CN112055012A - Distributed system - Google Patents
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- CN112055012A CN112055012A CN202010904771.9A CN202010904771A CN112055012A CN 112055012 A CN112055012 A CN 112055012A CN 202010904771 A CN202010904771 A CN 202010904771A CN 112055012 A CN112055012 A CN 112055012A
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Abstract
The invention relates to a distributed system, regard the participant who applies for resources or provides resources in the distributed system as the node; the nodes include a benevolent node, a Sybil node (malicious node), and a pending node; if there is a record for providing resource or obtaining resource between nodes, it is regarded as forming a connection edge; when the node to be determined is used as an applicant to send a resource application, assuming that a participant for providing resources receiving the resource application is a good node, in the random routing, respectively extracting the characteristics of the applicant node and the provider node, and accordingly identifying the property of the node to be determined which sends the resource application so as to determine whether to provide the resources for the node to be determined. The invention can effectively identify the Sybil node, thereby resisting the Sybil attack.
Description
Technical Field
The invention relates to the technical field of computer distributed systems, in particular to a distributed system.
Background
Due to the characteristics of distribution, openness and anonymity in the distributed system, the distributed system is easy to attack. Sybil attacks (which may be interpreted as Sybil attacks) refer to attack patterns in which a user may create multiple false identities, thereby using these identities to control or affect a large number of normal nodes of the system. Resource sharing (e.g., file downloading) is an important application in distributed systems. Nodes that normally provide resources are called benevolent nodes; if a node simply acquires resources of other nodes and does not provide resources to other nodes, such a node is called a Sybil node.
A trusted central certificate authority can effectively prevent Sybil attacks by allocating unique identifiers to each node. This mechanism requires a central certification authority and is not applicable in many cases, for example, it is difficult to select or establish a separate entity that is trusted by the entire system. The central certificate authority can become a bottleneck and may cause QoS attacks. It follows that the prior art still has difficulty effectively combating Sybil attacks.
Disclosure of Invention
In view of this, the present invention provides a distributed system to solve the technical problem in the prior art that it is difficult to effectively combat the Sybil attack, and improve the security of the distributed system.
The technical solution of the present invention is to provide a distributed system, which realizes node property identification based on the distributed system, and adopts the following node property identification method, including the following specific steps:
regarding the participants applying for the resources or providing the resources in the distributed system as nodes; the nodes comprise a good node, a Sybil node and a pending node;
if there is a record for providing resource or obtaining resource between nodes, it is regarded as forming a connection edge;
when the node to be determined is used as an applicant to send a resource application, assuming that a participant for providing resources receiving the resource application is a good node, in the random routing, respectively extracting the characteristics of the applicant node and the provider node, and accordingly identifying the property of the node to be determined which sends the resource application so as to determine whether to provide the resources for the node to be determined.
Optionally, the selecting of the random route means that, if the degree of one node is m, the probability that the adjacent nodes are selected as next hops is 1/m, a path is randomly searched from the applicant node or the provider node, the path length is w, the distance between any two adjacent nodes is taken as a unit length, the path length w is a multiple of the unit length, and if the number of nodes in the shortest path between the applicant node and the provider node is L.
Optionally, the features extracted from the applicant node and the provider node include features of an intersection node of random paths from the applicant node and the provider node, features of different property or type nodes in three positions, namely, far distance, middle distance and near distance, of the random paths from the applicant node, and features of different property or type nodes in three positions, namely, far distance, middle distance and near distance, of the random paths from the provider node.
Optionally, the features extracted from the applicant node and the provider node further include the number of resources in the different transfer directions at the three positions, i.e., the far distance, the middle distance, and the near distance, of the random path from the applicant node, and the number of resources in the different transfer directions at the three positions, i.e., the far distance, the middle distance, and the near distance, of the random path from the provider node.
Optionally, the more similar the features extracted from the applicant node and the features extracted from the provider node, the greater the possibility that the applicant is determined to be a good node.
