A sybil attack identification method and device for intelligent connected vehicles

Abstract

The application provides a Sybil attack identification method and device for intelligent networked vehicles, wherein the method comprises the following steps: obtaining three identity-unknown nodes including a malicious node, a Sybil node and a normal node, and reference signal receiving power (RSRP) sequences of the three identity-unknown nodes; finding mutation points of the RSRP sequences of the three identity-unknown nodes by using a Bernaola Galvn segmentation algorithm, and determining the RSRP sequence of the malicious node according to the number of the mutation points; calculating the average value of the difference between the RSRP sequence of the malicious node and the RSRP sequences of the other two identity-unknown nodes, adding the average value of the difference to the RSRP sequence of the malicious node, and obtaining the power-restored RSRP sequence of the malicious node; and calculating the similarity between the power-restored RSRP sequence of the malicious node and the RSRP sequences of the other two identity-unknown nodes corresponding to the power-restored RSRP sequence of the malicious node, and determining the Sybil node according to the similarity. By using the method, the malicious node that initiates a Sybil attack and the Sybil node generated by the malicious node can be identified in the intelligent networked vehicle.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data transmission and vehicle safety. BACKGROUND

[0002] Intelligent networked vehicles communicate with surrounding vehicles through on-board units (OBU, also known as vehicle-mounted nodes) to transmit data such as vehicle position, speed, steering, etc. Vehicles collect data from surrounding vehicles to optimize driving decisions and improve safety and efficiency of travel. The safety of data transmission affects the driving safety of intelligent networked vehicles, and Sybil attack is a common network attack faced by intelligent networked vehicles. The attacker of Sybil attack is usually called malicious node, which will generate multiple false identities, thereby creating multiple untrusted virtual nodes in intelligent networked vehicles, which are called Sybil nodes. Sybil attack can cause a variety of adverse consequences. For example, malicious vehicles can create a large number of virtual vehicles with false identities and false positions, which gives the illusion of traffic congestion in front of nearby vehicles, and these vehicles choose other routes to bypass, while the attacker can get a good road condition. In addition, Sybil attackers can cause more damage to intelligent networked vehicles by launching further attacks. Malicious nodes can send useless messages to target vehicles or roadside units through multiple Sybil nodes to reduce network performance. In another case, malicious nodes can converge seemingly disjointed paths in multi-path routing protocols to it through multiple Sybil nodes, and then discard all messages passing through it and initiate a black hole attack.

[0003] The OBU of intelligent networked vehicles will continuously receive data packets sent by surrounding vehicles, which record signal strength related indicators such as received signal strength indicator (RSSI), reference signal receiving power (RSRP), etc. The present application uses the vehicle-mounted device OBU of Datang Gaohong, which records the reference signal receiving power RSRP. In the LTE network, the reference signal receiving power is a key parameter of wireless signal strength, representing one of the physical layer measurement requirements, which is the average value of the signal power received on all resource elements carrying the reference signal within a certain symbol. The reference signal receiving power will fluctuate due to the influence of vehicle position or road environment, and will also mutate due to the power control of the sender.

[0004] The traditional Sybil attack identification method first collects a received signal strength indicator (RSSI), sorts the RSSIs of data packets sent by the same vehicle according to time to form an RSSI sequence, then calculates the Euclidean distance between the RSSI sequences of different vehicles, and finally performs dynamic time warping to compare the similarity of different sequences; if the similarity is less than a threshold, it is determined that the vehicle nodes corresponding to the two sequences are malicious nodes and Sybil nodes. However, in some cases, it is found that the malicious node will control the transmission power, so that the similarity of the RSSI sequences of the malicious node and the Sybil node is reduced, and the traditional method cannot identify them.

[0005] For the method of identifying Sybil attacks in intelligent connected vehicles, most current researches only compare the similarity of the time sequence of the received signal strength of the vehicle, and when the attacker manipulates the power, the similarity of the time sequence of the malicious node and the Sybil node will be significantly reduced, which cannot effectively detect the malicious node and the Sybil node. SUMMARY

[0006] The present application aims to at least solve one of the technical problems in the related art to some extent.

[0007] To this end, the present application aims to provide a Sybil attack identification method for intelligent connected vehicles, which is used to identify malicious nodes that launch Sybil attacks and the Sybil nodes generated by them.

