CN103957525A - Malicious node detection method based on clustering trust evaluation in internet of vehicles - Google Patents
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Abstract
The invention relates to the field of network communication safety of the internet of vehicles, in particular to a malicious node detection method based on clustering trust evaluation in the internet of vehicles. The malicious node detection method comprises the following steps of clustering and cluster head selection; cluster head node trust evaluation; trust evaluation on member nodes in clusters; cluster trust evaluation and node trust update. A network is divided into a plurality of clusters, intra-cluster nodes are communicated, and different clusters are communicated through cluster heads. The method can adapt to a large VANET network and is good in extendibility, and the communication cost of different intra-cluster member nodes is reduced. In addition, a recommendation chain on the aspect of recommendation trust computation is simplified, and the computation is performed by directly using packet loss rate.
Description
Technical Field
The invention relates to the field of Internet of vehicles network communication safety, in particular to a malicious node detection method based on clustering trust evaluation in the Internet of vehicles.
Background
The internet of vehicles is a vehicle-mounted self-organizing network, is a fast-moving broadband multi-hop wireless network, and is used for realizing communication between vehicles and between the vehicles and roadside infrastructure in a moving process, so that the vehicles in a certain range can exchange mutual condition information and road traffic information, the traffic efficiency can be improved, and the driving safety of a driver can be ensured. However, the characteristics of the car networking network such as openness, fast topology change and high autonomy make the car networking network face more serious security challenges than a general mobile ad hoc network, such as the problems that malicious vehicles spread false road information, and selfish vehicles refuse to cooperate with other vehicles.
The architecture of a conventional Ad-hoc network can be classified into a planar structure and a hierarchical structure. In the plane structure, the positions of the nodes are relatively equal, the network topology is simple, robust and convenient to maintain, but when the number of the nodes is large, large overhead is caused, the processing capacity is weakened, and even a path is interrupted, so the plane structure is mainly suitable for a small network. Aiming at the defects, a hierarchical structure suitable for a large-scale network is provided, the network is divided into a plurality of clusters, data communication among member nodes of different clusters is realized through cluster head nodes in each cluster, the method has good expandability, communication overhead among the member nodes in different clusters is reduced, and mobility management among the nodes is facilitated.
The traditional security mechanism, such as an authentication protocol, a digital signature, key management and the like, has a certain precaution effect on external attacks of the network, but in the face of internal attacks of malicious nodes in the network, such as malicious packet loss, data tampering, false information provision and the like, the existing security mechanism is difficult to effectively resist, so that a new scheme needs to be adopted to detect the malicious nodes in the network, and the security of the internet of vehicles is ensured.
Most of the currently proposed malicious node detection methods are in the context of wireless sensor networks, such as a detection algorithm based on multivariate classification [ liuhuabo, build-up, sannhong, a wireless sensor network malicious node detection algorithm based on multivariate classification [ J ]. sensory technical report [ 2011(05) ], a detection method based on reputation [ forest, research on a malicious node identification method based on a reputation mechanism in a mobile AdHoc network [ D ]. harbin industrial university 2010], a detection method based on node behaviors [ huilong, detection of malicious nodes based on node behavior classification in a wireless sensor network [ D ]. china metering institute 2013], while in vehicle networking (VANET), most of the detection methods also consider the nodes in the network to be at the same level, and in large networks, large expenses are caused.
George et al propose a trust model based on semi-ring algebraic theory. In this model, the computational reasoning of trust is analogous to finding the shortest path problem on weighted directed graph G (V, E). Points in the graph represent nodes in the network, directed edges represent trust relationships, and the trust value between the two nodes is calculated by using a semi-ring algebraic theory and is evaluated. The weighted edges of node i to node j represent the view of node i to node j. The weight function is defined as I (I, j): V multiplied by V → S, and S is an idea space and consists of two components of trust estimation value trust and confidence value confidence, wherein the confidence value is the reliability of trust estimation established after the two nodes are interacted for a plurality of times and represents the quality of trust. In the model method, the trust degree given to the destination node by the source node is calculated according to the trust degree of the intermediate nodes, and when a plurality of intermediate nodes exist, the whole recommendation chain is long and the calculation is complex. In the method, all vehicle nodes are in the same level, unified management is lacked, when the network scale is large and the number of the vehicle nodes is large, the control and routing overhead is large, the expandability is poor, and the relation that trust changes along with time is not considered, so that the situation that the original normal nodes are changed into malicious nodes cannot be detected.
