CN111431977A - Processing method and system for malicious node in block chain system - Google Patents
Processing method and system for malicious node in block chain system Download PDFInfo
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- CN111431977A CN111431977A CN202010185025.9A CN202010185025A CN111431977A CN 111431977 A CN111431977 A CN 111431977A CN 202010185025 A CN202010185025 A CN 202010185025A CN 111431977 A CN111431977 A CN 111431977A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
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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
- H04L41/30—Decision processes by autonomous network management units using voting and bidding
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/20—Network architectures or network communication protocols for network security for managing network security; network security policies in general
Abstract
The method of the embodiment of the invention provides a processing method and a system for rogue nodes in a block chain system, and the whole block chain system regularly eliminates the rogue nodes and potential rogue nodes in the block chain system by reasonably controlling common nodes and monitoring nodes for replacement; therefore, for the number of huge nodes in the blockchain system, the interference of malicious nodes on the operation of the whole system is reduced, the waste of computing resources of the blockchain system in the malicious node detection and processing process is reduced as much as possible, and the processing speed of the blockchain system is greatly improved.
Description
[ technical field ] A method for producing a semiconductor device
The present invention relates to the field of blockchain technologies, and in particular, to a method and a system for processing malicious nodes in a blockchain system.
[ background of the invention ]
If the blockchain system remains stable and efficient, it needs to rely on the behavioral integrity of the nodes. In the prior art, the monitoring node is arranged to monitor the abnormal behavior of the node, so that for the nodes with large quantity, if each behavior of each node is monitored, the calculation quantity requirement on the monitoring node is extremely high, and if the sampling monitoring mode is adopted for detection, a certain quantity of rogue nodes can be mixed into a block chain, so that the block chain system is damaged.
[ summary of the invention ]
Embodiments of the present invention provide a rotation method and system for a neutral node in a blockchain system.
In a first aspect, an embodiment of the present invention provides a method for processing a rogue node in a blockchain system, where the method includes:
s1, when the distribution period is reached, distributing the common nodes in the block chain system to a plurality of common node groups according to a preset rule, if ungrouped common nodes exist, writing node identifications corresponding to the ungrouped common nodes into an inferior node list, and updating the grouping rate of the ungrouped common nodes;
s2, initiating an intra-group vote by the preferred node in each common node group, picking out at least one weak node from the intra-group common nodes, and writing the node identification corresponding to the weak node into a poor node list;
s3, each monitoring node monitors all corresponding inferior nodes in the inferior node list in a specified monitoring period and generates loyalty points based on each inferior node;
s4, calculating the monitoring performance of each monitoring node by the block chain system according to the deep learning model to generate a corresponding detection score;
s5, writing the node identification corresponding to the inferior node with the loyalty score lower than the preset threshold into the rogue node list, and emptying the residual node identification in the inferior node list;
and S6, when the replacement period is reached, the monitoring node selects the rogue node from the rogue node list according to the priority based on the detection score, broadcasts the replacement request to the common nodes for confirmation, completes the replacement of the corresponding rogue node after the confirmation is passed, and synchronizes the replacement result to all the common nodes.
As to the above-mentioned aspect and any possible implementation manner, there is further provided an implementation manner, where the S1 specifically includes:
s11, when the distribution period is reached, firstly, the grouping rate psi is lower than the preset value psi1N of (A)1The common nodes are preferentially grouped, the grouping rate of the common nodes is recovered to a default value after the common nodes are grouped, and the grouping rate is in inverse proportion to the non-grouping times; then, for N with the grouping rate not lower than the preset value2Grouping common nodes; the number of the common nodes in each common node group is equal to K which is an even numberThe value is 20-40, the number of the common node groups is
S12, unbundling (N)1+N2) Node identifications corresponding to the modK common nodes are written into the poor node list, and the grouping rate of the ungrouped common nodes is updated through psi '-1/m, wherein psi' is the current grouping rate of the ungrouped common nodes, psi is the grouping rate after the ungrouped common nodes are updated, and m is a constant.
As to the above-mentioned aspect and any possible implementation manner, there is further provided an implementation manner, where the S2 specifically includes:
s21, selecting a preferred node according to the current node score of each common node in the common node group, taking other common nodes in the group as voted nodes, wherein the node score is a characteristic parameter of the node performance of the node between the current distribution period and the last distribution period;
s22, in a voting period, the preferred node carries out strong and weak voting on the task processing performance of the voted node, the strong voting times obtained by the voted node are represented by S, the weak voting times obtained are represented by W, and the voted node is guaranteed to be voted at least once in the voting period;
s23, if the S of the voted node is less than or equal to W, the voted node is a weak node and the corresponding node identification is written into the inferior node list; if S > W for all voted nodes in the group, then the voting period will be equal to the total number of voted nodes in the group
And determining the voted node with the minimum value as a weak node and writing the node identification corresponding to the weak node into the poor node list.
