CN113239407B - Block chain decision point selection method and device, electronic equipment and storage medium - Google Patents

Block chain decision point selection method and device, electronic equipment and storage medium Download PDF

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CN113239407B
CN113239407B CN202110396993.9A CN202110396993A CN113239407B CN 113239407 B CN113239407 B CN 113239407B CN 202110396993 A CN202110396993 A CN 202110396993A CN 113239407 B CN113239407 B CN 113239407B
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徐瑨
陈希
陶小峰
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Beijing University of Posts and Telecommunications
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Abstract

The embodiment of the invention provides a method and a device for selecting a block chain decision point, electronic equipment and a storage medium, which are applied to the technical field of information, and are used for obtaining multiple prediction violation strategies of a target block chain and violation probabilities corresponding to the violation strategies through a preset security evaluation model; counting the number of nodes corresponding to the prediction violation strategies with violation probability larger than a preset safety threshold to obtain the prediction number of violation nodes; calculating a first predicted number of decision nodes through a first preset formula according to a preset safety factor and the predicted number of violation nodes; according to the preset safety threshold, calculating the minimum value of the number of decision nodes meeting the preset safety threshold through a second preset formula to obtain a second predicted number of the decision nodes; and acquiring the target number of the decision nodes, and selecting the decision nodes with the target number from the non-violation nodes. The threat of illegal operation maliciously executed by partial nodes in the block chain to the network security can be prevented, and the network security in the block chain is improved.

Description

Block chain decision point selection method and device, electronic equipment and storage medium
Technical Field
The present invention relates to the field of information technologies, and in particular, to a method and an apparatus for selecting a block chain decision point, an electronic device, and a storage medium.
Background
At present, a blockchain is used as a technical tool for decentralization and privacy protection, system autonomy can be realized by utilizing a consensus mechanism and privacy protection capability of the blockchain, the problems of reliability, safety, privacy, trust and the like in a network are solved, and network safety and service efficiency are improved.
However, when the block chain consensus mechanism is executed, part of the nodes may maliciously execute illegal operations, and voting selection is changed, so that the final consensus result is influenced, and the security of the network is threatened.
Disclosure of Invention
Embodiments of the present invention provide a method and an apparatus for selecting a block chain decision point, an electronic device, and a storage medium, so as to solve the problem that some nodes in a block chain execute malicious illegal operations and threaten network security. The specific technical scheme is as follows:
in a first aspect of this embodiment, a method for selecting a block chain decision point is provided, where the method includes:
calculating multiple prediction violation strategies of a target block chain and violation probabilities corresponding to the prediction violation strategies through a preset security evaluation model, wherein the prediction violation strategies are that violation operations are executed through nodes corresponding to the prediction violation strategies;
counting the number of nodes corresponding to the prediction violation strategies with the violation probability being greater than a preset safety threshold to obtain the prediction number of violation nodes;
calculating a first predicted number of decision nodes through a first preset formula according to a preset safety factor and the predicted number of the violation nodes;
according to a preset safety threshold, calculating the minimum value of the number of decision nodes meeting the preset safety threshold through a second preset formula to obtain a second predicted number of the decision nodes;
when the smaller value of the first prediction quantity and the second prediction quantity is smaller than the quantity of the participating nodes, taking the smaller value of the first prediction quantity and the second prediction quantity as the target quantity of decision nodes; when the first prediction number is larger than or equal to the number of the participating nodes and the second prediction number is larger than or equal to the number of the participating nodes, taking the number of the participating nodes as a target number of decision nodes;
and selecting a target number of decision nodes from non-violation nodes according to the target number of the decision nodes, wherein the non-violation nodes are nodes except the violation nodes in the participating nodes.
Optionally, after selecting a target number of decision nodes from non-violation nodes according to the target number of decision nodes, the method further includes:
performing, by the decision node, a consensus operation in the target block chain.
Optionally, the preset safety threshold is a threshold preset according to the violation difficulty and violation cost of the participating node.
Optionally, the calculating, according to a preset safety factor and the predicted number of the violation nodes, a first predicted number of the decision nodes by using a first preset formula includes:
and calculating the ratio of the predicted number of the violation nodes to the preset safety factor to obtain a first predicted number of the decision nodes.
Optionally, the calculating, according to a preset safety threshold, a minimum value of the number of decision nodes meeting the preset safety threshold through a second preset formula to obtain a second predicted number of decision nodes includes:
according to a preset safety threshold value, through a second preset formula:
Figure BDA0003018933520000021
calculating the minimum value of the number of decision nodes meeting the preset safety threshold value to obtain a second predicted number of decision nodes, wherein,
Figure BDA0003018933520000022
representing an offending node NSThe set of components is composed of a plurality of groups,
Figure BDA0003018933520000023
representing an offending node NSA set of k nodes in the group,
Figure BDA0003018933520000032
for the violation policy to correspond to the violation probability,
Figure BDA0003018933520000031
the number of the combination of the specific k devices when the number of the illegal nodes is k, paIs composed of
Figure BDA0003018933520000033
Probability of a device being selected as a decision node, pbIs composed of
Figure BDA0003018933520000034
The probability of the selected device as a decision node, N is a safety factor, and N is the number of decision nodes.