Optionally, the numerical range of the feature "the distance of the random path from the applicant node is far, the characteristics of the nodes with different properties or types in the middle and near three positions, the distance of the random path from the provider node is far, the characteristics of the nodes with different properties or types in the middle and near three positions, the distance of the random path from the applicant node is far, the resource numbers of the random path from the provider node in different transmission directions in the middle and near three positions, and the resource numbers of the random path from the provider node in different transmission directions in the middle and near three positions" is normalized, and after the normalization processing is performed by using a K-means clustering algorithm, the feature data of the far, middle and near three positions are extracted.
Optionally, an AdaBoost algorithm in a machine learning algorithm is used to train on a given training set, where the training set is obtained from features extracted from applicant nodes and provider nodes and properties of the given nodes, a series of weak classifiers are learned from training data in the training set, and the weak classifiers are combined into a strong classifier, and the strong classifier is used to identify properties of unknown undetermined nodes.
Optionally, the AdaBoost algorithm in the machine learning algorithm specifically includes the following steps: given a training set D { (x)1,y1),(x2,y2)...,(xn,yn) In which xiAll features referring to the ith node; y isiAre correspondingly marked, yiSay 1 that the node is a good node, yi0, the node is a Sybil node;
define f (x) as the currently learned classifier, the purpose of training is to let the loss function
Gradually decrease;
in the (m + 1) th round of training, fm+1(x)＝fm(x) + h (x) for the purpose of finding h (x) letting fm+1(x) As close to y as possible; using a gradient lifting algorithm, fitting the residuals using a decision tree cart, yielding:
wherein J is the total number of features, J is the jth feature, and m refers to m trees; obtaining a classifier consisting of m trees after training is completed:
optionally, classifying by using an artificial neural network, and establishing a model:
given a training set D { (x)1,y1),(x2,y2)...,(xn,yn) In which xiAll features referring to the ith node; y isiAre correspondingly marked, yiSay 1 that the node is a good node, yi0, the node is a Sybil node;
the architecture of the artificial neural network comprises an input layer, a hidden layer and an output layer, wherein the hidden layer is one or more layers, the number of the hidden layers is used as a hyper-parameter, and the hidden layer is adjusted according to the accuracy in training; the loss function of the method adopts cross entropy and adopts a back propagation mode for training.
Compared with the prior art, the invention has the following advantages: the method adopts a random path sampling technology, analyzes different characteristics and resource number information of nodes in the path, judges the properties of the nodes according to extracted characteristics of surrounding nodes, trains by adopting a given training set based on a machine learning algorithm or an artificial neural network, and further identifies the properties of unknown nodes to be determined. The invention can effectively identify the Sybil node, thereby resisting the Sybil attack.
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Fig. 1 is a schematic diagram of a node in the present invention.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention is not limited to only these embodiments. The invention is intended to cover alternatives, modifications, equivalents and alternatives which may be included within the spirit and scope of the invention.
In the following description of the preferred embodiments of the present invention, specific details are set forth in order to provide a thorough understanding of the present invention, and it will be apparent to those skilled in the art that the present invention may be practiced without these specific details.
The invention is described in more detail in the following paragraphs by way of example with reference to the accompanying drawings. It should be noted that the drawings are in simplified form and are not to precise scale, which is only used for convenience and clarity to assist in describing the embodiments of the present invention.
Referring to fig. 1, a node and a random route in the present invention are illustrated. In the figure, w represents the path length, A, B represents two nodes respectively, and the point B to a represents that node a provides resources to node B, with this as the positive direction. Regarding the participants applying for the resources or providing the resources in the distributed system as nodes; the nodes comprise a good node, a Sybil node and a pending node;
if there is a record for providing resource or obtaining resource between nodes, it is regarded as forming a connection edge;
when the node to be determined is used as an applicant to send a resource application, assuming that a participant for providing resources receiving the resource application is a good node, in the random routing, respectively extracting the characteristics of the applicant node and the provider node, and accordingly identifying the property of the node to be determined which sends the resource application so as to determine whether to provide the resources for the node to be determined.