As to the above-mentioned aspect and any possible implementation manner, there is further provided an implementation manner, where the S3 specifically includes:
all the corresponding inferior nodes in the inferior node list are monitored by all the monitoring nodes in the appointed monitoring period, and all the monitoring nodes pass through a formula
A loyalty point is calculated corresponding to the bad node, wherein L is a loyalty parameter,
for the average of almost 8 loyalty points, β is the processing number of the node tasks within the preset time, α is the completion number of the node tasks within the preset time, χ is the processing accuracy number of the node tasks within the preset time, is the ungrouping number within the preset time, is the acknowledgement number within the preset time, φ is the acknowledgement voting number within the preset time, A is the first correction parameter, B is the second correction parameter, C is the third correction parameter, and D is the fourth correction parameter.
As to the above-mentioned aspect and any possible implementation manner, there is further provided an implementation manner, where the S4 specifically includes:
s41, constructing a linear strategy pi (x) ═ Mx, wherein M is a strategy weight,
p∈Z+，n∈Z+and p is not equal to n;
s42, carrying out random strategy search on the strategies, wherein M +/-v is in the process of random strategy search every timekThen, a strategy pi is generatedk,±(x)＝(M±νk) x, v is standard deviation detection noise,kis an interference value;
s43, passing through a mean standard filter pair strategy pik,±(x)＝(M±νk) After x treatment, the strategy pi is obtainedk,±(x)＝(M±νk)diag(Σ)-1/2(x-μ)，
k∈{1,2,…,N}；
S44, pass M ← M + σ [ r (π)k,+)-r(πk,-)]kM is updated, sigma is the step length, r (pi)k,+) Is pik,+Continuously carrying out iterative computation on the trajectory until M meets the preset model condition to obtain a required deep learning model;
and S45, calculating the monitoring performance of each monitoring node through a deep learning model to generate a corresponding detection score, wherein the input parameters of the deep learning model at least comprise the calculation power, the detection times, the detection accurate times and the rotation times of the monitoring nodes.
As to the above-mentioned aspect and any possible implementation manner, there is further provided an implementation manner, where the S5 specifically includes:
s51, matching the loyalty point L of each inferior node with a preset threshold L1Comparing;
s52, when the loyalty point L of the inferior node is more than or equal to L1Then, the node identification of the inferior node is removed from the inferior node list;
s53, when the loyalty point L < L of the bad node1And writing the node identification of the inferior node into a malignant node list.
As to the above-mentioned aspect and any possible implementation manner, there is further provided an implementation manner, where the S6 specifically includes:
s61, when the rotation period is reached, the number ξ of the operation nodes in the operation node list is counted1And monitoring node ξ2Comparing;
s62, when ξ1＝ξ2When the monitoring node is in a monitoring state, all the rogue nodes in the rogue node list are replaced with all the monitoring nodes in a one-to-one correspondence mode, and corresponding replacement requests are generated;
s63, when ξ1＜ξ2According to the first eliminating rule, ξ is eliminated2-ξ1The monitoring nodes carry out one-to-one corresponding replacement on the rest monitoring nodes and all the rogue nodes, and generate corresponding replacement requests;
s64, when ξ1＞ξ2According to the second elimination rule, ξ is eliminated1-ξ2Carrying out one-to-one corresponding replacement on the remaining rogue nodes and all monitoring nodes and generating corresponding replacement requests;
and S65, broadcasting the replacement request to the common nodes for confirmation, completing the rotation with the corresponding rogue nodes after the confirmation is passed, and synchronizing the replacement result to all the common nodes.
As with the above-described aspect and any possible implementation, there is further provided an implementation, where the S63 includes:
s631, the monitoring node carries a wheel empty counter, and the number of the wheel empty counter represents the wheel empty times of the node replacement process between the current previous zero clearing period;
s632, taking the number of the monitoring nodes with the empty counter not being 0 as the priority, and sequentially writing the monitoring nodes into an OPEN table, wherein the larger the number is, the higher the priority is, and the number of the node positions in the OPEN table is equal to the number of the rogue nodes;
s633, if the node position in the OPEN table is not completely occupied, executing S624; if the node position in the OPEN table is fully occupied, then S625 is executed;
s634, taking the detection fraction as the priority of the monitoring node with the empty counter of 0, and writing the monitoring node into an OPEN table in sequence, wherein the higher the fraction is, the higher the priority is corresponding to until the node positions in the OPEN table are all occupied;
s635, clearing the numbers of the round empty counters of the monitoring nodes placed in the OPEN table, adding 1 to the numbers of the round empty counters of the monitoring nodes not placed in the OPEN table, and removing the numbers;
and S636, carrying out one-to-one corresponding replacement on the monitoring nodes and all rogue nodes in the OPEN table, and generating corresponding replacement requests.
