CN109361678B - False data injection attack detection method for intelligent networked automobile automatic cruise system - Google Patents
False data injection attack detection method for intelligent networked automobile automatic cruise system Download PDFInfo
- Publication number
- CN109361678B CN109361678B CN201811306313.4A CN201811306313A CN109361678B CN 109361678 B CN109361678 B CN 109361678B CN 201811306313 A CN201811306313 A CN 201811306313A CN 109361678 B CN109361678 B CN 109361678B
- Authority
- CN
- China
- Prior art keywords
- cruise system
- automatic cruise
- value
- intelligent networked
- networked automobile
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
- H04L63/1416—Event detection, e.g. attack signature detection
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1441—Countermeasures against malicious traffic
- H04L63/1466—Active attacks involving interception, injection, modification, spoofing of data unit addresses, e.g. hijacking, packet injection or TCP sequence number attacks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
Abstract
A false data injection attack detection method of an intelligent networked automobile automatic cruise system aims at the problem of false data injection attack existing in the network transmission process of sensor measurement data of the intelligent networked automobile automatic cruise system. According to the method, the state filtering value of the intelligent networked automobile automatic cruise system is calculated in real time according to newly acquired observation data, the observation result is processed in real time on line, and the attack accident of the state data of the intelligent networked automobile automatic cruise system can be quickly detected.
Description
Technical Field
The invention belongs to the field of intelligent networked automobile automatic cruise control, and relates to a false data injection attack detection method for an intelligent networked automobile automatic cruise system.
Background
With the development of science and technology, automobiles are gradually intelligentized, not only are advanced sensors, controllers and other devices added to detect the real-time states of the automobiles, but also the automobiles can be connected with a network, the real-time states are reported to server equipment for managing the automobiles to be analyzed, and vehicle control instructions sent by execution server equipment are received, so that the car networking technology can be realized. The Internet of vehicles is a specific application of the Internet of things in the field of automobile industry, real-time communication among vehicles and between the vehicles and a data platform is realized by vehicle-mounted Internet equipment through the transmission technologies such as a wireless network and radio, the running information of the vehicles, the road conditions around the vehicles and other dynamic and static information are summarized, the information is transmitted to an information center through the wireless network to be processed, screened and calculated, and then the information is used for providing effective comprehensive network services such as guidance, road conditions, information sharing and multimedia for the vehicles so as to meet different functional requirements of the vehicles. The Internet of vehicles brings convenience to drivers and traffic management, and allows hackers to take advantage of the Internet of vehicles. Once a hacker invades, the automobile can be remotely and freely controlled, and personal privacy information stored in the automobile can be more possibly acquired. One exemplary network attack is a false data injection attack of an intelligent networked automobile automatic cruise system, wherein the attack falsifies data detected by a sensor and adds the data to the networked automobile automatic cruise system. Such attacks often disturb the guidance of the vehicle by the computer, for example causing false acceleration or deceleration of the vehicle during cruising. Specifically, when two vehicles cruise, the computer obtains a larger distance between the two vehicles by interfering speed measurement sensors such as radars or adding false data to the sensor data, so that the computer performs wrong guidance on the automatic cruise vehicle (namely, the rear vehicle accelerates to catch up with the front vehicle), and finally the two vehicles collide with each other; on the contrary, when two vehicles approach and the rear vehicle needs to brake, a hacker artificially interferes the sensor data by making false data attack on the automatic cruise system, so that the computer mistakenly recognizes that the two vehicles are still in the normal vehicle distance, and therefore, no braking measure is taken, and finally, rear-end collision accidents of the two vehicles occur.