The random routing is that if the degree of a node is m, the probability that adjacent nodes are selected as next hops is 1/m, a path is randomly searched from an applicant node or a provider node, the path length is w, the connecting edge between two nodes is taken as a unit length, the path length is w is a multiple of the unit length, and if the number of nodes in the shortest path between the applicant node and the provider node is L, w > is 1/2L. Determining whether an intersection node exists in a random route between an applicant node and a provider according to the length of a path length w, and if the number of nodes in the shortest path between the applicant node and the provider node is L, if w > is 1/2L, the intersection node exists in the random path from the applicant node and the provider node; otherwise, no intersection node exists.
The features extracted from the applicant node and the provider node include features of an intersection node of random paths from the applicant node and the provider node, features of different property or type nodes in three positions, namely a far distance position, a middle distance position and a near distance position of the random paths from the applicant node, and features of different property or type nodes in three positions, namely a far distance position, a middle distance position and a near distance position of the random paths from the provider node.
The features extracted from the applicant node and the provider node further include the resource numbers of the random path from the applicant node in the three positions of the distance, the middle position and the near position in different transmission directions, and the resource numbers of the random path from the provider node in the three positions of the distance, the middle position and the near position in different transmission directions.
The more similar the features extracted from the applicant node and the features extracted from the provider node, the greater the likelihood that the applicant is a good node.
Normalizing the numerical range of the characteristic, namely the characteristics of nodes with different properties or types in three positions, namely far distance, middle distance and near distance, the resource number of different transmission directions in three positions, namely far distance, middle distance and near distance, and the resource number of different transmission directions in three positions, namely far distance, middle distance and near distance, from the provider node, and extracting the characteristic data of the three positions, namely far distance, middle distance and near distance after the characteristic data are processed by adopting a K-means clustering algorithm.
Training on a given training set by using an AdaBoost algorithm in a machine learning algorithm, wherein the training set is obtained by characteristics extracted from an applicant node and a provider node and the properties of the given node, learning a series of weak classifiers from training data of the training set, combining the weak classifiers into a strong classifier, and identifying the properties of unknown undetermined nodes by using the strong classifier.
The extracted node features and the degree of association between the node features and the node properties are as follows:
(1) the number of intersection nodes and random paths from the applicant node and the provider node.
The relevance degree is as follows: the more the number of intersection nodes, the more likely the two nodes of the applicant and the provider are the same in nature.
(2) And the number of goodwill nodes in the intersection nodes.
The relevance degree is as follows: the more the number of goodwill nodes in the intersection node, the greater the likelihood that the applicant is a goodwill node.
(3) The number of Sybil nodes in the intersection node.
The relevance degree is as follows: the greater the number of Sybil nodes in the intersection node, the greater the likelihood that the applicant is a Sybil node.
(4) And in the random path from the applicant node, the number of goodwill nodes in three positions of far distance, middle distance and near distance.
The relevance degree is as follows: the closer to the applicant, the more benevolent nodes, the more likely the applicant is a benevolent node.
(5) And in the random path from the applicant node, the number of Sybil nodes in three positions of far distance, middle distance and near distance.
The relevance degree is as follows: the closer to the applicant, the more Sybil nodes, the greater the likelihood of the applicant being a Sybil node.
(6) In the random path from the applicant node, the number of nodes providing resources in the forward direction in three positions, namely far, middle and near positions (the forward direction refers to the direction towards the applicant node).
The relevance degree is as follows: the closer the applicant is, the more the number of the nodes providing resources forward is, which indicates that the applicant is always obtaining the resources of other nodes, and the higher the possibility that the applicant is the Sybil node is.
(7) The number of nodes which reversely provide resources in three positions of far distance, middle distance and near distance in the random path from the applicant node (the reverse direction refers to the direction leaving the applicant node)
The relevance degree is as follows: the closer the applicant is, the more the number of nodes providing resources reversely is, which indicates that the applicant is providing other node resources all the time, and the higher the possibility that the applicant is a good node is.
(8) And in the random path from the applicant node, the forward provided resource number is in three positions, namely a far position, a middle position and a near position.
The relevance degree is as follows: the closer to the applicant, the more resources are being provided, indicating that the more resources the applicant and its nearby nodes acquire from other nodes, the greater the likelihood that the applicant is a Sybil node.
(9) And in the random path from the applicant node, the distance is far, middle and near, and the number of resources provided reversely is small.