As with the above-described aspect and any possible implementation, there is further provided an implementation, where the S64 includes:
s641, the rogue node carries a round empty counter, and the number of the round empty counter represents the round empty times of the node replacement process between the current previous zero clearing period;
s642, regarding the number of the rogue nodes with the round trip empty counter not being 0 as the priority, and sequentially writing the rogue nodes into an OPEN table, wherein the larger the number is, the higher the priority is corresponding to, and the number of the node positions in the OPEN table is equal to the number of the monitoring nodes;
s643, if the node position in the OPEN table is not completely occupied, executing S624; if the node position in the OPEN table is fully occupied, then S625 is executed;
s644, writing the rogue nodes into the OPEN table in sequence by taking the vacancy counter as 0 as the rogue node and taking the loyalty points as priorities, wherein the higher the point is, the lower priority is corresponding to the higher the point is until the node positions in the OPEN table are all occupied;
s645, clearing the number of the wheel space counter which is put into the OPEN table and used as the malignant node, adding 1 to the number of the wheel space counter which is not put into the OPEN table and used as the malignant node, and removing the number;
s646, carrying out one-to-one corresponding replacement on the rogue node and all monitoring nodes in the OPEN table, and generating corresponding replacement requests.
In a second aspect, an embodiment of the present invention provides a system for processing a rogue node in a blockchain system, where the system includes:
a blockchain system comprising a plurality of regular nodes and a plurality of detection nodes; the blockchain system further comprises:
the node distribution unit is used for distributing the common nodes in the block chain system to a plurality of common node groups according to a preset rule when a distribution period is reached, writing node identifications corresponding to the ungrouped common nodes into an inferior node list if ungrouped common nodes exist, and updating the grouping rate of the ungrouped common nodes;
the voting processing unit is used for hitting the preferred nodes in each common node group to initiate in-group voting, picking out at least one weak node from the in-group common nodes, and writing the node identification corresponding to the weak node into the poor node list;
the monitoring processing unit is used for enabling all the monitoring nodes to monitor all the corresponding inferior nodes in the inferior node list in a specified monitoring period and generating loyalty points based on the inferior nodes;
the deep learning unit is used for calculating the monitoring performance of each monitoring node according to a deep learning model so as to generate a corresponding detection score;
the replacement processing unit is used for writing the node identification corresponding to the inferior node with the loyalty score lower than the preset threshold into the rogue node list and emptying the residual node identification in the inferior node list;
and the replacing unit is used for enabling the monitoring node to select the rogue node from the rogue node list according to the priority based on the detection score when the replacing period is reached, broadcasting the replacing request to the common node for confirmation, finishing the replacement of the corresponding rogue node after the confirmation is passed, and synchronizing the replacing result to all the common nodes.
One of the above technical solutions has the following beneficial effects:
the method of the embodiment of the invention provides a processing method and a system for rogue nodes in a block chain system, and the whole block chain system regularly eliminates the rogue nodes and potential rogue nodes in the block chain system by reasonably controlling common nodes and monitoring nodes for replacement; therefore, for the huge number of nodes in the blockchain system, the interference of malicious nodes on the operation of the blockchain system is reduced, the waste of computing resources of the blockchain system in the malicious node detection and processing process is reduced as much as possible, and the processing speed of the blockchain system is greatly improved.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
FIG. 1 is a flowchart illustrating a method for handling malicious nodes in a blockchain system according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of S5 according to the embodiment of the present invention;
fig. 3 is a schematic flow chart of S6 according to the embodiment of the present invention;
fig. 4 is a schematic flow chart of S62 according to the embodiment of the present invention;
fig. 5 is a schematic flow chart of S63 according to the embodiment of the present invention;
FIG. 6 is a block chain system architecture diagram according to an embodiment of the present invention;
fig. 7 is a hardware schematic diagram of a node device according to an embodiment of the present invention.
[ detailed description ] embodiments
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail and completely with reference to the following embodiments and accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the 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.