[0008] To achieve the above-mentioned purpose, the first aspect of the present application provides a Sybil attack identification method for intelligent connected vehicles, comprising:

[0009] obtaining three nodes with unknown identities including a malicious node, a Sybil node and a normal node, and reference signal received power (RSRP) sequences of the three nodes with unknown identities;

[0010] finding the mutation points of the RSRP sequences of the three nodes with unknown identities by using the Bernaola Galvn segmentation algorithm, and determining the RSRP sequence of the malicious node by the number of the mutation points;

[0011] calculating the average value of the difference between the RSRP sequence of the malicious node and the RSRP sequences of the other two nodes with unknown identities, and adding the average value of the difference to the RSRP sequence of the malicious node to obtain the power-restored RSRP sequence of the malicious node;

[0012] respectively calculating the similarity between the power-restored RSRP sequence of the malicious node and the RSRP sequences of the other two nodes with unknown identities corresponding to the power-restored RSRP sequence of the malicious node, and determining the Sybil node by the similarity;

[0013] In addition, the Sybil attack identification method for the intelligent connected vehicle according to the above-mentioned embodiment of the application can further have the following additional technical features.

[0014] Further, in an embodiment of the application, the step of calculating the difference value average of the malicious node RSRP sequence and the RSRP sequences of the other two nodes with unknown identities respectively comprises:

[0015] calculating the difference value between the malicious node RSRP sequence y = <y1, y2, …, y l > and another node RSRP sequence x = <x1, x2, …, x l > at the same time, i.e. d i = x i -y i , i = 1, 2, …, l, and recording the difference value in a sequence to obtain a difference value sequence d = <d1, d2, …, d l >;

[0016] dividing the malicious node RSRP sequence y = <y1, y2, …, y l > into several segments according to the position of the mutation point, and calculating the segment average value of the difference value between the malicious node RSRP and the other node RSRP sequence in a segment unit.

[0017] Further, in an embodiment of the application, the step of calculating the segment average value of the difference value between the malicious node RSRP and the other node RSRP sequence in a segment unit and adding the difference value average to the malicious node RSRP sequence respectively to obtain two malicious node RSRP sequences with power recovery comprises:

[0018] defining the sequence numbers of the start and end of the s-th segment as m and n respectively, and the RSRP difference value average of the segment is wherein k represents the k-th value in the difference value sequence d.

[0019] Further, in an embodiment of the application, the step of adding the difference value average to the malicious node RSRP sequence respectively to obtain two malicious node RSRP sequences with power recovery comprises:

[0020] adding the RSRP difference value average of the segment to the original malicious node RSRP sequence y = <y1, y2, …, y l >, i.e. to obtain the RSRP value with power recovery;

[0021] performing power recovery for each segment to obtain a complete power recovery RSRP sequence, i.e. y' = <y1', y2', …, y l '.

[0022] Further, in one embodiment of the present application, the Sybil node is determined by the similarity, including:

[0023] Respectively calculate the dynamic time warping values (DTW) of the two power-recovered malicious node RSRP sequences and the RSRP sequences of the other two unknown-identity nodes corresponding to the two power-recovered malicious node RSRP sequences, and the Sybil node is the one with the smaller dynamic time warping value (DTW).

[0024] To achieve the above object, the second aspect embodiment of the present application provides a Sybil attack identification device for intelligent connected vehicles, which comprises:

[0025] An acquisition module is configured to acquire three unknown-identity nodes including a malicious node, a Sybil node and a normal node, and reference signal receiving power (RSRP) sequences of the three unknown-identity nodes.

[0026] A searching module is configured to search for mutation points of the RSRP sequences of the three unknown-identity nodes by using a Bernaola Galvn segmentation algorithm, and determine a malicious node RSRP sequence by the number of the mutation points.

[0027] A recovery module is configured to calculate the average value of the difference between the malicious node RSRP sequence and the RSRP sequences of the other two unknown-identity nodes, and add the average value of the difference to the malicious node RSRP sequence to obtain a power-recovered malicious node RSRP sequence.

[0028] An identification module is configured to respectively calculate the similarity between the power-recovered malicious node RSRP sequence and the RSRP sequences of the other two unknown-identity nodes corresponding to the power-recovered malicious node RSRP sequence, and determine the Sybil node by the similarity.