Disclosure of Invention
The invention aims to provide a malicious node detection method suitable for large-scale vehicle networking. As shown in figure 1, the network is divided into a plurality of clusters, the nodes in the clusters communicate, and the cluster heads communicate among different clusters, so that the method has good expandability, reduces the communication overhead among the member nodes in different clusters, calculates the trust value according to the trust model by the communication result, and considers the node as a malicious node and removes the node from the network when the trust value of the node is lower than the preset threshold value.
In order to achieve the purpose, the invention provides a malicious node detection method based on clustering trust evaluation in the internet of vehicles, which comprises the following steps:
(1) clustering, selecting a cluster head: initializing a network, forming nodes with small mobility in all nodes of the network into a cluster, and selecting the node with the minimum mobility change relative to a neighbor as a cluster head;
(2) and (3) evaluating trust of cluster head nodes: the cluster head nodes are managed by the base station, and the comprehensive trust value is evaluated according to the direct trust value of the base station and the recommended trust values of other cluster head nodes; the direct trust value is an objective statistical result and is calculated according to the successful times and the failed times of interaction between the base station node and the cluster head node; calculating the recommended trust value by using packet loss rate;
(3) and (3) evaluating trust of member nodes in the cluster: the member nodes in the cluster are managed by the cluster head node, and the comprehensive trust value of the member nodes in the cluster comprises a direct trust value of the cluster head node to the member nodes and recommended trust values of other nodes in the cluster; the direct trust value is an objective statistical result and is calculated according to the interactive success times and the interactive failure times of the cluster head node and the member nodes; calculating the recommended trust value by using packet loss rate;
(4) cluster trust evaluation: the trust value of the cluster is jointly determined by the cluster head node and all member nodes in the cluster;
(5) and (3) updating node trust: and updating trust at intervals of T, and selecting the highest trust in each cluster as a cluster head on the basis of an initial cluster head selection algorithm.
Further, the above step (1) relative mobility calculation formula is:
wherein,indicating the relative mobility of node neighbor node X to node Y,indicating the magnitude of the information power received from the neighboring node X at the last time instant Y,the value of the power of the information received from the neighbor node X at the current moment Y after the delta t time;the smaller the value, the smaller the change in the motion of the relative adjacent point, ifThen X will be1And selecting the cluster head.
Further, the direct trust value, the recommended trust value and the comprehensive trust value in the step (2) are obtained by the following formulas:
setting the base station to perform x times of behavior observation on the cluster head node, wherein the number of times of obtaining the normal behavior (namely, successful interaction) of the cluster head node is u, the number of times of interaction failure is f, and then the direct trust value calculation formula of the base station i on the cluster head node h is as followsLambda is negative event influence strength;
the recommended trust value calculation formula of other cluster head nodes isWherein N isfThe actual forwarded data packet number of the cluster head node h is Nr, and the received data packet statistical value which is required to be forwarded from each cluster head node is Nr;
the comprehensive trust value calculation formula of the cluster head node h is Th＝w1 *dih+(1－w1 )rhWherein w is1Weight occupied by direct trust value, dihDirect trust value, r, for base station i to cluster head node hhAnd recommending trust values for other nodes in the cluster.
More further, the direct trust value, the recommended trust value and the comprehensive trust value in the step (3) are obtained by the following formulas:
setting a cluster head to observe the behavior of member nodes in the cluster for x times, wherein the number of times of obtaining the normal behavior of the member nodes in the cluster is u, iIf the number of the regular behaviors is f, the direct trust value of the cluster head node h to the member node k in the cluster is calculated according to the formulaLambda is negative event influence strength;
the recommended trust value calculation formula of other member nodes in the cluster isWherein N isfNumber of packets actually sent for member node k in the cluster, NrStatistics for received data packets from member nodes in each cluster;
the calculation formula of the comprehensive trust value of the member nodes in the cluster is Tk＝w2dhk+(1－w2)rkWherein d ishkDirect trust value, r, for cluster head node h to member node k in the clusterkRecommending trust values, w, for other nodes in the cluster2Weight occupied by direct trust value of cluster head node, w2Comprehensive trust value T with cluster head node hhIn connection with, if ThIf it is greater than the predetermined threshold value, w2The value is larger, otherwise, the threshold is selected to be 0.6, the node with the trust degree lower than the threshold is considered as a malicious node, the node with the trust degree higher than the threshold is considered as a normal node, and the trust degree is 1 at most.
Further, the formula for calculating the trust value of the cluster in the step (4) isWherein, let cluster be i, total n nodes (including cluster head node) in cluster i, ThIs the integrated trust value, w, of the cluster head node3Is the proportion of the cluster head trust value in the cluster head, w3The larger the cluster trust value representing the cluster is more dependent on the cluster head node, the trust value of the cluster can be used as a reference for the whole cluster, and when the cluster trust value is lower than the threshold value, the cluster trust value is removed from the network.