Disclosure of Invention
The invention provides a Kalman filtering-based false data injection attack detection method for an intelligent networked automobile automatic cruise system, aiming at solving the problem of false data injection attack detection of the intelligent networked automobile automatic cruise system. The method can accurately detect the problem of false data injection attack of the intelligent networked automobile automatic cruise system only by adopting the measured value at the current moment and the estimated value at the previous moment, reduces the data storage space, lightens the detection calculation amount at each step, has clear calculation steps, and is very suitable for the processing of an on-board computer.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a false data injection attack detection method for an intelligent networked automobile automatic cruise system comprises the following steps:
1) considering a three-order discrete time state space model of an intelligent networked automobile automatic cruise system, see formula (1):
x(k+1)＝Ax(k)+Bu(k)+w(k) (1)
where k denotes time, x ═ x1,x2,x3]TIs a column vector of three state variables, x1Is the safety spacing error in meters (m), x2Is the relative speed of two vehicles, unit meter per second (m/s), x3Is the acceleration of the cruising vehicle in units of meters per square second (m/s)2) T represents the transpose of the vector, u is the cruise vehicle control quantity, in meters per square second (m/s)2) W is white Gaussian noise with a mean value of zero and a covariance of Q, and the matrices A and B are respectively
Wherein, TsIs the sampling time, unit second(s), ξ is the kinetic parameter of the drive, unit second(s);
2) defining an auto-cruise system controller u (k) ═ lx (k), wherein a row vector L is a controller gain, substituting into a model formula (1), and obtaining an intelligent networked auto-cruise system closed-loop model, see formula (2):
wherein the matrix
3) And acquiring a cruise system state variable measured value by using a vehicle-mounted sensor, and referring to formula (3):
z(k)＝Hx(k)+v(k) (3)
wherein v represents the measurement noise of the sensor with the mean value of zero covariance of R, H is the third-order observation matrix of the sensor, and the column vector z ═ x1,x2,x3]TFor noisy measurements, T denotes vector transpose;
4) and (3) iteratively calculating the filter value of the state variable at the current k moment according to a Kalman filter formula by considering the formulas (2) and (3), and referring to the formula (4):
wherein the content of the first and second substances,
is the filtered value of the state variable at time k,
is a predicted value of the state variable at time K-1, KkIs the filter gain at the current time k, see equation (5):
Kk＝Pk|k-1HT(HPk|k-1HT+R)-1 (5)
wherein, Pk/k-1Representing the prior state estimation error covariance matrix, Pk/kThe a posteriori state estimation error covariance matrix is represented, see equations (6) and (7):
Pk|k＝(I3-KkH)Pk|k-1 (7)
wherein, I3To representA 3-order identity matrix, T representing the transpose of the matrix;
5) and calculating a residual error between the sensor measurement value and the Kalman filter value, see formula (8):
defining a target detection function of the false data injection attack of the intelligent networked automobile automatic cruise system by using a residual signal r (k), and referring to an equation (9):
f(k)＝r(k)Td(k)r(k) (9)
wherein d (k) is a covariance matrix of the residual signal r (k), see formula (10):
d(k)＝(H-HKkH)Pk|k-1(H-HKkH)T+(I3-HKk)R(I3-HKk)T (10)
wherein, the matrix I3Is a 3-order identity matrix, and T represents the transposition of the matrix;
6) setting a false data injection attack detection threshold value of the intelligent networked automobile automatic cruise system as the sum of the standard deviations of the sensors measured by the three state variables, recording the sum as sigma, and if the objective function value f (k) is greater than sigma, giving an alarm if false data injection attack exists in the automatic cruise system; otherwise, the automatic cruise system is not attacked by the false data injection, and returns to the loop detection of the step 3) by making k equal to k + 1.
The technical conception of the invention is as follows: aiming at the problem of false data injection attack in the network transmission process of the sensor measurement data of the intelligent networked automobile automatic cruise system, firstly, a discrete time state space model of the networked automobile automatic cruise system is established, a filter value of the measurement data at each moment is calculated according to a Kalman filtering formula, then the sum of the noise standard deviations of the measurement sensors of the automatic cruise system is taken as a detection threshold, if no data injection attack exists, the Kalman filter value gradually tends to a true value, and therefore, after the error between the Kalman filter value and the measurement value exceeds a given threshold, the false data injection attack of the intelligent networked automobile automatic cruise system is detected.
The main execution part of the invention is implemented by running on an intelligent networked automobile automatic cruise control computer. The application process of the method can be roughly divided into 2 stages:
1. setting parameters: the method comprises the following steps of model parameters and attack detection parameters: in the model import interface, the constant T in the model expressions (1) to (3) is inputsξ, H, L, Q, and R, where the matrix L satisfies the matrix in model (2)
Has a negative real part; inputting a threshold value sigma in an attack detection parameter setting interface>0、P0And
after the input parameters are confirmed, the control computer sends the setting data into a computer storage unit RAM for storage;
2. and (3) online operation: clicking a 'operation' button on a configuration interface, starting a CPU (central processing unit) of an intelligent networked automobile automatic cruise control computer to read model parameters and attack detection parameters of the intelligent networked automobile automatic cruise system, executing a 'false data injection attack detection program of the intelligent networked automobile automatic cruise system', calculating a residual error between a sensor measurement value and a Kalman filter value and a target detection function value in real time by measuring a measurement value of a state variable of the automatic cruise system on line and calculating a filter value of the state variable of the automatic cruise system on line, comparing the target detection function value with a threshold value, detecting that false data injection attack exists in the automatic cruise system if the target function value is greater than the threshold value, and detecting the automatic cruise system without the false data injection attack until the next moment by recycling if the target function value is less than or equal to the threshold value.