Optionally, the training process of the preset security assessment model includes:
inputting sample nodes into a security assessment model to be trained, wherein the sample nodes comprise pre-marked illegal nodes;
calculating and obtaining multiple prediction violation strategies and violation probabilities corresponding to various prediction violation operations through the to-be-trained safety evaluation model, wherein the prediction violation strategies are that the violation operations are executed through prediction violation nodes;
calculating the loss of the to-be-trained safety assessment model according to the pre-marked violation nodes and the predicted violation nodes;
and adjusting parameters of the to-be-trained safety assessment model according to the loss, returning to the step of calculating and obtaining multiple prediction violation strategies and violation probabilities corresponding to various prediction violation operations through the to-be-trained safety assessment model, and continuing to execute until the loss of the safety assessment model is smaller than a preset threshold value, so as to obtain the preset safety assessment model.
In a second aspect of the present application, there is provided an apparatus for selecting a block chain decision point, the apparatus including:
the violation policy obtaining module is used for calculating and obtaining multiple prediction violation policies of a target block chain and violation probabilities corresponding to the prediction violation policies through a preset security evaluation model, wherein the prediction violation policies are that violation operations are executed through nodes corresponding to the prediction violation policies;
the prediction number counting module is used for counting the number of nodes corresponding to the prediction violation strategies with the violation probability being greater than a preset safety threshold value to obtain the prediction number of the violation nodes;
the first prediction number calculation module is used for calculating a first prediction number of decision nodes through a first preset formula according to a preset safety factor and the prediction number of the violation nodes;
the second prediction number calculation module is used for calculating the minimum value of the number of decision nodes meeting the preset safety threshold through a second preset formula according to the preset safety threshold to obtain the second prediction number of the decision nodes;
a target number obtaining module, configured to, when a smaller value of the first predicted number and the second predicted number is smaller than a number of participating nodes, take the smaller value of the first predicted number and the second predicted number as a target number of decision nodes; when the first prediction number is larger than or equal to the number of the participating nodes and the second prediction number is larger than or equal to the number of the participating nodes, taking the number of the participating nodes as a target number of decision nodes;
and the decision node selection module is used for selecting decision nodes with target quantity from non-violation nodes according to the target quantity of the decision nodes, wherein the non-violation nodes are nodes except the violation nodes in the participating nodes.
Optionally, the apparatus further comprises:
and the consensus operation execution module is used for executing the consensus operation in the target block chain through the decision node.
Optionally, the preset safety threshold is a threshold preset according to the violation difficulty and violation cost of the participating node.
Optionally, the first prediction number calculating module is specifically configured to calculate a ratio of the prediction number of the violation node to the preset safety factor, so as to obtain the first prediction number of the decision node.
Optionally, the second prediction number calculating module is specifically configured to, according to a preset safety threshold, according to a second preset formula:
Figure BDA0003018933520000041
calculating the minimum value of the number of decision nodes meeting the preset safety threshold value to obtain a second predicted number of decision nodes, wherein,
Figure BDA0003018933520000042
representing an offending node NSThe set of components is composed of a plurality of groups,
Figure BDA0003018933520000043
indicating an offending node NSA set of k nodes in the group,
Figure BDA0003018933520000052
in order for the violation policy to correspond to a violation probability,
Figure BDA0003018933520000051
the number of the combination of the specific k devices when the number of the illegal nodes is k, paIs composed of
Figure BDA0003018933520000053
Probability of a device being selected as a decision node, pbIs composed of
Figure BDA0003018933520000054
The probability of the selected device as a decision node, N is a safety factor, and N is the number of decision nodes.
Optionally, the training process of the preset security assessment model includes:
inputting sample nodes into a safety assessment model to be trained, wherein the sample nodes comprise pre-marked violation nodes;
calculating and obtaining multiple prediction violation strategies and violation probabilities corresponding to various prediction violation operations through the to-be-trained safety evaluation model, wherein the prediction violation strategies are that the violation operations are executed through prediction violation nodes;
calculating the loss of the to-be-trained safety assessment model according to the pre-marked violation nodes and the predicted violation nodes;
and adjusting parameters of the to-be-trained safety assessment model according to the loss, returning to the step of calculating and obtaining multiple prediction violation strategies and violation probabilities corresponding to various prediction violation operations through the to-be-trained safety assessment model, and continuing to execute until the loss of the safety assessment model is smaller than a preset threshold value, so as to obtain the preset safety assessment model.
In another aspect of this embodiment, an electronic device is further provided, which includes a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete communication with each other through the communication bus;
a memory for storing a computer program;
the processor is used for realizing the selection method of any block chain decision point when executing the program stored in the memory.