The existing malicious node detection method based on trust is mostly based on a network plane structure, when the number of network nodes is large, particularly in the mobile environment of the vehicular networking VANET, the detection cost is large. And the recommendation chain is simplified in the aspect of recommendation trust calculation, and the packet loss rate is directly used for calculation.
Drawings
FIG. 1 is a schematic diagram of a clustering structure.
FIG. 2 is a flow chart of the detection method of the present invention.
FIG. 3 is a diagram of node X1,X2Schematic diagram of relative movement.
Detailed Description
As shown in fig. 2, the detection method of the present invention has 5 steps, which are respectively:
(1) clustering, selecting a cluster head: initializing a network, forming nodes with small mobility in all nodes of the network into a cluster, and selecting the node with the minimum mobility change relative to a neighbor as a cluster head;
(2) and (3) evaluating trust of cluster head nodes: the cluster head nodes are managed by the base station, and the comprehensive trust value is evaluated according to the direct trust value of the base station and the recommended trust values of other cluster head nodes; the direct trust value is an objective statistical result and is calculated according to the successful times and the failed times of interaction between the base station node and the cluster head node; calculating the recommended trust value by using packet loss rate;
(3) and (3) evaluating trust of member nodes in the cluster: the member nodes in the cluster are managed by the cluster head node, and the comprehensive trust value of the member nodes in the cluster comprises a direct trust value of the cluster head node to the member nodes and recommended trust values of other nodes in the cluster; the direct trust value is an objective statistical result and is calculated according to the interactive success times and the interactive failure times of the cluster head node and the member nodes; calculating the recommended trust value by using packet loss rate;
(4) cluster trust evaluation: the trust value of the cluster is jointly determined by the cluster head node and all member nodes in the cluster;
(5) and (3) updating node trust: and updating trust at intervals of T, and selecting the highest trust in each cluster as a cluster head on the basis of an initial cluster head selection algorithm.
The invention is further illustrated below with reference to specific embodiments and the accompanying drawings.
Assuming that the number of mobile vehicle nodes on a highway is 50, dividing the vehicle nodes with smaller mobility into a cluster, and selecting the node with the minimum mobility relative to the neighbor nodes as a cluster head, according to the specific method, in a time delta t, the power ratio of two continuous received messages is used as the basis for measuring the relative mobility of the nodes, as shown in fig. 3, n nodes (only two nodes are shown in the figure) are arranged around a node Y, and the relative mobility calculation method is as follows:
in the formula,indicating the relative mobility of the neighbor node X to node Y,indicating the magnitude of the information power received from the neighboring node X at the last time instant Y,the value of the power of the information received from the neighbor node X at the current moment Y after the delta t time;the smaller the value, the smaller the change in the motion of the relative adjacent point, ifThen X will be1And selecting the cluster head.
The method comprises the following specific steps:
(1) all nodes send or receive HELLO messages to or from neighbor nodes. Each node calculates the power of two consecutive messages received from the neighbor nodes and their relative mobility metrics according to the above equation. Then according to A set of relative mobility metrics M is calculated.
(2) All nodes set the initial state as the non-cluster state, and each node broadcasts a HELLO message to a neighbor of one hop in each broadcast interval, wherein the HELLO message comprises the mobility metric M (initially set to 0). And stores it in the neighbor list along with the timeout timer.
(3) And if the M value of the node is lower than the M values of all other neighbors, selecting the node as a cluster head, otherwise, selecting the node as a cluster member.