The invention has the beneficial effects that: according to the method, a large amount of observation data is not required to be stored during solving, the state filtering value of the intelligent networked automobile automatic cruise system is calculated in real time according to newly acquired observation data, the observation result is processed in real time on line, and whether the state data of the intelligent networked automobile automatic cruise system is attacked or not can be detected quickly, so that traffic accidents caused by detection delay are prevented.
Drawings
FIG. 1 is a schematic diagram of a data injection attack process of an intelligent networked automobile automatic cruise system;
FIG. 2 is a flow chart of false data injection attack detection of an intelligent networked automobile auto-cruise system;
fig. 3 is a distribution of detection function values after the intelligent networked automobile automatic cruise system is attacked, wherein a solid line is a detection function value curve after the false data injection attack of the intelligent networked automobile automatic cruise system, and a dotted line is a threshold value of the false data injection attack detection of the intelligent networked automobile automatic cruise system.
Detailed Description
The method of the present invention is described in further detail below with reference to the accompanying drawings.
Referring to fig. 1 to 3, a method for detecting false data injection attack of an intelligent networked automobile automatic cruise system, the method comprising the following steps:
1) considering a three-order discrete time state space model of an intelligent networked automobile automatic cruise system, see formula (1):
x(k+1)＝Ax(k)+Bu(k)+w(k) (1)
where k denotes time, x ═ x1,x2,x3]TIs a column vector of three state variables, x1Is the safety spacing error in meters (m), x2Is the relative speed of two vehicles, unit meter per second (m/s), x3Is the acceleration of the cruising vehicle in units of meters per square second (m/s)2) T represents the transpose of the vector, u is the cruise vehicle control quantity, in meters per square second (m/s)2) W is white Gaussian noise with a mean value of zero and a covariance of Q, and the matrices A and B are respectively
Wherein, TsIs the sampling time, and unit second(s), ξ is the kinetic parameter of the drive, unit second(s) ((S))s)；
2) Defining an auto-cruise system controller u (k) ═ lx (k), wherein a row vector L is a controller gain, substituting into a model formula (1), and obtaining an intelligent networked auto-cruise system closed-loop model, see formula (2):
wherein the matrix
3) And acquiring a cruise system state variable measured value by using a vehicle-mounted sensor, and referring to formula (3):
z(k)＝Hx(k)+v(k) (3)
wherein v represents the measurement noise of the sensor with the mean value of zero covariance of R, H is the third-order observation matrix of the sensor, and the column vector z ═ x1,x2,x3]TFor noisy measurements, T denotes vector transpose;
4) and (3) iteratively calculating the filter value of the state variable at the current k moment according to a Kalman filter formula by considering the formulas (2) and (3), and referring to the formula (4):
wherein the content of the first and second substances,
is the filtered value of the state variable at time k,
is the filtered value of the state variable at time K-1, KkIs the filter gain at the current time k, see equation (5):
Kk＝Pk|k-1HT(HPk|k-1HT+R)-1 (5)
wherein, Pk/k-1Representing a priori statesState estimation error covariance matrix, Pk/kThe a posteriori state estimation error covariance matrix is represented, see equations (6) and (7):
Pk|k＝(I3-KkH)Pk|k-1 (7)
wherein, I3Represents a 3-order identity matrix, and T represents the transposition of the matrix;
5) and calculating a residual error between the sensor measurement value and the Kalman filter value, see formula (8):
defining a target detection function of the false data injection attack of the intelligent networked automobile automatic cruise system by using a residual signal r (k), and referring to an equation (9):
f(k)＝r(k)Td(k)r(k) (9)
wherein d (k) is a covariance matrix of the residual signal r (k), see formula (10):
d(k)＝(H-HKkH)Pk|k-1(H-HKkH)T+(I3-HKk)R(I3-HKk)T (10)
wherein, the matrix I3Is a 3-order identity matrix, and T represents the transposition of the matrix;
6) setting a false data injection attack detection threshold value of the intelligent networked automobile automatic cruise system as the sum of the standard deviations of the sensors measured by the three state variables, recording the sum as sigma, and if the objective function value f (k) is greater than sigma, giving an alarm if false data injection attack exists in the automatic cruise system; otherwise, the automatic cruise system is not attacked by the false data injection, and returns to the loop detection of the step 3) by making k equal to k + 1.