Please refer to fig. 1, which is a flowchart illustrating a method for handling malicious nodes in a blockchain system according to an embodiment of the present invention, wherein the method includes the following steps:
s1, when the distribution period is reached, distributing the common nodes in the block chain system to a plurality of common node groups according to a preset rule, if ungrouped common nodes exist, writing node identifications corresponding to the ungrouped common nodes into an inferior node list, and updating the grouping rate of the ungrouped common nodes;
s2, initiating an intra-group vote by the preferred node in each common node group, picking out at least one weak node from the intra-group common nodes, and writing the node identification corresponding to the weak node into a poor node list;
s3, each monitoring node monitors all corresponding inferior nodes in the inferior node list in a specified monitoring period and generates loyalty points based on each inferior node;
s4, calculating the monitoring performance of each monitoring node by the block chain system according to the deep learning model to generate a corresponding detection score;
s5, writing the node identification corresponding to the inferior node with the loyalty score lower than the preset threshold into the rogue node list, and emptying the residual node identification in the inferior node list;
and S6, when the replacement period is reached, the monitoring node selects the rogue node from the rogue node list according to the priority based on the detection score, broadcasts the replacement request to the common nodes for confirmation, completes the replacement of the corresponding rogue node after the confirmation is passed, and synchronizes the replacement result to all the common nodes.
In the embodiment of the invention, the whole block chain system periodically rejects rogue nodes and potential rogue nodes in the block chain system by reasonably controlling the common nodes and the monitoring nodes to replace; the method comprises the steps of distributing, voting, parameterizing and generating a list for common nodes, and achieving the purpose of finally replacing rogue and potential rogue nodes after replacement is achieved.
It should be noted that S1 specifically includes:
s11, when the distribution period is reached, firstly, the grouping rate psi is lower than the preset value psi1N of (A)1The common nodes are preferentially grouped, the grouping rate of the common nodes is recovered to a default value after the common nodes are grouped, and the grouping rate is in inverse proportion to the non-grouping times; then, for N with the grouping rate not lower than the preset value2Grouping common nodes; the number of the common nodes in each common node group is equal to K, the K is an even number and takes a value of 20-40, and then the number of the common node groups is
S12, unbundling (N)1+N2) Node identifications corresponding to the modK common nodes are written into the poor node list, and ungrouped common nodes are paired through psi' -1/mWhere ψ' is the current grouping rate of ungrouped normal nodes, ψ is the grouping rate after the update of ungrouped normal nodes, and m is a constant.
As to the above-mentioned aspect and any possible implementation manner, there is further provided an implementation manner, where the S2 specifically includes:
s21, selecting a preferred node according to the current node score of each common node in the common node group, taking other common nodes in the group as voted nodes, wherein the node score is a characteristic parameter of the node performance of the node between the current distribution period and the last distribution period;
s22, in a voting period, the preferred node carries out strong and weak voting on the task processing performance of the voted node, the strong voting times obtained by the voted node are represented by S, the weak voting times obtained are represented by W, and the voted node is guaranteed to be voted at least once in the voting period;
s23, if the S of the voted node is less than or equal to W, the voted node is a weak node and the corresponding node identification is written into the inferior node list; if S > W for all voted nodes in the group, then the voting period will be equal to the total number of voted nodes in the group
And determining the voted node with the minimum value as a weak node and writing the node identification corresponding to the weak node into the poor node list.
Specifically, the nodes are divided into small groups, and then voting is carried out in the groups to carry out preliminary screening on the malignant nodes.
Further, the S3 specifically includes:
all the corresponding inferior nodes in the inferior node list are monitored by all the monitoring nodes in the appointed monitoring period, and all the monitoring nodes pass through a formula
A loyalty point is calculated corresponding to the bad node, wherein L is a loyalty parameter,
for the average of almost 8 loyalty points, β is the processing number of the node tasks within the preset time, α is the completion number of the node tasks within the preset time, χ is the processing accuracy number of the node tasks within the preset time, is the ungrouping number within the preset time, is the acknowledgement number within the preset time, φ is the acknowledgement voting number within the preset time, A is the first correction parameter, B is the second correction parameter, C is the third correction parameter, and D is the fourth correction parameter.
Further, the S4 specifically includes:
s41, constructing a linear strategy pi (x) ═ Mx, wherein M is a strategy weight,
p∈Z+，n∈Z+and p is not equal to n;
s42, carrying out random strategy search on the strategies, wherein M +/-v is in the process of random strategy search every timekThen, a strategy pi is generatedk,±(x)＝(M±νk) x, v is standard deviation detection noise,kis an interference value;
s43, passing through a mean standard filter pair strategy pik,±(x)＝(M±νk) After x treatment, the strategy pi is obtainedk,±(x)＝(M±νk)diag(Σ)-1/2(x-μ)，
k∈{1,2,…,N}；
S44, pass M ← M + σ [ r (π)k,+)-r(πk,-)]kM is updated, sigma is the step length, r (pi)k,+) Is pik,+Continuously carrying out iterative computation on the trajectory until M meets the preset model condition to obtain a required deep learning model;
and S45, calculating the monitoring performance of each monitoring node through a deep learning model to generate a corresponding detection score, wherein the input parameters of the deep learning model at least comprise the calculation power, the detection times, the detection accurate times and the rotation times of the monitoring nodes.