In another aspect of the present application, a computer-readable storage medium is further provided, in which a computer program is stored, and when the computer program is executed by a processor, the method for selecting any one of the above-mentioned blockchain decision points is implemented.
In another aspect of the present invention, there is also provided a computer program product containing instructions which, when run on a computer, cause the computer to perform any one of the above methods for blockchain decision point selection.
The embodiment of the invention has the following beneficial effects:
according to the method, the device, the electronic equipment and the storage medium for selecting the block chain decision point, provided by the embodiment of the invention, multiple prediction violation strategies of a target block chain and violation probabilities corresponding to the prediction violation strategies can be calculated and obtained through a preset security evaluation model; counting the number of nodes corresponding to the prediction violation strategies with violation probability larger than a preset safety threshold to obtain the prediction number of violation nodes; calculating a first predicted number of decision nodes through a first preset formula according to a preset safety factor and the predicted number of violation nodes; according to the preset safety threshold, calculating the minimum value of the number of decision nodes meeting the preset safety threshold through a second preset formula to obtain a second predicted number of the decision nodes; when the smaller value of the first prediction quantity and the second prediction quantity is smaller than the quantity of the participating nodes, taking the smaller value of the first prediction quantity and the second prediction quantity as the target quantity of decision nodes; when the first prediction number is larger than or equal to the number of the participating nodes and the second prediction number is larger than or equal to the number of the participating nodes, taking the number of the participating nodes as a target number of decision nodes; and selecting the decision nodes with the target number from the non-violation nodes according to the target number of the decision nodes. Therefore, by the method of the embodiment of the application, part of nodes in non-violation nodes can be selected as decision nodes according to the preset security evaluation model, so that the target block chain can execute consensus operation through the selected decision nodes, the threat of malicious violation operation on network security by the part of nodes in the block chain is prevented, and the network security in the block chain is improved.
Of course, not all of the advantages described above need to be achieved at the same time in the practice of any one product or method of the invention.
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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 some embodiments of the present invention, and it is obvious for those skilled in the art that other embodiments can be obtained by referring to these drawings.
Fig. 1 is a schematic flowchart of a method for selecting a block chain decision point according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of a relationship between a participating node and a decision node according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a block chain based wireless communication system according to an embodiment of the present application;
fig. 4 is a schematic flowchart of a training process of a preset security assessment model according to an embodiment of the present application;
fig. 5 is a diagram illustrating an example of a method for selecting a block chain decision point according to an embodiment of the present disclosure;
FIG. 6 is a block chain decision point selection apparatus according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived from the embodiments given herein by one of ordinary skill in the art, are within the scope of the invention.
In order to solve the problem that some nodes in a blockchain execute illegal operations maliciously and threaten network security, a first aspect of the present application firstly provides a method for selecting a blockchain decision point, where the method includes:
calculating multiple prediction violation strategies of the target block chain and violation probabilities corresponding to the prediction violation strategies through a preset security evaluation model, wherein the prediction violation strategies are that violation operations are executed through nodes corresponding to the prediction violation strategies;
counting the number of nodes corresponding to the prediction violation strategies with violation probability larger than a preset safety threshold to obtain the prediction number of violation nodes;
calculating a first predicted number of decision nodes through a first preset formula according to a preset safety factor and the predicted number of violation nodes;
according to the preset safety threshold, calculating the minimum value of the number of decision nodes meeting the preset safety threshold through a second preset formula to obtain a second predicted number of the decision nodes;
when the smaller value of the first prediction quantity and the second prediction quantity is smaller than the quantity of the participating nodes, taking the smaller value of the first prediction quantity and the second prediction quantity as the target quantity of the decision nodes; when the first prediction number is larger than or equal to the number of the participating nodes and the second prediction number is larger than or equal to the number of the participating nodes, taking the number of the participating nodes as the target number of the decision nodes;
and selecting decision nodes with target quantity from non-violation nodes according to the target quantity of the decision nodes, wherein the non-violation nodes are nodes except the violation nodes in the participating nodes.
Therefore, by the method of the embodiment of the application, part of nodes in non-violation nodes can be selected as decision nodes according to the preset security evaluation model, so that the target block chain can execute consensus operation through the selected decision nodes, the threat of malicious violation operation on network security by the part of nodes in the block chain is prevented, and the network security in the block chain is improved.
Specifically, referring to fig. 1, fig. 1 is a schematic flow chart of a method for selecting a block chain decision point according to an embodiment of the present application, including:
and step S11, calculating and obtaining multiple prediction violation strategies of the target block chain and violation probabilities corresponding to the prediction violation strategies through a preset security evaluation model.
The violation prediction strategy is to execute violation operation through a node corresponding to the violation prediction strategy. The preset security evaluation model may refer to the following embodiments, which are not described herein again.
In the embodiment of the present application, reference may be made to fig. 2 for a relationship among a participating node T, a decision node N, and an illegal node Ns in a blockchain, where the participating node may be all nodes in the blockchain, and the illegal node may perform illegal operation, which may threaten security in the blockchain network.