The relevance degree is as follows: the closer the applicant is, the more resources are provided in the reverse direction, which means that the more resources are provided to other nodes by the applicant and the nodes in the vicinity thereof, the higher the possibility that the applicant is a good node.
(10) And in the random path from the provider node, the number of goodwill nodes in three positions of far distance, middle distance and near distance.
(11) And in the random path from the provider node, the number of Sybil nodes in three positions of far distance, middle distance and near distance.
(12) And in the random path from the provider node, the number of nodes which are far away, in the middle and in the near three positions and are providing resources forward.
(13) And in the random path from the provider node, the number of nodes for reversely providing resources in three positions of far distance, middle distance and near distance.
(14) And in a random path from the provider node, the number of resources provided forward in three positions, namely a far position, a middle position and a near position.
(15) And in a random path from the provider node, reversely providing the number of resources in three positions of far distance, middle distance and near distance.
Feature 10-15 relevance: the more likely the applicant is a benevolent node if the characteristics of the applicant are similar to those of the provider; conversely, the greater the likelihood that the applicant is a Sybil node.
The AdaBoost algorithm in the machine learning algorithm specifically includes the following steps: given a training set D { (x)1,y1),(x2,y2)...,(xn,yn) In which xiAll features referring to the ith node; y isiAre correspondingly marked, yiSay 1 that the node is a good node, yi0, the node is a Sybil node;
define f (x) as the currently learned classifier, the purpose of training is to let the loss function
Gradually decrease;
in the (m + 1) th round of training, fm+1(x)＝fm(x) + h (x) for the purpose of finding h (x) letting fm+1(x) As close to y as possible; using a gradient lifting algorithm, fitting the residuals using a decision tree cart, yielding:
wherein J is the total number of features, J is the jth feature, and m refers to m trees; obtaining a classifier consisting of m trees after training is completed:
other machine learning algorithms are: random forests, support vector machines can also be used as an alternative.
Adopting a random forest algorithm to establish a model:
given a training set D { (x)1,y1),(x2,y2)...,(xn,yn) In which xiAll features referring to the ith node; y isiFor corresponding labels, for example: y isiSay 1 that the node is a good node, yi0, the node is a Sybil node; different values may also be chosen to represent benign and malicious (Sybil) nodes, respectively; and obtaining a plurality of decision trees by training by adopting a random forest algorithm. In implementation, a plurality of trained decision trees are adopted to carry out classification prediction on nodes with unknown properties.
Adopting a support vector machine algorithm to establish a model:
given a training set D { (x)1,y1),(x2,y2)...,(xn,yn) In which xiAll features referring to the ith node; y isiFor corresponding labels, for example: y isiSay 1 that the node is a good node, yi0, the node is a Sybil node; different values may also be chosen to represent benign and malicious (Sybil) nodes, respectively; and (4) obtaining the classifier by training by adopting a support vector machine algorithm. In implementation, a trained classifier is adopted to perform classification prediction on nodes with unknown properties.
Classifying by adopting an artificial neural network, and establishing a model:
given a training set D { (x)1,y1),(x2,y2)...,(xn,yn) In which xiAll the characteristics referring to the ith node; y isiAre correspondingly marked, yiSay 1 that the node is a good node, yi0, the node is a Sybil node;
the architecture of the artificial neural network comprises an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer; the loss function of the method is trained by adopting cross entropy (cross entropy) and a back propagation mode.
Although the embodiments have been described and illustrated separately, it will be apparent to those skilled in the art that some common techniques may be substituted and integrated between the embodiments, and reference may be made to one of the embodiments not explicitly described, or to another embodiment described.
The above-mentioned embodiments are intended to illustrate the technical solutions and advantages of the present invention, and it should be understood that the above-mentioned embodiments are only the most preferred embodiments of the present invention, and are not intended to limit the present invention, and any modifications, additions, equivalents, etc. made within the scope of the principles of the present invention should be included in the scope of the present invention.