The deep learning model greatly reduces the iteration speed, and the deep learning model is generated and updated more quickly, so that a more accurate detection score can be quickly obtained after corresponding parameters are input.
Please refer to fig. 3, which is a schematic flowchart of S5 according to an embodiment of the present invention, wherein S5 specifically includes:
s51, matching the loyalty point L of each inferior node with a preset threshold L1Comparing;
s52, when the loyalty point L of the inferior node is more than or equal to L1Then, the node identification of the inferior node is removed from the inferior node list;
s53, when the loyalty point L < L of the bad node1And writing the node identification of the inferior node into a malignant node list.
Please refer to fig. 3, which is a schematic flowchart of S6 according to an embodiment of the present invention, wherein S6 specifically includes:
s61, when the rotation period is reached, the number ξ of the operation nodes in the operation node list is counted1And monitoring node ξ2Comparing;
s62, when ξ1＝ξ2When the monitoring node is in a monitoring state, all the rogue nodes in the rogue node list are replaced with all the monitoring nodes in a one-to-one correspondence mode, and corresponding replacement requests are generated;
s63, when ξ1＜ξ2According to the first eliminating rule, ξ is eliminated2-ξ1The monitoring nodes carry out one-to-one corresponding replacement on the rest monitoring nodes and all the rogue nodes, and generate corresponding replacement requests;
s64, when ξ1＞ξ2According to the second elimination rule, ξ is eliminated1-ξ2Carrying out one-to-one corresponding replacement on the remaining rogue nodes and all monitoring nodes and generating corresponding replacement requests;
and S65, broadcasting the replacement request to the common nodes for confirmation, completing the rotation with the corresponding rogue nodes after the confirmation is passed, and synchronizing the replacement result to all the common nodes.
Specifically, please refer to fig. 4, which is a schematic flow chart of S63 according to an embodiment of the present invention, wherein S63 includes:
s631, the monitoring node carries a wheel empty counter, and the number of the wheel empty counter represents the wheel empty times of the node replacement process between the current previous zero clearing period;
s632, taking the number of the monitoring nodes with the empty counter not being 0 as the priority, and sequentially writing the monitoring nodes into an OPEN table, wherein the larger the number is, the higher the priority is, and the number of the node positions in the OPEN table is equal to the number of the rogue nodes;
s633, if the node position in the OPEN table is not completely occupied, executing S624; if the node position in the OPEN table is fully occupied, then S625 is executed;
s634, taking the detection fraction as the priority of the monitoring node with the empty counter of 0, and writing the monitoring node into an OPEN table in sequence, wherein the higher the fraction is, the higher the priority is corresponding to until the node positions in the OPEN table are all occupied;
s635, clearing the numbers of the round empty counters of the monitoring nodes placed in the OPEN table, adding 1 to the numbers of the round empty counters of the monitoring nodes not placed in the OPEN table, and removing the numbers;
and S636, carrying out one-to-one corresponding replacement on the monitoring nodes and all rogue nodes in the OPEN table, and generating corresponding replacement requests.
Specifically, please refer to fig. 5, which is a schematic flow chart of S64 according to an embodiment of the present invention, wherein S64 includes:
s641, the rogue node carries a round empty counter, and the number of the round empty counter represents the round empty times of the node replacement process between the current previous zero clearing period;
s642, regarding the number of the rogue nodes with the round trip empty counter not being 0 as the priority, and sequentially writing the rogue nodes into an OPEN table, wherein the larger the number is, the higher the priority is corresponding to, and the number of the node positions in the OPEN table is equal to the number of the monitoring nodes;
s643, if the node position in the OPEN table is not completely occupied, executing S624; if the node position in the OPEN table is fully occupied, then S625 is executed;
s644, writing the rogue nodes into the OPEN table in sequence by taking the vacancy counter as 0 as the rogue node and taking the loyalty points as priorities, wherein the higher the point is, the lower priority is corresponding to the higher the point is until the node positions in the OPEN table are all occupied;
s645, clearing the number of the wheel space counter which is put into the OPEN table and used as the malignant node, adding 1 to the number of the wheel space counter which is not put into the OPEN table and used as the malignant node, and removing the number;
s646, carrying out one-to-one corresponding replacement on the rogue node and all monitoring nodes in the OPEN table, and generating corresponding replacement requests.