In the actual use process, multiple prediction violation strategies of the target block chain and violation probabilities corresponding to the prediction violation strategies are calculated and obtained through a preset safety evaluation model, multiple prediction violation strategies can be calculated and obtained according to historical decision nodes and preset safety factors in the target block chain, and the multiple prediction violation strategies are calculated and obtained through a preset formula:
Figure BDA0003018933520000081
and calculating the violation probability corresponding to each predicted violation policy, wherein,
Figure BDA0003018933520000083
representative of offending node NSThe set of components is composed of a plurality of groups,
Figure BDA0003018933520000084
indicating an offending node NSA set of k nodes in the group,
Figure BDA0003018933520000085
for the violation policy to correspond to the violation probability,
Figure BDA0003018933520000082
the number of the combination of the specific k devices when the number of the illegal nodes is k, paIs composed of
Figure BDA0003018933520000091
Probability of a device being selected as a decision node, pbIs composed of
Figure BDA0003018933520000092
The probability of the selected device as a decision node, N is a safety factor, and N is the number of decision nodes.
When multiple prediction violation strategies of the target block chain are calculated and obtained through a preset safety evaluation model, the method can be implemented through a preset formula: w is a group ofiB＝Ci-γi-QiBCalculate the violation revenue for each node, where QiBIndicating the penalty, gamma, incurred by the system in detecting an illegal operationiDifficulty of violation indicating a node in violation, CiDenoted as the payment of the offending node i to perform the offending operation, when WiBIf the number is larger than 0, the node is an illegal node, and if the number is smaller than 0, the node is a non-illegal node.
The method for selecting the block chain decision point according to the embodiment of the present application is applied to an intelligent terminal, and referring to fig. 3, the method can be executed by the intelligent terminal, where the intelligent terminal may be a node in a target block chain, and specifically, the intelligent terminal may be a mobile phone, a computer, a server, or the like.
Step S12, counting the number of nodes corresponding to the prediction violation strategies with violation probability greater than the preset safety threshold to obtain the prediction number of violation nodes.
Optionally, the preset safety threshold is a threshold preset according to violation difficulty and violation cost of the participating nodes.
And step S13, calculating a first predicted number of decision nodes through a first preset formula according to a preset safety factor and the predicted number of illegal nodes.
Optionally, calculating a first predicted number of decision nodes according to a preset security factor and the predicted number of violation nodes, where the calculating includes: and calculating the ratio of the predicted number of the violation nodes to a preset safety factor through a first preset formula to obtain the first predicted number of the decision nodes.
The first preset formula may be: the first predicted number is the predicted number of offending nodes/a preset safety factor.
And when the safety factor N is used for indicating that N x N illegal nodes exist in the decision nodes, the illegal operation can be realized.
And step S14, according to the preset safety threshold, calculating the minimum value of the number of the decision nodes meeting the preset safety threshold through a second preset formula to obtain a second predicted number of the decision nodes.
Optionally, according to the preset safety threshold, calculating a minimum value of the number of decision nodes meeting the preset safety threshold through a second preset formula to obtain a second predicted number of decision nodes, where the second predicted number includes: according to a preset safety threshold value, through a second preset formula:
Figure BDA0003018933520000101
calculating the number of decision nodes meeting a preset safety thresholdA minimum value, resulting in a second predicted number of decision nodes, wherein,
Figure BDA0003018933520000103
representing an offending node NSThe set of components is composed of a plurality of groups,
Figure BDA0003018933520000104
indicating an offending node NSA set of k nodes in the group,
Figure BDA0003018933520000105
in order for the violation policy to correspond to a violation probability,
Figure BDA0003018933520000102
the number of the combination of the specific k devices when the number of the illegal nodes is k, paIs composed of
Figure BDA0003018933520000106
Probability of a device being selected as a decision node, pbIs composed of
Figure BDA0003018933520000107
The probability of the selected device as a decision node, N is a safety factor, and N is the number of decision nodes.
In the actual calculation process, the number of violation nodes can be continuously increased until the corresponding violation probability reaches the preset safety threshold, so that the minimum value of the number of decision nodes meeting the preset safety threshold is obtained, and the second predicted number of the decision nodes is obtained.
Step S15, when the smaller value of the first prediction quantity and the second prediction quantity is smaller than the quantity of the participating nodes, the smaller value of the first prediction quantity and the second prediction quantity is used as the target quantity of the decision nodes; and when the first prediction number is larger than or equal to the number of the participating nodes and the second prediction number is larger than or equal to the number of the participating nodes, taking the number of the participating nodes as the target number of the decision nodes.
In the actual use process, the number N of decision nodes is based on two different N lower limits, namely a systemThe Nmin1 obtained by the calculation and determination of the total safety factor n and the maximum Ns number; second, from all P to NSThe violation strategy starts, the value of each possible Ns is increased in sequence, and when the value of Ns is increased to the corresponding success rate P, the safety bottom line P is metdWhen required, Nmin2 obtained at this time was recorded. Taking the minimum value of Nmin1 and Nmin2 as Nmin, if Nmin is less than T, N is equal to Nmin, otherwise N is equal to T.