The embodiment is a false data injection attack detection process of an intelligent networked automobile automatic cruise system, and the method specifically comprises the following operations:
1. in a parameter setting interface, inputting a constant value T of a false data injection attack detection process of an intelligent networked automobile automatic cruise systems0.05 second, 0.25 second, L0.2293, 0.8056,0.2825]Q is diagonal matrix diag {0.5,0.001,0.001}, R is 0.16I3And H ═ I3Wherein, I3Is a third-order identity matrix; input attack detection parameter σ 1.2, P0＝I3And
2. and (3) online operation: clicking a 'operation' button on a configuration interface, starting a CPU (central processing unit) of an intelligent networked automobile automatic cruise control computer to read model parameters and attack detection parameters of the intelligent networked automobile automatic cruise system, executing a 'false data injection attack detection program of the intelligent networked automobile automatic cruise system', calculating a residual error between a sensor measurement value and a Kalman filter value and a target detection function value in real time by measuring a measurement value of a state variable of the automatic cruise system on line and calculating a filter value of the state variable of the automatic cruise system on line, comparing the target detection function value with a threshold value, detecting that false data injection attack exists in the automatic cruise system if the target function value is greater than the threshold value, and detecting the automatic cruise system without the false data injection attack until the next moment by recycling if the target function value is less than or equal to the threshold value.
As shown in fig. 1, data of a sensor of the auto cruise system of the intelligent networked automobile may be attacked during transmission, so that wrong state data of the cruise system is sent to the auto cruise controller, the auto cruise controller of the intelligent networked automobile makes a wrong control instruction, and an accident can be effectively prevented after the vehicle state estimation and attack detection of the auto cruise system of the intelligent networked automobile are added.
As shown in FIG. 2, the model parameter T of the intelligent networked automobile automatic cruise system is givensXi, automatic cruise controller gain L, observation matrix H, system noise covariance matrix Q and measurement noise covariance matrix R, initializing attack detection parameter P0And
and a detection threshold σ; and then carrying out loop iteration detection: measuring a measured value of a state variable of the automatic cruise system on line, calculating a filter value of the state variable of the automatic cruise system on line, calculating a residual error between a measured value of a sensor and a Kalman filter value and a target detection function value in real time, comparing the target detection function value with a threshold value, detecting that false data injection attack exists in the automatic cruise system if the target function value is greater than the threshold value, and detecting the automatic cruise system without the false data injection attack at the next moment if the target function value is less than or equal to the threshold value.
As shown in fig. 3, a distribution diagram of detection function values after the intelligent networked automobile automatic cruise system is attacked, wherein a solid line is a detection function value curve after the intelligent networked automobile automatic cruise system false data is injected into the attack, and a dotted line is a threshold value of detection of the intelligent networked automobile automatic cruise system false data injection attack; according to the given model parameters and attack detection parameters of the intelligent networked automobile automatic cruise system, the cruise state filtering value at each moment is used for calculating a target detection function value, the intelligent networked automobile automatic cruise system is attacked by normal distribution false data with the average value of zero variance of 3 when running between 50 th and 100 th moments, the value of the calculated target detection function is far more than a threshold value, the fact that false data injection attack exists in the intelligent networked automobile automatic cruise system is indicated, and on the contrary, the target detection function value at other moments is smaller than the threshold value, the fact that false data injection attack does not exist in the intelligent networked automobile automatic cruise system is indicated, and therefore the method meets the requirements of rapid and accurate detection of the false data injection attack of the intelligent networked automobile automatic cruise system.
The invention provides an embodiment of false data injection attack detection for an intelligent networked automobile automatic cruise system. It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that any modifications made within the spirit of the invention and the scope of the appended claims fall within the scope of the invention.