The method of the embodiment of the invention provides a processing method and a system for rogue nodes in a block chain system, and the whole block chain system regularly eliminates the rogue nodes and potential rogue nodes in the block chain system by reasonably controlling common nodes and monitoring nodes for replacement; therefore, for the huge number of nodes in the blockchain system, the interference of malicious nodes on the operation of the blockchain system is reduced, the waste of computing resources of the blockchain system in the malicious node detection and processing process is reduced as much as possible, and the processing speed of the blockchain system is greatly improved.
The embodiment of the invention further provides an embodiment of a device for realizing the steps and the method in the embodiment of the method.
Please refer to fig. 6, which is a block chain system architecture diagram according to an embodiment of the present invention, the system includes:
a blockchain system including a plurality of normal nodes 100 and a plurality of detection nodes 200; the blockchain system further comprises:
the node allocating unit 610 is configured to, when an allocation period is reached, allocate the common nodes in the block chain system to a plurality of common node groups according to a preset rule, if there are ungrouped common nodes, write node identifiers corresponding to the ungrouped common nodes into the poor node list, and update the grouping rate of the ungrouped common nodes;
a voting processing unit 620, configured to instruct a preferred node in each common node group to initiate an intra-group voting, pick out at least one weak node from the intra-group common nodes, and write a node identifier corresponding to the weak node into a poor node list;
the monitoring processing unit 630 is configured to enable each monitoring node to monitor all inferior nodes corresponding to the inferior node list in a specified monitoring period, and generate loyalty points based on each inferior node;
the deep learning unit 640 is used for calculating the monitoring performance of each monitoring node according to a deep learning model to generate a corresponding detection score;
the replacement processing unit 650 is configured to write the node identifier corresponding to the inferior node with the loyalty score lower than the preset threshold into the malicious node list, and then empty the remaining node identifiers in the inferior node list;
and a replacing unit 660, configured to, when a replacement cycle is reached, enable the monitoring node to select a rogue node from the rogue node list according to priority based on the detection score, broadcast a replacement request to a common node for confirmation, complete replacement with the corresponding rogue node after the confirmation is passed, and synchronize a replacement result with all the common nodes.
Since each unit module in the embodiment can execute the method shown in fig. 1, reference may be made to the related description of fig. 1 for a part of the embodiment that is not described in detail. Fig. 7 is a hardware schematic diagram of a node device according to an embodiment of the present invention. Referring to fig. 7, at a hardware level, the node device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the node device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (peripheral component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 5, but this does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
In a possible implementation manner, the processor reads the corresponding computer program from the non-volatile memory into the memory and then runs the computer program, and the corresponding computer program can also be acquired from other equipment so as to form the corresponding apparatus on a logic level. And the processor executes the program stored in the memory so as to realize the node working method provided by any embodiment of the invention through the executed program.
An embodiment of the present invention further provides a computer-readable storage medium storing one or more programs, where the one or more programs include instructions, which when executed by a node device including a plurality of application programs, enable the node device to execute the node operating method provided in any embodiment of the present invention.
The method performed by the node device according to the embodiment of the present invention may be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. 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 other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
An embodiment of the present invention further provides a computer-readable storage medium storing one or more programs, where the one or more programs include instructions, which when executed by a node device including a plurality of application programs, enable the node device to execute the node operating method provided in any embodiment of the present invention.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units or modules by function, respectively. Of course, the functionality of the units or modules may be implemented in the same one or more software and/or hardware when implementing the invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments of the present invention are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (10)
1. A method for handling malicious nodes in a blockchain system, the method comprising:
s1, when the distribution period is reached, distributing the common nodes in the block chain system to a plurality of common node groups according to a preset rule, if ungrouped common nodes exist, writing node identifications corresponding to the ungrouped common nodes into an inferior node list, and updating the grouping rate of the ungrouped common nodes;
s2, initiating an intra-group vote by the preferred node in each common node group, picking out at least one weak node from the intra-group common nodes, and writing the node identification corresponding to the weak node into a poor node list;
s3, each monitoring node monitors all corresponding inferior nodes in the inferior node list in a specified monitoring period and generates loyalty points based on each inferior node;
s4, calculating the monitoring performance of each monitoring node by the block chain system according to the deep learning model to generate a corresponding detection score;
s5, writing the node identification corresponding to the inferior node with the loyalty score lower than the preset threshold into the rogue node list, and emptying the residual node identification in the inferior node list;
and S6, when the replacement period is reached, the monitoring node selects the rogue node from the rogue node list according to the priority based on the detection score, broadcasts the replacement request to the common nodes for confirmation, completes the replacement of the corresponding rogue node after the confirmation is passed, and synchronizes the replacement result to all the common nodes.