[0029] Further, in one embodiment of the present application, the identification module is further configured to:

[0030] Respectively calculate the dynamic time warping values (DTW) of the two power-recovered malicious node RSRP sequences and the RSRP sequences of the other two unknown-identity nodes corresponding to the two power-recovered malicious node RSRP sequences, and the Sybil node is the one with the smaller dynamic time warping value (DTW).

[0031] To achieve the above object, the third aspect embodiment of the present application provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the Sybil attack identification method for intelligent connected vehicles as described above when executing the computer program.

[0032] To achieve the above object, the fourth aspect of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the intelligent connected vehicle Sybil attack identification method.

[0033] The intelligent connected vehicle Sybil attack identification method of the embodiment of the present application is aimed at intelligent connected vehicles with power control, and performs Sybil attack detection according to RSRP. In particular, a power recovery RSRP calculation method is designed to identify Sybil attacks with power control. For a given RSRP sequence of three unknown identity nodes including malicious nodes, Sybil nodes and normal nodes, first, the Bernaola Galvn segmentation algorithm is used to find the mutation points of each sequence, and then the number of mutation points is used to determine the malicious node RSRP sequence. According to the found mutation points, the malicious node RSRP sequence is divided into multiple segments, and then the RSRP difference value average of each segment of the malicious node RSRP sequence and the other two sequences is calculated, and added to the original malicious node RSRP sequence to obtain the power recovery malicious node RSRP sequence. Finally, the dynamic time planning is used to calculate the similarity of the RSRP sequences between nodes, and the Sybil node is determined by finding the node with the minimum dynamic time planning value of the malicious node RSRP sequence. BRIEF DESCRIPTION OF DRAWINGS

[0034] The above and / or additional aspects and advantages of the present application will become apparent and more readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:

[0035] Figure 1 A flowchart of an intelligent connected vehicle Sybil attack identification method provided by the embodiment of the present application.

[0036] Figure 2 A RSRP sequence diagram of three different nodes provided by the embodiment of the present application.

[0037] Figure 3 A RSRP sequence diagram of node 1 provided by the embodiment of the present application.

[0038] Figure 4 A RSRP sequence diagram of node 2 provided by the embodiment of the present application.

[0039] Figure 5 A RSRP sequence diagram of node 3 provided by the embodiment of the present application.

[0040] Figure 6 A power recovery RSRP sequence diagram of node 2 and node 1 provided by the embodiment of the present application.

[0041] Figure 7 This is a schematic diagram of the RSRP sequence for power recovery of node 2 and node 3 provided in an embodiment of the present invention.

[0042] Figure 8 This is a schematic diagram of the DTW of the RSRP sequences of node 2 and two other nodes provided in an embodiment of the present invention.

[0043] Figure 9 This is a flowchart illustrating a Sybil attack identification device for intelligent connected vehicles, provided as an embodiment of the present invention. Detailed Implementation

[0044] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0045] The Sybil attack identification method for intelligent connected vehicles according to embodiments of the present invention is described below with reference to the accompanying drawings.

[0046] Figure 1 This is a flowchart illustrating a Sybil attack identification method for intelligent connected vehicles provided in an embodiment of the present invention.

[0047] like Figure 1 As shown, the Sybil attack identification method for this intelligent connected vehicle includes the following steps:

[0048] S101: Obtain the reference signal received power (RSRP) sequence of three unknown nodes, including a malicious node, a Sybil node, and a normal node, as well as the three unknown nodes.

[0049] S102: Use the Bernaola Galvn segmentation algorithm to find mutation points in the RSRP sequences of three unknown nodes, and determine the RSRP sequence of the malicious node by the number of mutation points;

[0050] S103: Calculate the mean difference between the RSRP sequence of the malicious node and the RSRP sequences of the other two unknown nodes, and add the mean difference to the RSRP sequence of the malicious node to obtain the RSRP sequence of the malicious node with power recovery.

[0051] S104: Calculate the similarity between the RSRP sequence of the malicious node that has recovered power and the RSRP sequences of its two other unknown nodes, and determine the Sybil node based on the similarity.

[0052] The method uses an on-board equipment OBU, a malicious node controls the transmission power, and a normal node and a Sybil node (generated by the malicious node) do not change the transmission power. Different IDs are assigned to the malicious node, the Sybil node and the normal node, and the ID and the reference signal receiving power of the sender are recorded in each data packet, that is, <ID, RSRP>. Over time, the RSRP sequences of the three nodes are formed as follows: Figure 2 .

