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Method and system for network attack detection at intersection of Internet of Vehicles and Internet of Things

A network attack and detection method technology, applied in network topology, transmission system, vehicle components, etc., can solve problems such as poor performance, and achieve the effect of simple operation, wide application range, and good security

Active Publication Date: 2021-10-08
TONGJI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the existing methods have the following shortcomings: These methods focus on the study of road traffic safety, not intersection traffic safety; these methods are only suitable for the CV permeability environment between small cells, and perform poorly for the environment under low permeability conditions; These methods are more used to prevent the occurrence of attacks, and there are fewer detection methods for attacks that have already occurred

Method used

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  • Method and system for network attack detection at intersection of Internet of Vehicles and Internet of Things
  • Method and system for network attack detection at intersection of Internet of Vehicles and Internet of Things
  • Method and system for network attack detection at intersection of Internet of Vehicles and Internet of Things

Examples

Experimental program
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Embodiment 1

[0064] A method for network attack detection at intersections of Internet of Vehicles and Internet of Vehicles, such as figure 1 ,Specifically:

[0065] Obtain the estimated SPaT information through the estimation step, collect the actual SPaT information, input the estimated SPaT information and the actual SPaT information into the trained long-term short-term memory network, and obtain the probability of network-connected intersections being attacked by the network. Both the estimated SPaT information and the actual SPaT information include The lane signal displays the initial moment, cycle duration and effective red light time of the time series;

[0066] Set several alarm levels, and each alarm level corresponds to a probability range. According to the probability obtained by the detection method, the corresponding level of alarm is issued to detect network attacks in time to ensure the security of networked information and the safety of networked vehicles.

[0067] like...

Embodiment 2

[0090] In this embodiment, the long-short-term memory network includes a first feedforward neural network layer, a first long-short-term memory network layer, a second long-short-term memory network layer, and a second feedforward neural network layer connected in sequence. Others are the same as in Example 1.

Embodiment 3

[0092] A network attack detection system corresponding to the embodiment 1, including a network connection information estimation module, a network connection information collection module and a network attack detection module:

[0093] The network information estimation module is used to obtain SPaT estimated information through the estimation step, the network information collection module is used to collect SPaT actual information and network vehicle trajectory data, and the network attack detection module is used to input SPaT estimated information and SPaT actual information into training The long-short-term memory network, and obtain the probability that the intersection of the network is attacked by the network;

[0094] Among them, the network information estimation module includes a period segmentation unit, a period breakpoint calculation unit and a red light time calculation unit, and the estimation steps are as follows:

[0095] The period segmentation unit divides...

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Abstract

The present invention relates to a method and system for network attack detection at intersections of Internet of Vehicles and Internet of Vehicles. The method specifically includes: acquiring SPaT estimation information through an estimation step, collecting SPaT actual information, and inputting the SPaT estimation information and SPaT actual information into the trained long-term and short-term Memory network, to obtain the probability of network-connected intersections being attacked by the network; the estimation steps are as follows: obtain the trajectory data of connected vehicles, segment the trajectory data of connected vehicles, and determine the first-end connected vehicles, terminal connected vehicles and cycle breakpoints Interval, the first connected car is the first connected car that enters the intersection in a cycle of the lane signal display time series, the last connected car is the previous car before the first connected car, and the cycle breakpoint interval is the end The time interval when the networked vehicle and the first networked vehicle behind the terminal networked vehicle pass through the stop line of the intersection successively. Compared with the prior art, the invention has the advantages of wide application range, strong timeliness, good safety and the like.

Description

technical field [0001] The invention relates to a vehicle networking and vehicle-road coordination technology, in particular to a network attack detection method and system for a vehicle network network intersection. Background technique [0002] The Internet of Vehicles system is mainly composed of three parts: Internet-connected vehicle CV, control platform CP and Internet-connected infrastructure. Netlink infrastructure includes roadside unit RSU and netlink signal CS. Networked signal CS refers to a signal light that has the function of traffic information transmission and is controlled by the connected vehicle system. [0003] The logic of information transmission in the Internet of Vehicles system is as follows: image 3 As shown, at a networked intersection in a typical V2I system, signal phase and timing SPaT information is broadcast from CS to CV to help CV make decisions. Each CV is equipped with an on-board unit (OBU). OBU broadcasts the basic security informat...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L29/06H04L29/08H04W4/44H04W4/46H04W84/18
CPCH04L63/1416H04L63/145H04L67/12H04W84/18H04W4/44H04W4/46
Inventor 胡笳祁隆骞张子晗王浩然
Owner TONGJI UNIV
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