Claims (9)
1. A distributed system realizes node property identification based on the distributed system, and adopts the following node property identification method, which comprises the following specific steps:
regarding the participants applying for the resources or providing the resources in the distributed system as nodes; the nodes comprise a good node, a Sybil node and a pending node;
if there is a record for providing resource or obtaining resource between nodes, it is regarded as forming a connection edge;
when the node to be determined is used as an applicant to send a resource application, assuming that a participant for providing resources receiving the resource application is a good node, in the random routing, respectively extracting the characteristics of the applicant node and the provider node, and accordingly identifying the property of the node to be determined which sends the resource application so as to determine whether to provide the resources for the node to be determined.
2. The distributed system of claim 1, wherein: the random routing selection means that if the degree of one node is m, the probability that the adjacent nodes are selected as the next hops is 1/m, a path is randomly searched from an applicant node or a provider node, the path length is w, the connecting edge between the two nodes is taken as a unit length, the path length w is a multiple of the unit length, and if the number of the nodes in the shortest path between the applicant node and the provider node is L.
3. The distributed system of claim 2, wherein: the features extracted from the applicant node and the provider node include features of an intersection node of random paths from the applicant node and the provider node, features of different property or type nodes in three positions, namely a far distance position, a middle distance position and a near distance position of the random paths from the applicant node, and features of different property or type nodes in three positions, namely a far distance position, a middle distance position and a near distance position of the random paths from the provider node.
4. The distributed system of claim 3, wherein: the features extracted from the applicant node and the provider node further include the resource numbers of the random path from the applicant node in the three positions of the distance, the middle position and the near position in different transmission directions, and the resource numbers of the random path from the provider node in the three positions of the distance, the middle position and the near position in different transmission directions.
5. The distributed system of claim 4, wherein: the more similar the features extracted from the applicant node and the features extracted from the provider node, the greater the likelihood that the applicant is a good node.
6. The distributed system of claim 4, wherein: normalizing the numerical range of the characteristic, namely the characteristics of nodes with different properties or types in three positions, namely far distance, middle distance and near distance, the resource number of different transmission directions in three positions, namely far distance, middle distance and near distance, and the resource number of different transmission directions in three positions, namely far distance, middle distance and near distance, from the provider node, and extracting the characteristic data of the three positions, namely far distance, middle distance and near distance after the characteristic data are processed by adopting a K-means clustering algorithm.
7. The distributed system of claim 6, wherein: training on a given training set by using an AdaBoost algorithm in a machine learning algorithm, wherein the training set is obtained by characteristics extracted from an applicant node and a provider node and the properties of the given node, learning a series of weak classifiers from training data of the training set, combining the weak classifiers into a strong classifier, and identifying the properties of unknown undetermined nodes by using the strong classifier.
8. The distributed system of claim 7, wherein: the AdaBoost algorithm in the machine learning algorithm specifically includes the following steps: given a training set D { (x)1,y1),(x2,y2)...,(xn,yn) In which xiAll features referring to the ith node; y isiAre correspondingly marked, yiSay 1 that the node is a good node, yi0, the node is a Sybil node;
define f (x) as the currently learned classifier, the purpose of training is to let the loss function
Gradually decrease;
in the (m + 1) th round of training, fm+1(x)＝fm(x) + h (x) for the purpose of finding h (x) letting fm+1(x) As close to y as possible; using a gradient boosting algorithm, using a decision tree cartFitting the residual error to obtain:
wherein J is the total number of features, J is the jth feature, and m refers to m trees; obtaining a classifier consisting of m trees after training is completed:
9. the distributed system of claim 6, wherein: classifying by adopting an artificial neural network, and establishing a model:
given a training set D { (x)1,y1),(x2,y2)...,(xn,yn) In which xiAll features referring to the ith node; y isiAre correspondingly marked, yiSay 1 that the node is a good node, yi0, the node is a Sybil node;
the architecture of the artificial neural network comprises an input layer, a hidden layer and an output layer, wherein the hidden layer is one or more layers, the number of the hidden layers is used as a hyper-parameter, and the hidden layer is adjusted according to the accuracy in training; the loss function of the method adopts cross entropy and adopts a back propagation mode for training.
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| CN108810030B (en) | 2020-09-25 |
| CN112055012B (en) | 2022-11-25 |
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