And (3) evaluating trust of cluster head nodes: because the node trust is directly evaluated according to historical experience, the base station and the cluster head node need a recording table to record the number of successful times, failure times, the number of successfully forwarded data packets and the total number of data packets required to be forwarded during the past period of time and the direct interaction with other nodes. And (3) within a certain period of time, directly interacting the base station and a cluster head node h successfully U times and failing f times, obeying uniform distribution U (0,1) according to the prior probability of successful interaction between the base station and the cluster head node h assumed by Bayes, marking as pi (p), and deducing to obtain the beta distribution obeyed by the posterior probability. After the base station carries out x-time interaction on the cluster head node, a new event R occurs, wherein R is that the x-time interaction occurs u times successfully, and P (R | P) ═ Pu(1－p)x－uFrom a continuous form of the total probability formula
The posterior probability represents the update probability after the occurrence of the event R, and according to the bayes theorem, the density function is as follows:
it can be seen that the posterior probability density of p is not uniformly distributed any more, but is Beta distributed, and Beta (u +1, x-u +1) is satisfied, the future success event is predicted by the above formula, and p (x) is "the probability that x times of successful interaction, i.e. x +1 times of successful interaction, are known, i.e. the direct trust value: <math>
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considering that in the real world, a malicious behavior can greatly reduce the trust evaluation of the subject on the object, namely, the influence of a negative event on the trust is larger than that of a positive event, the influence of the negative event is introduced to correct a direct trust value, and thenThe direct trust value of the base station i to the cluster head node h isWherein, the negative event influence strength lambda is 2c(c is more than or equal to 0), c is a continuous packet loss counter, the initial value of c is 0, the increment is 1 when packet loss occurs for the first time, then c is counted up after continuous packet loss, the lambda value is increased exponentially rapidly, the influence of continuous packet loss on the trust value is amplified rapidly, and when no packet loss occurs in observation of two continuous time periods, c is decreased by 1 until the value is decreased to 1, the lambda is also decreased slowly, the influence of a packet loss event is weakened slowly, and the direct trust value is enabled to show the characteristics of rapid decrease and slow recovery by introducing negative event influence lambda so as to accord with the change rule of the trust of the real world.
Recommending trust values of cluster head nodes are obtained by recommending other cluster head nodes, in order to simplify the model, the recommending trust values are calculated by applying packet loss rate, and N is assumedfNumber of packets actually forwarded for cluster head node h, NrFor the received data packet statistics value required to be forwarded from each cluster head node, the recommended trust value of the cluster head node h is
The comprehensive trust value of the cluster head node h is Th＝w1 *dih+(1－w1 )rhWherein w is1Taking w as the weight of the direct trust value and considering that the proportion of the direct trust value is larger1Is 0.7. If the trust value is lower than the specified threshold value of 0.6, the node is removed, otherwise, the trust value is stored in the record table of the cluster head node h.
And (3) evaluating trust of member nodes in the cluster: the evaluation method is the same as that of a cluster head node, the cluster head is set to carry out x times of behavior observation on member nodes in the cluster, wherein the number of times of obtaining the normal behavior of the member nodes in the cluster is u, and then the direct trust value calculation formula of the member nodes in the cluster k by the cluster head node h isThe recommended trust value calculation formula of other member nodes in the cluster isWherein N isfNumber of packets actually forwarded for member node k in the cluster, NrThe statistics of the received data packets which come from member nodes in each cluster and require to be forwarded; the calculation formula of the comprehensive trust value of the member nodes in the cluster is Tk＝w2dhk+(1－w2)rkWherein d ishkDirect trust value, r, for cluster head node h to member node k in the clusterkRecommended trust value, w, for nodes k in other clusters2Weight occupied by direct trust value of cluster head node, w2Comprehensive trust value T with cluster head node hhIn connection with, if ThIf it is greater than the predetermined threshold value, w2The value is larger, otherwise, the value is smaller, the threshold is selected to be 0.6, if the comprehensive trust degree is lower than the threshold, the node is considered to be a malicious node, if the comprehensive trust degree is higher than the threshold, the node is a normal node, and the trust degree is 1 at most. And if the comprehensive trust value of the member node in the cluster is lower than a specified threshold value, removing the node, otherwise, storing the node in the cluster head node record table.
Cluster trust evaluation: the trust value of the cluster is jointly determined by the cluster head node and all member nodes in the cluster. If n nodes (including cluster head nodes) in the cluster i are set, the trust vector of the whole cluster is Tiv＝(T1,T2,...,Tn-1,Th) And combining the trust values of the nodes to obtain the trust value of the cluster i as follows:
wherein w3Is the proportion of the cluster head trust value in the cluster head, w3The larger the cluster trust value representing the cluster is more heavily focused on the cluster head node. The trust value of a cluster may be used as a reference for the entire cluster and when it is below a threshold, it is removed from the network.
And (3) updating node trust: due to the mobile characteristic of the nodes, the network topology structure changes dynamically, the node trust value needs to be updated at intervals of a period T, when a member node with lower node trust degree enters a cluster where a cluster head with higher node trust degree is located, the original cluster head node does not change, when a member node with higher trust degree enters a cluster with lower cluster head trust degree, the cluster head node needs to be updated, and on the basis of an initial cluster head selection algorithm, the cluster head with higher trust degree is selected.