[0053] The malicious node launching the Sybil attack reduces the similarity between the RSRP sequences of the malicious node and the Sybil node by adjusting the transmission power, so that the traditional method cannot detect the Sybil attack. After obtaining the RSRP sequences of the malicious node and the Sybil node, the method identifies the power-controlled malicious node according to the mean value change of the RSRP sequence, adjusts the RSRP value, removes the influence of power control, and obtains the RSRP sequence of the power-restored malicious node. Subsequently, the method calculates the Euclidean distance and the dynamic time warping value (Dynamic Time Warping, DTW), compares the similarity between the RSRP sequences of each node after power restoration, and identifies the malicious node and the Sybil node.

[0054] RSRP power restoration

[0055] 1) The Bernaola Galvn segmentation algorithm is used, the t-test significance level is set to 0.01 on both sides, the mutation points of the three node sequences are found, and the sequence with the most mutation points is determined as the malicious node sequence, for example, y = <y1, y2, …, y l For example, Figure 3 The RSRP sequence of node 1 is shown, and 4 mutation points are found, and the horizontal axis corresponds to values 25, 56, 62 and 88. Figure 4 The RSRP sequence of node 2 is shown, and 8 mutation points are found, and the horizontal axis corresponds to values 9, 20, 30, 39, 49, 62, 79 and 87. Figure 5 The RSRP sequence of node 3 is shown, and 2 mutation points are found, and the horizontal axis corresponds to values 20 and 62. It can be seen from the largest number of mutation points of node 2 that node 2 is a malicious node.

[0056] Further, in an embodiment of the present application, the mean value of the difference between the RSRP sequence of the malicious node and the RSRP sequences of the other two nodes with unknown identities is calculated, including:

[0057] The difference between the RSRP sequence y = <y1, y ， …, y l > of the malicious node and the RSRP sequence x = <x1, x2, …, x l > of another node at the same time is calculated, that is, di = x i - y i , i = 1, 2, …, l, and record the difference values in a sequence, obtaining the difference value sequence d = <d1, d2, …, d l >;

[0058] According to the position of the mutation point, the malicious node RSRP sequence y = <y1, y2, …, y l > is divided into several segments, and the segment average value of the difference between the malicious node RSRP and the RSRP sequence of the other node is calculated in the segment unit.

[0059] After obtaining the position of the mutation point, the malicious node RSRP sequence y = <y1, y2, …, y l > is divided into several segments according to the mutation point, and then the segment average value of the difference between the malicious node RSRP and the other sequence and the power recovered RSRP are calculated in the segment unit. Taking the s-th segment of the malicious node as an example, the sequence numbers of the start and end of the segment are denoted as m and n respectively, and the segment average values of the RSRP difference between the segment and node 1 and node 3 are d' and d'' respectively. On the original malicious node RSRP sequence y m～n = <y m , y m+1 , y m+2 , …, y n >, the segment average value of the RSRP difference of the segment is added to obtain the power recovered RSRP sequence, that is, y' m～n = <y m + d', y m+1 + d', y m+2 + d', …, y n + d' > and y'' m～n = <y m + d'', y m+1 + d'', y m+2 + d'', …, y n + d'' >.

[0060] The power recovery is performed on each segment to obtain two power recovered RSRP sequences, that is, y' = <y1', y2', …, y l ' > and y'' = <y1'', y2'', …, y l '', which eliminates the RSRP change caused by the power control of the sender. Subsequently, the power recovered RSRP sequences are used to calculate the DTW values with the other two nodes respectively, that is, the DTW value of y' = <y1', y2', …, y l ' > with the RSRP sequence of node 1, and the DTW value of y'' = <y1'', y2'', …, y l '' with the RSRP sequence of node 3. Specifically as Figure 6 ,Figure 7 as shown.

[0061] Further, in an embodiment of the present application, the segment average value of the difference value of the malicious node RSRP sequence and another node RSRP sequence is calculated in a segment unit, and the difference value average is added to the malicious node RSRP sequence respectively, to obtain two power restored malicious node RSRP sequences, including:

[0062] The sequence numbers of the start and end of the s-th segment are defined as m and n respectively, and the RSRP difference value average of the segment is where k represents the k-th value in the difference value sequence d.