And step S16, selecting decision nodes with target quantity from non-violation nodes according to the target quantity of the decision nodes.
And the non-violation nodes are nodes except the violation nodes in the participating nodes. In an actual use process, when the number of non-violation nodes is smaller than the target number, that is, the number of non-violation nodes is not enough to select the decision nodes with the target number, the node with the minimum violation probability of the corresponding violation policy can be selected from the violation nodes as the decision node.
In the actual use process, the establishment process of the decision node is as follows: when N is T, the T participating nodes form decision nodes; when N is less than T-Ns, N equipment which does not belong to the equipment group corresponding to the maximum Ns in T is randomly selected as decision nodes; and when the T-Ns is less than N < T, all the devices which do not belong to the maximum Ns corresponding device group in the T are taken as decision nodes, and the lowest N- (T-Ns) devices which become the decision node probability are selected from the Ns corresponding device set to be supplemented as the decision nodes.
Optionally, after selecting the decision nodes with the target number from the non-violation nodes according to the target number of the decision nodes, the method further includes: and executing the consensus operation in the target block chain through the decision node.
The consensus mechanism in the embodiment of the application can adopt a Byzantine algorithm, one node is randomly selected from decision nodes to serve as a main node, the main node packs transaction entering blocks which pass verification as much as possible, then the packed blocks are forwarded to other decision nodes, the decision nodes attach own digital signatures to the blocks after confirming that the received block contents are legal and effective, the signed blocks are forwarded to the main node, and the main node summarizes the signatures and broadcasts the finally generated blocks after confirming that the signature number meets the requirements of safety factors.
Therefore, by the method of the embodiment of the application, part of nodes in non-violation nodes can be selected as decision nodes according to the preset security evaluation model, so that the target block chain can execute consensus operation through the selected decision nodes, the threat of malicious violation operation on network security by the part of nodes in the block chain is prevented, and the network security in the block chain is improved.
Optionally, referring to fig. 4, the training process of the preset security assessment model includes:
step S41, inputting sample nodes into a safety assessment model to be trained, wherein the sample nodes comprise pre-marked violation nodes;
step S42, calculating and obtaining multiple prediction violation strategies and violation probabilities corresponding to various prediction violation operations through a to-be-trained safety evaluation model, wherein the prediction violation strategies are that the violation operations are executed through prediction violation nodes;
step S43, calculating the loss of the safety assessment model to be trained according to the pre-marked violation nodes and the predicted violation nodes;
and step S44, adjusting parameters of the security assessment model to be trained according to the loss, returning to the step of obtaining multiple prediction violation strategies and violation probabilities corresponding to various prediction violation operations through calculation of the security assessment model to be trained, and continuing to execute until the loss of the security assessment model is smaller than a preset threshold value, so as to obtain a preset security assessment model.
Therefore, by the method of the embodiment of the application, the network model can be trained to obtain the preset safety assessment model, and therefore the possibility that the node becomes the violation node can be judged according to the preset safety assessment model.
Referring to fig. 5, fig. 5 is a diagram illustrating an example of a method for selecting a block chain decision point according to an embodiment of the present application, including:
1. determining the number T of participating nodes and a security factor n;
2. determining a security assessment model and a corresponding revenue function;
3. predicting the possibility of all violations from the point of view of the violation nodes;
4. calculating the violation nodes with the profit larger than 0 under the current prejudgment according to a preset formula;
5. selecting the undetermined number of decision nodes according to a safety threshold;
6. when the undetermined number of the decision nodes is larger than the number of the participating nodes, all the participating nodes are used as the decision nodes, and a voting consensus process is executed;
7. when the target number of the decision nodes is smaller than the number of the participating nodes, taking the undetermined number of the decision nodes as the target number of the decision nodes;
8. when the target number of the decision nodes is smaller than the number of non-violation nodes of the participating nodes, randomly selecting the nodes with the target number from the non-violation nodes of the participating nodes as the decision nodes, and executing a voting consensus process;
9. and when the target number of the decision nodes is not less than the number of non-violation nodes of the participating nodes, selecting all the non-violation nodes as the decision nodes, selecting the node with the lowest violation probability from the violation nodes as a supplement to obtain the decision nodes with the target number, and then executing the voting consensus process.
In order to illustrate the beneficial effects of the embodiments of the present application, the following is illustrated by specific experimental results:
it is assumed that in a block chain based wireless communication system, authentication of devices in the system is based on the trustworthiness of the devices. The system consensus adopts a consensus process similar to a Byzantine fault-tolerant algorithm with a safety factor of 2/3, wherein 100 devices are selected as system participation nodes, and the weight values of all the devices in the system are equal, so that the violation difficulty of all the devices is the same.