2. The method according to claim 1, wherein the S1 specifically includes:
s11, when the distribution period is reached, firstly, the grouping rate psi is lower than the preset value psi1N of (A)1The common nodes are preferentially grouped, the grouping rate of the common nodes is recovered to a default value after the common nodes are grouped, and the grouping rate is in inverse proportion to the non-grouping times; then, for N with the grouping rate not lower than the preset value2Grouping common nodes; the number of the common nodes in each common node group is equal to K, the K is an even number and takes a value of 20-40, and then the number of the common node groups is
S12, unbundling (N)1+N2) Node identifiers corresponding to modK common nodes are written into the inferior node listAnd updating the grouping rate of ungrouped common nodes by psi '-1/m, wherein psi' is the current grouping rate of ungrouped common nodes, psi is the grouping rate after updating of ungrouped common nodes, and m is a constant.
3. The method according to claim 1, wherein the S2 specifically includes:
s21, selecting a preferred node according to the current node score of each common node in the common node group, taking other common nodes in the group as voted nodes, wherein the node score is a characteristic parameter of the node performance of the node between the current distribution period and the last distribution period;
s22, in a voting period, the preferred node carries out strong and weak voting on the task processing performance of the voted node, the strong voting times obtained by the voted node are represented by S, the weak voting times obtained are represented by W, and the voted node is guaranteed to be voted at least once in the voting period;
s23, if the S of the voted node is less than or equal to W, the voted node is a weak node and the corresponding node identification is written into the inferior node list; if S > W for all voted nodes in the group, then the voting period will be equal to the total number of voted nodes in the group
And determining the voted node with the minimum value as a weak node and writing the node identification corresponding to the weak node into the poor node list.
4. The method according to claim 1, wherein the S3 specifically includes:
all the corresponding inferior nodes in the inferior node list are monitored by all the monitoring nodes in the appointed monitoring period, and all the monitoring nodes pass through a formula
A loyalty point is calculated corresponding to the bad node, wherein L is a loyalty parameter,
for the average of almost 8 loyalty points, β is the processing number of the node tasks within the preset time, α is the completion number of the node tasks within the preset time, χ is the processing accuracy number of the node tasks within the preset time, is the ungrouping number within the preset time, is the acknowledgement number within the preset time, φ is the acknowledgement voting number within the preset time, A is the first correction parameter, B is the second correction parameter, C is the third correction parameter, and D is the fourth correction parameter.
5. The method according to claim 1, wherein the S4 specifically includes:
s41, constructing a linear strategy pi (x) ═ Mx, wherein M is a strategy weight,
p∈Z+，n∈Z+and p is not equal to n;
s42, carrying out random strategy search on the strategies, wherein M +/-v is in the process of random strategy search every timekThen, a strategy pi is generatedk,±(x)＝(M±νk) x, v is standard deviation detection noise,kis an interference value;
s43, passing through a mean standard filter pair strategy pik,±(x)＝(M±νk) After x treatment, the strategy pi is obtainedk,±(x)＝(M±νk)diag(Σ)-1/2(x-μ)，
k∈{1,2,...,N}；
S44, pass M ← M + σ [ r (π)k,+)-r(πk,-)]kM is updated, sigma is the step length, r (pi)k,+) Is pik,+Continuously carrying out iterative computation on the trajectory until M meets the preset model condition to obtain a required deep learning model;
and S45, calculating the monitoring performance of each monitoring node through a deep learning model to generate a corresponding detection score, wherein the input parameters of the deep learning model at least comprise the calculation power, the detection times, the detection accurate times and the rotation times of the monitoring nodes.
6. The method according to claim 1, wherein the S5 specifically includes:
s51, matching the loyalty point L of each inferior node with a preset threshold L1Comparing;
s52, when the loyalty point L of the inferior node is more than or equal to L1Then, the node identification of the inferior node is removed from the inferior node list;
s53, when the loyalty point L < L of the bad node1And writing the node identification of the inferior node into a malignant node list.