[0063] Further, in an embodiment of the present application, the difference value average is added to the malicious node RSRP sequence respectively, to obtain two power restored malicious node RSRP sequences, including:

[0064] In the original malicious node RSRP sequence y = <y1, y2, …, y l >, the RSRP difference value average of the segment is added, that is to obtain the power restored RSRP value;

[0065] The power restoration is performed on each segment to obtain the complete power restored RSRP sequence, that is y' = <y1', y2', …, y l >.

[0066] The Euclidean distance and the dynamic time warping value DTW are calculated for the RSRP sequence x = <x1, x2, …, x l > and the power restored RSRP sequence y' = <y1', y2', …, y l '> respectively. The smaller the DTW value is, the closer the two sequences are. When the Sybil attack is launched, the DTW value of the malicious node and the Sybil node will be smaller than the DTW value of the malicious node and other nodes. Therefore, after the malicious node identity is determined and the power is restored, the Sybil node is obtained by calculating the DTW value. As shown in Figure 8 , the Euclidean distance and the dynamic time warping are calculated to obtain the DTW values of node 2 and node 1 and node 3. It can be seen from the result that the DTW value of node 2 and node 3 is the smallest. Therefore, node 3 can be found as the Sybil node.

[0067] Further, in an embodiment of the present application, the Sybil node is determined by similarity, including:

[0068] The dynamic time warping values DTW of the two power restored malicious node RSRP sequences and the RSRP sequences of the other two nodes whose identities are unknown are calculated respectively, and the Sybil node is the one with the smaller dynamic time warping value DTW.

[0069] The Sybil attack identification method of the intelligent connected vehicle of the embodiment of the application is aimed at the intelligent connected vehicle with power control, and performs Sybil attack detection according to RSRP, in particular, a power recovery RSRP calculation method is designed, so that the Svbil attack with power control is identified. For the RSRP sequence of the three identity unknown nodes containing malicious nodes, Sybil nodes and normal nodes, first, the Bemaola Galvn segmentation algorithm is used to find the mutation points of each sequence, and then the number of mutation points is used to determine the RSRP sequence of the malicious nodes. According to the found mutation points, the RSRP sequence of the malicious nodes is divided into multiple segments, and then the RSRP difference value average of each segment of the RSRP sequence of the malicious nodes and the other two sequences is calculated respectively, and added to the original RSRP sequence of the malicious nodes, to obtain the power recovery RSRP sequence of the malicious nodes. Finally, the similarity of the RSRP sequences between the nodes is calculated through dynamic time planning, and the Sybil node is determined by finding the node with the minimum dynamic time planning value of the RSRP sequence of the malicious nodes. The transmission of the intelligent connected vehicle data is more secure.

[0070] Figure 9 The structure diagram of the Sybil attack identification device of the intelligent connected vehicle provided by the embodiment of the application.

[0071] As shown in Figure 9 , the Sybil attack identification device of the intelligent connected vehicle comprises: an acquisition module 100, a finding module 200, a recovery module 300 and an identification module 400, wherein,

[0072] The acquisition module is used to acquire three identity unknown nodes including malicious nodes, Sybil nodes and normal nodes, and the reference signal receiving power (RSRP) sequence of the three identity unknown nodes;

[0073] The finding module is used to find the mutation points of the RSRP sequence of the three identity unknown nodes by using the Bernaola Galvn segmentation algorithm, and determine the RSRP sequence of the malicious nodes through the number of mutation points;

[0074] The recovery module is used to calculate the difference value average of the RSRP sequence of the malicious nodes and the RSRP sequence of the other two identity unknown nodes, add the difference value average to the RSRP sequence of the malicious nodes respectively, and obtain the power recovery RSRP sequence of the malicious nodes;

[0075] The identification module is used to calculate the similarity of the power recovery RSRP sequence of the malicious nodes and the RSRP sequence of the other two identity unknown nodes corresponding thereto respectively, and determine the Sybil node through the similarity.

[0076] Further, in one embodiment of the present application, the identification module is further configured to:

[0077] respectively calculate a dynamic time warping value (DTW) of a sequence of RSRP of the malicious node of the two power recovery paths and a sequence of RSRP of the other two nodes with unknown identities, and the Sybil node is the one with the smaller DTW value.

[0078] To achieve the above object, the third aspect of the present application provides a computer device, characterized in that comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the intelligent connected vehicle Sybil attack identification method as described above.

[0079] To achieve the above object, the fourth aspect of the present application provides a computer readable storage medium, characterized in that the computer readable storage medium stores a computer program, and the computer program is executable on a processor to implement the intelligent connected vehicle Sybil attack identification method as described above.