Based on which the system can determine relevant parameters in the security assessment model. The illegal profit W is calculated based on the device reliability in the system, and W is taken as 0.45. Since the violation difficulty and the violation cost of the violation equipment are the same, CiWhen the rule is equal to 0.0075, the violation probability P and the violation income W are equaliBIs calculated as follows:
Figure BDA0003018933520000131
WiB≈Ci，
where P is the violation probability, CiDenoted as the payment for the offending node i to perform the offending operation,
Figure BDA0003018933520000132
to select the number of combinations of decision nodes from the non-violating nodes,
Figure BDA0003018933520000133
the number of combinations of decision nodes is selected from among the offending nodes.
The number of decision nodes of the last round of consensus is 80, so that the range of all violation nodes satisfying the violation policy is [71,73 ]](ii) a Nmin1 obtained at this time (73 × 3/2 ═ 110)>100, calculate Nmin 2-74, and then adding one more device does not satisfy PNS·W>C, so that the safety threshold Pd is satisfied, where P can be considered to be equal to about 0. In summary, the value of the decision node N is 74, and since the weights of all devices in the system are the same, 74 devices may be randomly selected from 100 devices as participating nodes. Subsequently, the selected 74 devices are used as decision common identification uplink.
Referring to fig. 6, fig. 6 is a schematic structural diagram of an apparatus for selecting a block chain decision point according to an embodiment of the present application, where the apparatus includes:
the violation policy obtaining module 601 is configured to calculate, through a preset security evaluation model, multiple predicted violation policies of the target block chain and violation probabilities corresponding to the predicted violation policies, where the predicted violation policies are that violation operations are executed through nodes corresponding to the predicted violation policies;
a predicted number counting module 602, configured to count the number of nodes corresponding to the prediction violation policy with the violation probability greater than the preset safety threshold, to obtain a predicted number of violation nodes;
the first predicted quantity calculating module 603 is configured to calculate, according to a preset security factor and the predicted quantity of the violation nodes, a first predicted quantity of the decision nodes by using a first preset formula;
a second predicted number calculating module 604, configured to calculate, according to a preset safety threshold, a minimum value of the number of decision nodes that meet the preset safety threshold by using a second preset formula, to obtain a second predicted number of the decision nodes;
a target number obtaining module 605, configured to, when the smaller of the first predicted number and the second predicted number is smaller than the number of participating nodes, take the smaller of the first predicted number and the second predicted number as a target number of the decision node; when the first prediction number is larger than or equal to the number of the participating nodes and the second prediction number is larger than or equal to the number of the participating nodes, taking the number of the participating nodes as the target number of the decision nodes;
and a decision node selecting module 606, configured to select decision nodes with a target number from non-violation nodes according to the target number of the decision nodes, where the non-violation nodes are nodes, excluding the violation nodes, in the participating nodes.
Optionally, the apparatus further comprises:
and the consensus operation execution module is used for executing the consensus operation in the target block chain through the decision node.
Optionally, the preset safety threshold is a threshold preset according to violation difficulty and violation cost of the participating nodes.
Optionally, the first prediction number calculating module 603 is specifically configured to calculate a ratio of the prediction number of the violation node to a preset security factor, so as to obtain the first prediction number of the decision node.
Optionally, the second predicted quantity calculating module 604 is specifically configured to, according to a preset safety threshold, according to a second preset formula:
Figure BDA0003018933520000151
calculating the minimum value of the number of decision nodes meeting the preset safety threshold value to obtain a second predicted number of decision nodes, wherein,
Figure BDA0003018933520000153
representing an offending node NSThe set of components is composed of a plurality of groups,
Figure BDA0003018933520000154
indicating an offending node NSA set of k nodes in the group,
Figure BDA0003018933520000155
in order for the violation policy to correspond to a violation probability,
Figure BDA0003018933520000152
the number of the combination of the specific k devices when the number of the illegal nodes is k, paIs composed of
Figure BDA0003018933520000156
Probability of a device being selected as a decision node, pbIs composed of
Figure BDA0003018933520000157
The probability of the selected device as a decision node, N is a safety factor, and N is the number of decision nodes.
Optionally, the training process of the preset security assessment model includes:
inputting sample nodes into a safety assessment model to be trained, wherein the sample nodes comprise pre-marked violation nodes;
calculating and obtaining multiple prediction violation strategies and violation probabilities corresponding to various prediction violation operations through a to-be-trained safety evaluation model, wherein the prediction violation strategies are that the violation operations are executed through prediction violation nodes;
calculating the loss of the safety evaluation model to be trained according to the pre-marked violation nodes and the predicted violation nodes;
and adjusting parameters of the safety assessment model to be trained according to the loss, returning to the step of obtaining various prediction violation strategies and violation probabilities corresponding to various prediction violation operations through calculation of the safety assessment model to be trained, and continuing to execute until the loss of the safety assessment model is smaller than a preset threshold value, so as to obtain a preset safety assessment model.
