnormal trust value through a punishment mechanism to obtain a corrected trust value; aggregating the corrected trust value with a normal trust value to obtain a feedback trust value; feeding back the feedback trust value to the request initiator;
the second device 730 is mainly used for calculating the trust value of the first device according to the interaction condition with the first device when receiving the trust value calculation instruction of the first device, so as to obtain an actual trust value; the actual trust value is returned to the edge proxy server.
The calculation modules of the direct trust value in the second device 730 and the request initiator 710 may specifically be configured to: acquiring the total number of positive scores and the total number of negative scores of the capability of the opposite side to finish the requested task in the interaction process of the two devices within the preset time; wherein, the positive scoring and the negative scoring are obtained by scoring the service quality of the opposite side by the two interactive sides; and counting the proportion of the total number of the positive scores to the total number of the positive scores and the negative scores to obtain a direct trust value.
Specifically, the process of information interaction among the three devices in the system may refer to the specific implementation manner corresponding to fig. 2, and is not described herein again.
The IoT edge device trust evaluation system provided by this embodiment can detect and determine the edge device trust value, and modify the trust value from the untrusted device through a punishment system, so as to cope with malicious behaviors such as false feedback, malicious attack, and collusion cheating, thereby improving the accuracy of the overall trust and the security and reliability of the system.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The IoT edge device trust evaluation method, apparatus, system and edge proxy server provided by the present invention are described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (9)
1. An IoT edge device trust evaluation method, comprising:
the method comprises the steps that when an edge proxy server receives a trust value request of first equipment, edge equipment which is interacted with the first equipment at present is screened out, and second equipment is obtained;
sending a first device trust value calculation instruction to the second device;
predicting the trust value of the second equipment to the first equipment according to the stored historical trust value to obtain a predicted trust value;
calculating the difference value between the received actual trust value and the corresponding predicted trust value;
comparing the difference value with an error threshold value, and dividing a direct trust value into an abnormal trust value and a normal trust value according to a comparison result;
correcting the abnormal trust value through a punishment mechanism to obtain a corrected trust value;
aggregating the corrected trust value and the normal trust value to obtain a feedback trust value;
feeding back the feedback trust value to a request initiator so that the request initiator performs fusion calculation on the feedback trust value and a direct trust value obtained by the request initiator by evaluating the first equipment to obtain an overall trust value of the first equipment;
the fusion calculation of the feedback trust value and the direct trust value obtained by the request initiator evaluating the first device by the request initiator comprises the following steps:
acquiring preset weight factors corresponding to direct trust and feedback trust to obtain direct weight and feedback weight;
and summing the product value of the direct trust value and the direct weight and the product value of the feedback trust value and the feedback weight to obtain an integral trust value.
2. The IoT edge device trust evaluation method recited in claim 1, wherein predicting the trust value of the second device for the first device from the stored historical trust values comprises:
screening out a trust value of the second equipment to the first equipment within a preset time period span to obtain a historical trust value;
and calculating the average value of the historical trust values, and taking the calculated average value as a prediction trust value.
3. The IoT edge device trust evaluation method recited in claim 1, wherein the method of calculating the direct trust value comprises:
acquiring the total number of positive scores and the total number of negative scores of the capability of the opposite side to finish the requested task in the interaction process of the two devices within the preset time; wherein the positive score and the negative score are obtained by scoring the service quality of the opposite party by the two interactive parties;
and counting the proportion of the total number of the positive scores to the total number of the positive scores and the negative scores to obtain a direct trust value.