[0080] In the description of the present application, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example" or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present application, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the different embodiments or examples described in the present application and the features of the different embodiments or examples can be combined and combined by those skilled in the art without contradiction.

[0081] In addition, the terms "first", "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality of" is at least two, for example, two, three, etc., unless otherwise specifically limited.

[0082] Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and cannot be understood as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A Sybil attack identification method for intelligent connected vehicles, characterized in that, The method comprises the following steps: obtaining three identity-unknown nodes including a malicious node, a Sybil node and a normal node, and reference signal receiving power (RSRP) sequences of the three identity-unknown nodes; finding mutation points of the RSRP sequences of the three identity-unknown nodes by using a Bernaola Galvn segmentation algorithm, and determining the RSRP sequence of the malicious node by the number of the mutation points; respectively, and the difference value sequence is obtained respectively, and the difference value sequence is obtained the difference value at the same time, i.e. and record the difference value in the sequence, and obtain the difference value sequence ; According to the location of the mutation point, the RSRP sequence of the malicious node is determined The RSRP sequence of the malicious node is divided into several segments, and the segment average value of the difference between the RSRP of the malicious node and the RSRP sequence of the other node is calculated in units of segments, wherein the sequence numbers defining the start and end of the s-th segment are denoted as m and n respectively, and the RSRP difference average value of the segment is wherein k represents the k-th value in the difference sequence d; The difference value mean is added to the malicious node RSRP sequence respectively, to obtain two power restored malicious node RSRP sequences, wherein, in the original malicious node RSRP sequence , the RSRP difference value mean of the segment is added, that is, , wherein, , to obtain the power restored RSRP value; the power restoration is performed on each segment, to obtain the complete power restored RSRP sequence, that is, ; calculating the similarity of the two power-restored RSRP sequences of the malicious node and the RSRP sequences of the other two identity-unknown nodes corresponding to the two power-restored RSRP sequences of the malicious node respectively, and determining the Sybil node by the similarity.

2. The method of claim 1, wherein, The determining of the Sybil node by the similarity comprises: calculating the dynamic time warping (DTW) values of the two power-restored RSRP sequences of the malicious node and the RSRP sequences of the other two identity-unknown nodes corresponding to the two power-restored RSRP sequences of the malicious node respectively, and the Sybil node is the one with the smaller DTW value. 3.A Sybil attack identification device of an intelligent connected vehicle, characterized in that, The method comprises the following modules: an obtaining module, configured to obtain three identity-unknown nodes including a malicious node, a Sybil node and a normal node, and reference signal receiving power (RSRP) sequences of the three identity-unknown nodes; a finding module, configured to find mutation points of the RSRP sequences of the three identity-unknown nodes by using a Bernaola Galvn segmentation algorithm, and determine the RSRP sequence of the malicious node by the number of the mutation points; The recovery module is used to calculate the average difference between the RSRP sequence of the malicious node and the RSRP sequences of two other unknown nodes, including: calculating the RSRP sequence of the malicious node. With another node RSRP sequence The difference at the same time, i.e. The difference is then recorded in a sequence to obtain the difference sequence. Based on the location of the mutation point, the malicious node RSRP sequence The segment is divided into several segments, and the average value of the RSRP sequence difference between the malicious node and the other node is calculated for each segment. The start and end sequence numbers of the s-th segment are defined as m and n, respectively. The average RSRP difference for this segment is then calculated as follows: Where k represents the k-th value in the difference sequence d; the mean of the differences is added to the malicious node RSRP sequence to obtain two malicious node RSRP sequences with power recovery, wherein, in the original malicious node RSRP sequence Add the average RSRP difference of that segment to the above, i.e. ,in, This yields the RSRP value for power recovery; power recovery is then performed on each segment to obtain the complete RSRP sequence for power recovery. ; an identifying module, configured to calculate the similarity of the two power-restored RSRP sequences of the malicious node and the RSRP sequences of the other two identity-unknown nodes corresponding to the two power-restored RSRP sequences of the malicious node respectively, and determine the Sybil node by the similarity.

4. A computer device, comprising: The computer program is executed by the processor to implement the intelligent connected vehicle Sybil attack identification method according to any one of claims 1-2.

5. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the intelligent connected vehicle Sybil attack identification method according to any one of claims 1-2.

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