Claims (1)
1. A false data injection attack detection method for an intelligent networked automobile automatic cruise system is characterized by comprising the following steps:
1) considering a three-order discrete time state space model of an intelligent networked automobile automatic cruise system, see formula (1):
x(k+1)＝Ax(k)+Bu(k)+w(k) (1)
where k denotes time, x ═ x1,x2,x3]TIs a column vector of three state variables, x1Is the safety spacing error, in meters, x2Is the relative speed of two vehicles, unit meter/second, x3Is the acceleration of the cruising vehicle in unit meter/square second, T represents the transposition of the vector, u is the control quantity of the cruising vehicle in unit meter/square second, w is white Gaussian noise with zero mean value and Q covariance, and the matrixes A and B are respectively
Wherein, TsIs the sampling time, unit second, ξ is the kinetic parameter of the driver, unit second;
2) defining an auto-cruise system controller u (k) ═ lx (k), wherein a row vector L is a controller gain, substituting into a model formula (1), and obtaining an intelligent networked auto-cruise system closed-loop model, see formula (2):
wherein the matrix
3) And acquiring a cruise system state variable measured value by using a vehicle-mounted sensor, and referring to formula (3):
z(k)＝Hx(k)+v(k) (3)
wherein v represents the sensor measurement noise with zero mean value and R covariance, H is the third-order observation matrix of the sensor, and the column vector z is [ z ═ z [1,z2,z3]TFor noisy measurements, T denotes vector transpose;
4) and (3) iteratively calculating the filter value of the state variable at the current k moment according to a Kalman filter formula by considering the formulas (2) and (3), and referring to the formula (4):
wherein the content of the first and second substances,
is the filtered value of the state variable at time k,
is a predicted value of the state variable at time K-1, KkIs the filter gain at the current time k, see equation (5):
Kk＝Pk|k-1HT(HPk|k-1HT+R)-1 (5)
wherein, Pk/k-1Representing the prior state estimation error covariance matrix, Pk/kThe a posteriori state estimation error covariance matrix is represented, see equations (6) and (7):
Pk|k＝(I3-KkH)Pk|k-1 (7)
wherein, I3Represents a 3-order identity matrix, and T represents the transposition of the matrix;
5) and calculating a residual error between the sensor measurement value and the Kalman filter value, see formula (8):
defining a target detection function of the false data injection attack of the intelligent networked automobile automatic cruise system by using a residual signal r (k), and referring to an equation (9):
f(k)＝r(k)Td(k)r(k) (9)
wherein d (k) is a covariance matrix of the residual signal r (k), see formula (10):
d(k)＝(H-HKkH)Pk|k-1(H-HKkH)T+(I3-HKk)R(I3-HKk)T (10)
wherein, the matrix I3Is a 3-order identity matrix, and T represents the transposition of the matrix;
6) setting a false data injection attack detection threshold value of the intelligent networked automobile automatic cruise system as the sum of the standard deviations of the sensors measured by the three state variables, recording the sum as sigma, and if the objective function value f (k) is greater than sigma, giving an alarm if false data injection attack exists in the automatic cruise system; otherwise, the automatic cruise system is not attacked by the false data injection, and returns to the loop detection of the step 3) by making k equal to k + 1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811306313.4A CN109361678B (en) | 2018-11-05 | 2018-11-05 | False data injection attack detection method for intelligent networked automobile automatic cruise system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811306313.4A CN109361678B (en) | 2018-11-05 | 2018-11-05 | False data injection attack detection method for intelligent networked automobile automatic cruise system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109361678A CN109361678A (en) | 2019-02-19 |
| CN109361678B true CN109361678B (en) | 2021-10-12 |
Family
ID=65344227
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811306313.4A Active CN109361678B (en) | 2018-11-05 | 2018-11-05 | False data injection attack detection method for intelligent networked automobile automatic cruise system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109361678B (en) |
Families Citing this family (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109978025B (en) * | 2019-03-11 | 2022-03-01 | 浙江工业大学 | Intelligent internet vehicle front vehicle acceleration prediction method based on Gaussian process regression |
| CN110049057B (en) * | 2019-04-25 | 2021-04-27 | 东南大学 | Sensor network event trigger information fusion method under false data injection attack |
| CN110996300A (en) * | 2019-11-12 | 2020-04-10 | 北京航空航天大学 | Vehicle-mounted terminal information safety risk control method based on traffic scene safety |