4. The IoT edge device trust evaluation method in claim 1, wherein the method of calculating the error threshold comprises:
when in use
Presentation device diAnd djWhen n is the time cycle span, calculating an error threshold according to a threshold calculation formula to obtain an error threshold;
the threshold calculation formula specifically includes:
5. the IoT edge device trust evaluation method recited in claim 1, wherein the method of determining the weight factor comprises:
when in use
For devices d during Δ t timeiTo djWhen the total number of the negative scores of the task capacity of the request is finished, calculating according to a factor calculation formula, and taking the obtained data as direct weight;
calculating the difference between 1 and the direct weight, and taking the obtained result as the feedback weight;
wherein, the factor calculation formula specifically comprises:
6. the IoT edge device trust evaluation method of claim 1, wherein correcting the anomalous trust value with a penalty mechanism comprises:
when in use
For devices d during Δ t timeiTo djThe total number of negative scores for the ability to complete the requested task,
presentation device diAnd djWhen the direct trust value is within the time delta t, correcting the abnormal trust value according to a correction formula to obtain a corrected trust value;
wherein, the correction formula is specifically as follows:
wherein the beta is a correction factor, and the correction factor is,
7. an IoT edge device trust evaluation apparatus, comprising:
the edge device screening unit is used for screening the edge device which is interacted with the first device at present to obtain a second device when receiving a trust value request to the first device;
a calculation instruction sending unit, configured to send a first device trust value calculation instruction to the second device;
the trust prediction unit is used for predicting the trust value of the second equipment to the first equipment according to the stored historical trust value to obtain a predicted trust value;
a difference value calculating unit for calculating the difference value between the received actual trust value and the corresponding predicted trust value;
the difference comparison unit is used for comparing the difference with an error threshold value and dividing the direct trust value into an abnormal trust value and a normal trust value according to a comparison result;
the abnormal correction unit is used for correcting the abnormal trust value through a punishment mechanism to obtain a corrected trust value;
the feedback trust aggregation unit is used for aggregating the corrected trust value and the normal trust value to obtain a feedback trust value;
and the feedback trust sending unit is used for feeding back the feedback trust value to the request initiator so that the request initiator performs fusion calculation on the feedback trust value and a direct trust value obtained by evaluating the first equipment by the request initiator to obtain an overall trust value of the first equipment.
8. An edge proxy server, comprising:
a memory for storing a program;
a processor configured to implement the steps of the IoT edge device trust evaluation method in accordance with any of claims 1-6 when executing the program.
9. An IoT edge device trust evaluation system, comprising:
a request initiator, configured to send a trust value request for a first device to an edge proxy server; calculating a trust value of the first equipment according to the interaction condition of the first equipment and the first equipment to obtain a direct trust value of the first equipment; when a feedback trust value is received, performing fusion calculation on the feedback trust value and the direct trust value to obtain an overall trust value of the first device;
the edge proxy server is used for screening out the edge equipment which is interacted with the first equipment at present to obtain second equipment when receiving a trust value request to the first equipment; sending a first device trust value calculation instruction to the second device; predicting the trust value of the second equipment to the first equipment according to the stored historical trust value to obtain a predicted trust value; calculating the difference value between the received actual trust value and the corresponding predicted trust value; comparing the difference value with an error threshold value, and dividing the direct trust value into an abnormal trust value and a normal trust value according to a comparison result; correcting the abnormal trust value through a punishment mechanism to obtain a corrected trust value; aggregating the corrected trust value and the normal trust value to obtain a feedback trust value; feeding back the feedback trust value to a request initiator;
the second device is used for calculating the trust value of the first device according to the interaction condition with the first device when receiving the trust value calculation instruction of the first device, so as to obtain an actual trust value; returning the actual trust value to the edge proxy server.
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| CN111641637B (en) * | 2020-05-28 | 2021-05-11 | 重庆邮电大学 | Edge node calculation result credibility judgment method based on trust evaluation |
| CN113301134B (en) * | 2021-05-14 | 2022-01-07 | 山东大学 | Error-tolerant cooperative decision method suitable for edge Internet of things agent device |
| CN113487218A (en) * | 2021-07-21 | 2021-10-08 | 国网浙江省电力有限公司电力科学研究院 | Internet of things trust evaluation method |
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