Therefore, by the device in the embodiment of the application, part of nodes in non-violation nodes can be selected as decision nodes according to the preset security evaluation model, so that the target block chain can execute consensus operation through the selected decision nodes, the threat of malicious violation operation on network security by part of nodes in the block chain is prevented, and the network security in the block chain is improved.
An embodiment of the present invention further provides an electronic device, as shown in fig. 7, including a processor 701, a communication interface 702, a memory 703 and a communication bus 704, where the processor 701, the communication interface 702, and the memory 703 complete mutual communication through the communication bus 704,
a memory 703 for storing a computer program;
the processor 701 is configured to implement the following steps when executing the program stored in the memory 703:
calculating and obtaining multiple prediction violation strategies of the target block chain and violation probabilities corresponding to the prediction violation strategies through a preset security evaluation model, wherein the prediction violation strategies are that violation operations are executed through nodes corresponding to the prediction violation strategies;
counting the number of nodes corresponding to the prediction violation strategies with violation probability larger than a preset safety threshold to obtain the prediction number of violation nodes;
calculating a first predicted number of decision nodes through a first preset formula according to a preset safety factor and the predicted number of violation nodes;
according to the preset safety threshold, calculating the minimum value of the number of decision nodes meeting the preset safety threshold through a second preset formula to obtain a second predicted number of the decision nodes;
when the smaller value of the first prediction quantity and the second prediction quantity is smaller than the quantity of the participating nodes, taking the smaller value of the first prediction quantity and the second prediction quantity as the target quantity of the decision nodes; when the first prediction number is larger than or equal to the number of the participating nodes and the second prediction number is larger than or equal to the number of the participating nodes, taking the number of the participating nodes as the target number of the decision nodes;
and selecting a target number of decision nodes from non-violation nodes according to the target number of the decision nodes, wherein the non-violation nodes are nodes except the violation nodes in the participating nodes.
The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this is not intended to represent only one bus or type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Alternatively, the memory may be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or node other Programmable logic devices, discrete Gate or node transistor logic devices, discrete hardware components.
In yet another embodiment of the present invention, a computer-readable storage medium is further provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any one of the above methods for selecting blockchain decision points.
In yet another embodiment of the present invention, there is also provided a computer program product containing instructions which, when run on a computer, cause the computer to perform the method for selecting any of the above-described blockchain decision points.
In the above embodiments, the implementation may be wholly or partly implemented by software, hardware, firmware or any combination of nodes. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or a node or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted by a node from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium or node that can be accessed by a computer and is a data storage device, such as a server, a data center, etc., that includes an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a nodal semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It should be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or node operation from another entity or operation without necessarily requiring or implying any actual such relationship or order between such entities or operations. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or node apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or such that a node further comprises an element inherent to such process, method, article, or node apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or node apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus, the electronic device, the storage medium, and the computer program product embodiment, since they are substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to part of the description of the method embodiment.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims (10)

1. A method for selecting a block chain decision point, the method comprising:
calculating multiple prediction violation strategies of a target block chain and violation probabilities corresponding to the prediction violation strategies through a preset security evaluation model, wherein the prediction violation strategies are that violation operations are executed through nodes corresponding to the prediction violation strategies;
counting the number of nodes corresponding to the prediction violation strategies with the violation probability being greater than a preset safety threshold to obtain the prediction number of violation nodes;
calculating a first predicted number of decision nodes through a first preset formula according to a preset safety factor and the predicted number of the violation nodes;
according to a preset safety threshold, calculating the minimum value of the number of decision nodes meeting the preset safety threshold through a second preset formula to obtain a second predicted number of the decision nodes;
when the smaller value of the first prediction quantity and the second prediction quantity is smaller than the quantity of the participating nodes, taking the smaller value of the first prediction quantity and the second prediction quantity as the target quantity of decision nodes; when the first prediction number is larger than or equal to the number of the participating nodes and the second prediction number is larger than or equal to the number of the participating nodes, taking the number of the participating nodes as a target number of decision nodes;
selecting decision nodes with a target number from non-violation nodes according to the target number of the decision nodes, wherein the non-violation nodes are nodes except the violation nodes in the participating nodes;
the first preset formula is as follows: the first predicted quantity is equal to the predicted quantity of the illegal nodes/a preset safety factor;
the second preset formula is as follows:
Figure FDA0003638412260000011
wherein the content of the first and second substances,
Figure FDA0003638412260000012
representing an offending node NSThe set of components is composed of a plurality of groups,
Figure FDA0003638412260000013
indicating an offending node NSA set of k nodes in the group,
Figure FDA0003638412260000014
in order for the violation policy to correspond to a violation probability,
Figure FDA0003638412260000015
the number of the combination of the specific k devices when the number of the illegal nodes is k, paIs composed of
Figure FDA0003638412260000021
Probability of a device being selected as a decision node, pbIs composed of
Figure FDA0003638412260000022
The probability of the selected device as a decision node, N is a safety factor, and N is the number of decision nodes.
2. The method of claim 1, wherein after selecting a target number of decision nodes from the non-violating nodes based on the target number of decision nodes, the method further comprises:
performing, by the decision node, a consensus operation in the target block chain.
3. The method according to claim 1, wherein the preset safety threshold is a threshold preset according to violation difficulty and violation cost of the participating node.
4. The method according to claim 1, wherein calculating a first predicted number of decision nodes according to a preset safety factor and the predicted number of violation nodes by a first preset formula comprises:
and calculating the ratio of the predicted number of the violation nodes to the preset safety factor to obtain a first predicted number of the decision nodes.
5. The method according to claim 1, wherein the calculating, according to a preset safety threshold, a minimum value of the number of decision nodes satisfying the preset safety threshold by a second preset formula to obtain a second predicted number of decision nodes comprises:
according to a preset safety threshold value, through a second preset formula:
Figure FDA0003638412260000023
calculating the minimum value of the number of decision nodes meeting the preset safety threshold value to obtainA second number of predictions to the decision node, wherein,
Figure FDA0003638412260000024
representing an offending node NSThe set of components is composed of a plurality of groups,
Figure FDA0003638412260000025
indicating an offending node NSA set of k nodes in the group,
Figure FDA0003638412260000031
for the violation policy to correspond to the violation probability,
Figure FDA0003638412260000032
the number of the combination of the specific k devices when the number of the illegal nodes is k, paIs composed of
Figure FDA0003638412260000033
Probability of a device being selected as a decision node, pbIs composed of
Figure FDA0003638412260000034
The probability of the selected device as a decision node, N is a safety factor, and N is the number of decision nodes.
6. The method of claim 1, wherein the training process of the preset security assessment model comprises:
inputting sample nodes into a safety assessment model to be trained, wherein the sample nodes comprise pre-marked violation nodes;
calculating and obtaining multiple prediction violation strategies and violation probabilities corresponding to various prediction violation operations through the to-be-trained safety evaluation model, wherein the prediction violation strategies are that the violation operations are executed through prediction violation nodes;
calculating the loss of the security assessment model to be trained according to the pre-marked violation nodes and the predicted violation nodes;
and adjusting parameters of the to-be-trained safety assessment model according to the loss, returning to the step of calculating and obtaining multiple prediction violation strategies and violation probabilities corresponding to various prediction violation operations through the to-be-trained safety assessment model, and continuing to execute until the loss of the safety assessment model is smaller than a preset threshold value, so as to obtain the preset safety assessment model.
7. An apparatus for selecting a block chain decision point, the apparatus comprising:
the violation policy acquisition module is used for calculating and obtaining multiple prediction violation policies of a target block chain and violation probabilities corresponding to the prediction violation policies through a preset security evaluation model, wherein the prediction violation policies are that violation operations are executed through nodes corresponding to the prediction violation policies;
the prediction number counting module is used for counting the number of nodes corresponding to the prediction violation strategies with the violation probability being greater than a preset safety threshold value to obtain the prediction number of the violation nodes;
the first prediction number calculation module is used for calculating a first prediction number of decision nodes through a first preset formula according to a preset safety factor and the prediction number of the violation nodes;
the second prediction number calculation module is used for calculating the minimum value of the number of decision nodes meeting the preset safety threshold through a second preset formula according to the preset safety threshold to obtain the second prediction number of the decision nodes;
a target number obtaining module, configured to, when a smaller value of the first predicted number and the second predicted number is smaller than a number of participating nodes, take the smaller value of the first predicted number and the second predicted number as a target number of decision nodes; when the first prediction number is larger than or equal to the number of the participating nodes and the second prediction number is larger than or equal to the number of the participating nodes, taking the number of the participating nodes as a target number of decision nodes;
a decision node selection module, configured to select a target number of decision nodes from non-violation nodes according to the target number of the decision nodes, where the non-violation nodes are nodes of the participating nodes except for the violation nodes;
the first preset formula is as follows: the first predicted quantity is equal to the predicted quantity of the illegal nodes/a preset safety factor;
the second preset formula is as follows:
Figure FDA0003638412260000041
wherein the content of the first and second substances,
Figure FDA0003638412260000042
representing an offending node NSThe set of components is composed of a plurality of groups,
Figure FDA0003638412260000043
indicating an offending node NSA set of k nodes in the group,
Figure FDA0003638412260000044
in order for the violation policy to correspond to a violation probability,
Figure FDA0003638412260000045
the number of the combination of the specific k devices when the number of the illegal nodes is k, paIs composed of
Figure FDA0003638412260000046
Probability of a device being selected as a decision node, pbIs composed of
Figure FDA0003638412260000047
The probability of the selected device as a decision node, N is a safety factor, and N is the number of decision nodes.
8. The apparatus of claim 7, further comprising:
and the consensus operation execution module is used for executing the consensus operation in the target block chain through the decision node.
9. The electronic equipment is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing the communication between the processor and the memory through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any one of claims 1 to 6 when executing a program stored in a memory.
10. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1 to 6.
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