Claims (5)
1. A malicious node detection method based on clustering trust evaluation in Internet of vehicles is characterized by comprising the following steps:
(1) clustering, selecting a cluster head: initializing a network, forming nodes with small mobility in all nodes of the network into a cluster, and selecting the node with the minimum mobility change relative to a neighbor as a cluster head;
(2) and (3) evaluating trust of cluster head nodes: the cluster head nodes are managed by the base station, and the comprehensive trust value is evaluated according to the direct trust value of the base station and the recommended trust values of other cluster head nodes; the direct trust value is an objective statistical result and is calculated according to the successful times and the failed times of interaction between the base station node and the cluster head node; calculating the recommended trust value by using packet loss rate;
(3) and (3) evaluating trust of member nodes in the cluster: the member nodes in the cluster are managed by the cluster head node, and the comprehensive trust value of the member nodes in the cluster comprises a direct trust value of the cluster head node to the member nodes and recommended trust values of other nodes in the cluster; the direct trust value is an objective statistical result and is calculated according to the interactive success times and the interactive failure times of the cluster head node and the member nodes; calculating the recommended trust value by using packet loss rate;
(4) cluster trust evaluation: the trust value of the cluster is jointly determined by the cluster head node and all member nodes in the cluster;
(5) and (3) updating node trust: and updating trust at intervals of T, and selecting the highest trust in each cluster as a cluster head on the basis of an initial cluster head selection algorithm.
2. The method for detecting the malicious node based on the clustering trust evaluation in the internet of vehicles according to claim 1, wherein the relative mobility calculation formula in the step (1) is as follows:
wherein,indicating the relative mobility of node neighbor node X to node Y,indicating the magnitude of the information power received from the neighboring node X at the last time instant Y,the value of the power of the information received from the neighbor node X at the current moment Y after the delta t time;the smaller the value, the smaller the change in the motion of the relative adjacent point, ifThen X will be1And selecting the cluster head.
3. The method for detecting the malicious nodes based on the clustering trust evaluation in the internet of vehicles according to claim 1, wherein the direct trust value, the recommended trust value and the comprehensive trust value in the step (2) are obtained by the following formulas:
setting the base station to perform x times of behavior observation on the cluster head node, wherein the number of times of obtaining the normal behavior (namely, successful interaction) of the cluster head node is u, the number of times of interaction failure is f, and then the direct trust value calculation formula of the base station i on the cluster head node h is as followsLambda is negative event influence strength;
the recommended trust value calculation formula of other cluster head nodes isWherein N isfNumber of packets actually forwarded for cluster head node h, NrThe statistics of the data packets which are required to be forwarded for the received data packets from each cluster head node;
the comprehensive trust value calculation formula of the cluster head node h is Th＝w1 *dih+(1－w1 )rhWherein w is1Weight occupied by direct trust value, dihDirect trust value, r, for base station i to cluster head node hhAnd recommending trust values for other nodes in the cluster.
4. The malicious node detection method based on the clustered trust evaluation in the internet of vehicles according to claim 1, wherein the direct trust value, the recommended trust value and the comprehensive trust value in the step (3) are obtained by the following formulas:
setting a cluster head to perform x times of behavior observation on member nodes in the cluster, wherein the number of times of obtaining normal behaviors of the member nodes in the cluster is u, the number of times of obtaining abnormal behaviors is f, and then the direct trust value calculation formula of the member nodes in the cluster k by the cluster head node h is as followsLambda is negative event influence strength;
the recommended trust value calculation formula of other member nodes in the cluster isWherein N isfNumber of packets actually forwarded for member node k in the cluster, NrThe statistics of the received data packets which are from member nodes in the cluster and require to be forwarded;
the calculation formula of the comprehensive trust value of the member nodes in the cluster is Tk＝w2dhk+(1－w2)rkWherein d ishkDirect trust value, r, for cluster head node h to member node k in the clusterkRecommending trust values, w, for other nodes in the cluster2Weight occupied by direct trust value of cluster head node, w2Comprehensive trust value T with cluster head node hhlIn connection with, if ThlIf it is greater than the predetermined threshold value, w2The value is larger, otherwise, the threshold is selected to be 0.6, the node with the trust degree lower than the threshold is considered as a malicious node, the node with the trust degree higher than the threshold is considered as a normal node, and the trust degree is 1 at most.
5. The method for detecting malicious nodes in the internet of vehicles based on clustering trust evaluation as claimed in claim 1, wherein the trust value calculation formula of the cluster in the step (4) isWherein, let cluster be i, total n nodes (including cluster head node) in cluster i, w3Is the proportion of the cluster head trust value in the cluster head, w3The larger the cluster trust value representing the cluster is more dependent on the cluster head node, the trust value of the cluster can be used as a reference for the whole cluster, and when the cluster trust value is lower than the threshold value, the cluster trust value is removed from the network.
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