7. The method according to claim 1, wherein the S6 specifically includes:
s61, when the rotation period is reached, the number ξ of the operation nodes in the operation node list is counted1And number of monitoring nodes ξ2Comparing;
s62, when ξ1＝ξ2When the monitoring node is in a monitoring state, all the rogue nodes in the rogue node list are replaced with all the monitoring nodes in a one-to-one correspondence mode, and corresponding replacement requests are generated;
s63, when ξ1＜ξ2According to the first eliminating rule, ξ is eliminated2-ξ1The monitoring nodes carry out one-to-one corresponding replacement on the rest monitoring nodes and all the rogue nodes, and generate corresponding replacement requests;
s64, when ξ1＞ξ2According to the second elimination rule, ξ is eliminated1-ξ2Carrying out one-to-one corresponding replacement on the remaining rogue nodes and all monitoring nodes and generating corresponding replacement requests;
and S65, broadcasting the replacement request to the common nodes for confirmation, completing the rotation with the corresponding rogue nodes after the confirmation is passed, and synchronizing the replacement result to all the common nodes.
8. The method according to claim 7, wherein the S63 includes:
s631, the monitoring node carries a wheel empty counter, and the number of the wheel empty counter represents the wheel empty times of the node replacement process between the current previous zero clearing period;
s632, taking the number of the monitoring nodes with the empty counter not being 0 as the priority, and sequentially writing the monitoring nodes into an OPEN table, wherein the larger the number is, the higher the priority is, and the number of the node positions in the OPEN table is equal to the number of the rogue nodes;
s633, if the node position in the OPEN table is not completely occupied, executing S624; if the node position in the OPEN table is fully occupied, then S625 is executed;
s634, taking the detection fraction as the priority of the monitoring node with the empty counter of 0, and writing the monitoring node into an OPEN table in sequence, wherein the higher the fraction is, the higher the priority is corresponding to until the node positions in the OPEN table are all occupied;
s635, clearing the numbers of the round empty counters of the monitoring nodes placed in the OPEN table, adding 1 to the numbers of the round empty counters of the monitoring nodes not placed in the OPEN table, and removing the numbers;
and S636, carrying out one-to-one corresponding replacement on the monitoring nodes and all rogue nodes in the OPEN table, and generating corresponding replacement requests.
9. The method according to claim 7, wherein the S64 includes:
s641, the rogue node carries a round empty counter, and the number of the round empty counter represents the round empty times of the node replacement process between the current previous zero clearing period;
s642, regarding the number of the rogue nodes with the round trip empty counter not being 0 as the priority, and sequentially writing the rogue nodes into an OPEN table, wherein the larger the number is, the higher the priority is corresponding to, and the number of the node positions in the OPEN table is equal to the number of the monitoring nodes;
s643, if the node position in the OPEN table is not completely occupied, executing S624; if the node position in the OPEN table is fully occupied, then S625 is executed;
s644, writing the rogue nodes into the OPEN table in sequence by taking the vacancy counter as 0 as the rogue node and taking the loyalty points as priorities, wherein the higher the point is, the lower priority is corresponding to the higher the point is until the node positions in the OPEN table are all occupied;
s645, clearing the number of the wheel space counter which is put into the OPEN table and used as the malignant node, adding 1 to the number of the wheel space counter which is not put into the OPEN table and used as the malignant node, and removing the number;
s646, carrying out one-to-one corresponding replacement on the rogue node and all monitoring nodes in the OPEN table, and generating corresponding replacement requests.
10. A system for handling malicious nodes in a blockchain system, comprising:
a blockchain system comprising a plurality of regular nodes and a plurality of detection nodes; the blockchain system further comprises:
the node distribution unit is used for distributing the common nodes in the block chain system to a plurality of common node groups according to a preset rule when a distribution period is reached, writing node identifications corresponding to the ungrouped common nodes into an inferior node list if ungrouped common nodes exist, and updating the grouping rate of the ungrouped common nodes;
the voting processing unit is used for hitting the preferred nodes in each common node group to initiate in-group voting, picking out at least one weak node from the in-group common nodes, and writing the node identification corresponding to the weak node into the poor node list;
the monitoring processing unit is used for enabling all the monitoring nodes to monitor all the corresponding inferior nodes in the inferior node list in a specified monitoring period and generating loyalty points based on the inferior nodes;
the deep learning unit is used for calculating the monitoring performance of each monitoring node according to a deep learning model so as to generate a corresponding detection score;
the replacement processing unit is used for writing the node identification corresponding to the inferior node with the loyalty score lower than the preset threshold into the rogue node list and emptying the residual node identification in the inferior node list;
and the replacing unit is used for enabling the monitoring node to select the rogue node from the rogue node list according to the priority based on the detection score when the replacing period is reached, broadcasting the replacing request to the common node for confirmation, finishing the replacement of the corresponding rogue node after the confirmation is passed, and synchronizing the replacing result to all the common nodes.
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