| CN111079271B (en) * | 2019-12-02 | 2024-03-22 | 浙江工业大学 | Industrial information physical system attack detection method based on system residual fingerprint |
| CN112600814B (en) * | 2020-12-08 | 2022-06-14 | 震兑工业智能科技有限公司 | Detection method and system for deception attack of ship CPS (cyber-physical system) |
| CN113268730B (en) * | 2021-05-01 | 2023-07-25 | 群智未来人工智能科技研究院(无锡)有限公司 | Smart power grid false data injection attack detection method based on reinforcement learning |
| CN113765880B (en) * | 2021-07-01 | 2022-05-20 | 电子科技大学 | Power system network attack detection method based on space-time correlation |
| CN113589085B (en) * | 2021-08-04 | 2023-04-07 | 南京信息工程大学 | Detection and mitigation method for false data injection attack of generator frequency modulation system |
| CN114459473B (en) * | 2022-02-16 | 2023-11-21 | 北方工业大学 | Inertial/satellite/polarization integrated navigation method under error data injection attack |
| CN114666100B (en) * | 2022-03-02 | 2023-03-24 | 南京航空航天大学 | Intelligent vehicle network attack security detection system and method |
| CN114996706B (en) * | 2022-06-22 | 2023-04-04 | 燕山大学 | Intelligent traffic false data attack detection method based on unknown input observer |
| CN115134162B (en) * | 2022-07-15 | 2023-05-05 | 西南民族大学 | Method for detecting and compensating malicious threat of industrial control system and electronic equipment |
| CN117579309A (en) * | 2023-10-31 | 2024-02-20 | 海南大学 | State feedback safety synchronous control method of network-connected vehicle system |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108196448A (en) * | 2017-12-25 | 2018-06-22 | 北京理工大学 | False data injection attacks method based on inaccurate mathematical model |
-
2018
- 2018-11-05 CN CN201811306313.4A patent/CN109361678B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108196448A (en) * | 2017-12-25 | 2018-06-22 | 北京理工大学 | False data injection attacks method based on inaccurate mathematical model |
Non-Patent Citations (2)
| Title |
|---|
| Fasle Data Injection Attacks in Cyber-Physical Systems Based on Inaccurate Model;Fangyuan Hou等;《IECON 2017-43rd Annual Conference of the IEEE Industrial Electronics Society》;20171101;第5791-5795页 * |
| Secure estimation based Kalman Filter for cyber-physical systems against sensor attacks;Young Hwan Chang等;《Automatica》;20180618;第399-412页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109361678A (en) | 2019-02-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109361678B (en) | False data injection attack detection method for intelligent networked automobile automatic cruise system | |
| Åsljung et al. | Using extreme value theory for vehicle level safety validation and implications for autonomous vehicles | |
| US10890907B2 (en) | Vehicle component modification based on vehicular accident reconstruction data | |
| CN107884757B (en) | Constant false alarm target detection method and device and vehicle | |
| CN111580494B (en) | Self-adaptive calibration method and system for control parameters of parallel driving vehicle | |
| CN110329249B (en) | Automobile forward collision early warning control system and method of cyclic neural network | |
| CN110155081B (en) | Self-adaptive obstacle avoidance control system for intelligent driving automobile | |
| Wielitzka et al. | Joint unscented Kalman filter for state and parameter estimation in vehicle dynamics | |
| US11130455B2 (en) | Vehicle security enhancement | |
| US11423162B2 (en) | Systems and methods for message assurance in vehicle systems | |
| DE102015202628A1 (en) | VEHICLE-MOUNTED APPARATUS FOR SELECTING PRECEDING VEHICLE POSITIONED IN THE TRAVEL PATH OF THE HOST VEHICLE OF THE APPARATUS | |
| CN113933858A (en) | Abnormal detection method and device of positioning sensor and terminal equipment | |
| CN114629698A (en) | Automatic driving network attack detection system and method based on vehicle state estimation | |
| CN110889862A (en) | Combined measurement method for multi-target tracking in network transmission attack environment | |
| US20210370891A1 (en) | Information processing device, non-transitory computer-readable storage medium, and information processing method | |
| CN112319505B (en) | Automatic driving lane change judgment method, device, equipment and storage medium | |
| US11893004B2 (en) | Anomaly detection in multidimensional sensor data | |
| CN110861678B (en) | Train wheel diameter checking method and device | |
| CN114792478B (en) | Intelligent network-connected automobile formation control method based on event triggering | |
| CN107719373B (en) | Method and system for estimating road friction | |
| CN108292125B (en) | Method and device for influencing vehicle characteristics | |
| CN116476843A (en) | Target slip estimation | |
| US11555919B2 (en) | Radar calibration system | |
| WO2023035234A1 (en) | Vehicle state parameter estimation method and apparatus | |
| Hong et al. | An interacting multiple-model-based algorithm for driver behavior characterization using handling risk |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |