APPARATUS AND METHOD FOR DELIBERATE ATTACK SIMULATION TO EVALUATE THE PERFORMANCE OF ToF LiDAR SENSOR

Abstract

The present invention relates to a device for simulating an intentional attack for evaluating the performance of a ToF (Time-of-Flight) lidar sensor, comprising: a sensor fixing part for fixing a ToF lidar sensor; a light source control part equipped with a rotating stage to which an attack light source is fixed and which controls the output and operation method of the attack light source; a sensor monitoring part for monitoring the signal output of the ToF lidar sensor; and a processor for controlling the overall operation of the sensor fixing part, the light source control part, and the sensor monitoring part, wherein the processor is characterized by adjusting the alignment state and output characteristics of the attack light source for the ToF lidar sensor in conjunction with the light source control part and the alignment control part.

Description

Technology Field

[0001] The present invention relates to an apparatus and method for simulating an intentional attack to evaluate the performance of a Time-of-Flight (ToF) lidar sensor, and in particular, to an apparatus and method for simulating an intentional attack to evaluate the performance of a ToF lidar sensor that enables the quantitative evaluation of physical and signal-based attacks targeting the ToF lidar sensor. Background Technology

[0003] Time-of-Flight (ToF) LiDAR sensors are sensors that estimate distance information by emitting light pulses and measuring the time it takes for them to reflect back. This method enables high-speed distance measurement and has the advantage of being relatively inexpensive and implementable with a simple hardware configuration. Thanks to these characteristics, ToF LiDAR sensors are widely used in various application fields, such as autonomous vehicles, drones, robots, security surveillance systems, and 3D mapping.

[0004] However, ToF LiDAR sensors are potentially vulnerable to strong light source interference and signal manipulation, and there is a problem where distance measurements may become inaccurate in certain environments. In particular, various attack techniques capable of causing ToF LiDAR sensors to malfunction are currently being researched by malicious attackers.

[0005] For example, attack techniques that can cause malfunctions in ToF LiDAR sensors include jamming attacks (an attack that interferes with the sensor's ability to detect normal reflected signals by emitting strong light), spoofing attacks (an attack that artificially generates a signal similar to one reflected from an actual object to induce the sensor to measure distorted distance data), blinding attacks (an attack that disables the sensor by directly shining strong light on the sensor's light receiver), and replay attacks (an attack that re-emits a previously captured signal to induce the sensor to detect the same signal as the original data).

[0006] Such attacks pose a risk of degrading the reliability of ToF LiDAR sensors in various applications, including autonomous vehicles, security surveillance systems, and unmanned aerial vehicles. In particular, for autonomous vehicles, if an attack causes obstacles to be failed to be detected or distorted distance information to be provided, there is a very high likelihood of it leading to safety accidents.

[0007] However, current robustness verification methods for ToF LiDAR sensors have limitations, including a lack of a quantitative evaluation framework, difficulty in repeatability, and an inability to reflect various attack scenarios. Existing studies have largely been limited to verifying sensor operation in specific environments, and systematic methods for reproducing and evaluating attacks have not yet been established.

[0008] Therefore, in order to quantitatively evaluate the performance of ToF LiDAR sensors and verify their robustness by reproducing various attack scenarios, there is a need for a device capable of systematically simulating and evaluating various attacks on ToF LiDAR sensors.

[0009] The background technology of the present invention is disclosed in Korean Patent No. 10-2594663 (registered on October 23, 2023). The problem to be solved

[0011] According to one aspect of the present invention, the present invention is created to solve the above-mentioned problems and aims to provide an apparatus and method for simulating an intentional attack for evaluating the performance of a ToF LiDAR sensor, which enables the ToF LiDAR sensor to quantitatively evaluate its robustness against malicious physical attacks (e.g., optical interference, signal modulation, etc.).

[0012] According to another aspect, the present invention aims to provide an apparatus and method for simulating an intentional attack for evaluating the performance of a ToF LiDAR sensor, which enables the construction of a repeatable environment by precisely controlling the output and operation method of an attack light source.

[0013] According to another aspect, the present invention aims to provide an apparatus and method for simulating an intentional attack for evaluating the performance of a ToF LiDAR sensor, which analyzes the output data of the sensor to quantitatively measure the impact of the attack and enables a highly reliable performance evaluation based thereon. means of solving the problem

[0015] A device for simulating an intentional attack for evaluating the performance of a ToF (Time-of-Flight) lidar sensor according to one aspect of the present invention comprises: a sensor fixing part for fixing a ToF lidar sensor; a light source control part equipped with a rotating stage to which an attack light source is fixed and controlling the output and operation method of the attack light source; a sensor monitoring part for monitoring the signal output of the ToF lidar sensor; and a processor that controls the overall operation of the sensor fixing part, the light source control part, and the sensor monitoring part, and adjusts the alignment state and output characteristics of the attack light source for the ToF lidar sensor in conjunction with the light source control part and the alignment control part, wherein the processor generates a performance evaluation report of the ToF lidar sensor by comparing data before and after the attack is performed based on the analysis results of the sensor monitoring part.

[0016] In the present invention, the processor is characterized by operating in conjunction with the light source control unit to set the output intensity, frequency, and modulation method of the attack light source before performing an attack on the ToF LiDAR sensor, selecting a Pulse Wave or Continuous Wave mode according to the characteristics of the ToF LiDAR sensor, and adjusting the rotation angle of the attack light source through the rotation of the rotation stage to perform an attack on the ToF LiDAR sensor from various angles.

[0017] In the present invention, the alignment control unit adjusts the relative position and angle of the attack light source and the ToF lidar sensor so that the attack light source is aligned within the detection range of the ToF lidar sensor, and the processor stores the alignment state of the attack light source and the ToF lidar sensor to maintain test conditions for repeated reproduction.

[0018] In the present invention, the light source control unit is characterized by adjusting the frequency band of the attack light source in accordance with the signal processing characteristics of the ToF lidar sensor so that the ToF lidar sensor can perform an attack in a frequency range where it is vulnerable.

[0019] In the present invention, the sensor monitoring unit collects point cloud data output from the ToF lidar sensor, analyzes changes before and after the execution of an attack, determines whether a jamming attack has occurred based on whether a distance value is not detected or whether a distance value is returned infinitely, and determines whether a spoofing attack has occurred by analyzing the difference between the distance value detected by the ToF lidar sensor and the actual distance value.

[0020] In the present invention, the sensor fixing part is designed to allow the ToF lidar sensor to be evaluated to be positioned at various heights and angles, and is characterized by increasing the repeatability of the test by adjusting the mounting environment of the ToF lidar sensor to a specified height and angle.

[0021] In the present invention, the attack light source is formed to include an attack confirmation light source and a LiDAR attack light source, wherein the attack confirmation light source is a light source that emits light in the visible light band to enable visual confirmation of the location of the attack light source, and the LiDAR attack light source is characterized by including a laser light source in the infrared band that performs an actual attack affecting the ToF LiDAR sensor.

[0023] A method for simulating an intentional attack for evaluating the performance of a ToF LiDAR sensor according to another aspect of the present invention comprises: a step in which a processor of an intentional attack simulation device controls the output and operation method of an attack light source through a light source control unit, wherein the ToF LiDAR sensor is fixed to a sensor fixing unit and the attack light source is fixed to a light source fixing unit in the intentional attack simulation device; a step in which the processor, in conjunction with the light source control unit and the alignment control unit, adjusts the rotation angle, alignment state, and output characteristics of the attack light source for the ToF LiDAR sensor through the rotation stage; and a step in which the processor monitors the signal output of the ToF LiDAR sensor through a sensor monitoring unit; wherein, in the step of controlling the output and operation method of the attack light source, the processor, in conjunction with the light source control unit, sets the output intensity, frequency, and modulation method of the attack light source before performing an attack on the ToF LiDAR sensor, and selects a Pulse Wave or Continuous Wave mode according to the characteristics of the ToF LiDAR sensor.

[0024] In the present invention, in the step of adjusting the alignment state and output characteristics of the attack light source, the alignment control unit adjusts the relative position and angle of the attack light source and the ToF lidar sensor so that the attack light source is aligned within the detection range of the ToF lidar sensor, and the processor stores the alignment state of the attack light source and the ToF lidar sensor to maintain test conditions for repeated reproduction.

[0025] In the present invention, in the step of controlling the output and operation method of the attack light source, the light source control unit adjusts the frequency band of the attack light source in accordance with the signal processing characteristics of the ToF lidar sensor so that the ToF lidar sensor can perform an attack in a frequency range where it is vulnerable.

[0026] In the present invention, in the step of monitoring the signal output of the ToF LiDAR sensor, the sensor monitoring unit collects point cloud data output from the ToF LiDAR sensor and analyzes changes before and after the execution of an attack, determines whether a jamming attack is occurring based on whether a distance value is not detected or whether the distance value returns infinity, and determines whether a spoofing attack is occurring by analyzing the difference between the distance value detected by the ToF LiDAR sensor and the actual distance value.

[0027] In the present invention, in the step of adjusting the alignment state and output characteristics of the attack light source, the processor adjusts the rotation angle of the attack light source through the rotation of the rotation stage of the light source fixing part to which the attack light source is fixed, thereby performing an attack on the ToF LiDAR sensor at various angles, and also generates a performance evaluation report of the ToF LiDAR sensor by comparing data before and after the attack based on the analysis results of the sensor monitoring part. Effects of the invention

[0029] According to one aspect, the present invention enables the evaluation of the robustness of a sensor by precisely simulating a malicious physical attack on a ToF lidar sensor.

[0030] According to one aspect, the present invention enables the establishment of a repeatable performance evaluation environment by controlling the output and operation method of an attack light source.

[0031] According to one aspect, the present invention enables quantitative evaluation of the impact of attacks such as jamming and spoofing by analyzing output data of a sensor.

[0032] According to one aspect, the present invention enables verification of performance in various environments by applying attack scenarios tailored to the signal characteristics of each LiDAR sensor.

[0033] According to one aspect, the present invention improves the reliability of LiDAR sensors used in autonomous driving and security systems, thereby enhancing safety. Brief explanation of the drawing

[0035] FIG. 1 is an exemplary diagram showing the schematic configuration of a device for simulating an intentional attack for evaluating the performance of a ToF lidar sensor according to one embodiment of the present invention. FIG. 2 is a flowchart illustrating a control method for a deliberate attack simulation device for evaluating the performance of a ToF lidar sensor according to an embodiment of the present invention. FIG. 3 is an exemplary diagram schematically showing the structure of a sensor fixing part that fixes a lidar sensor to be evaluated in a device for simulating an intentional attack for evaluating the performance of a ToF lidar sensor according to one embodiment of the present invention. FIG. 4 is an exemplary diagram schematically showing the mounting structure of a lidar sensor to be evaluated in a device for simulating an intentional attack for evaluating the performance of a ToF lidar sensor according to one embodiment of the present invention. FIG. 5 is an exemplary diagram shown to explain the structure and operation of a light source fixing part that fixes an attack light source in a device for simulating an intentional attack for evaluating the performance of a ToF lidar sensor according to one embodiment of the present invention. FIG. 6 is an exemplary diagram showing the configuration of an attack light source in a device for simulating an intentional attack for evaluating the performance of a ToF lidar sensor according to one embodiment of the present invention. FIG. 7 is an example diagram for explaining the operation of the light source control unit in FIG. 1. FIG. 8 is an exemplary diagram showing the response of a ToF LiDAR sensor according to the operating method of an attack light source in a device for simulating an intentional attack for evaluating the performance of a ToF LiDAR sensor according to one embodiment of the present invention. FIG. 9 is an exemplary diagram illustrating the rotational movement of an attack light source and the process of performing an attack on a sensor in a device for simulating an intentional attack for evaluating the performance of a ToF lidar sensor according to one embodiment of the present invention. FIG. 10 is an example diagram for comparing and explaining the signal change when normal signal processing and a jamming attack are performed in a device for simulating an intentional attack for evaluating the performance of a ToF lidar sensor according to one embodiment of the present invention. FIG. 11 is an example diagram for comparing and explaining the signal distortion phenomenon when a spoofing attack is performed in a device for simulating an intentional attack for evaluating the performance of a ToF lidar sensor according to one embodiment of the present invention. Specific details for implementing the invention

[0036] Hereinafter, an embodiment according to the present invention will be described with reference to the attached drawings.

[0037] In this process, the thickness of lines or the size of components depicted in the drawings may be exaggerated for the sake of clarity and convenience of explanation. Furthermore, the terms described below are defined considering their functions in the present invention, and these may vary depending on the intent or convention of the user or operator. Therefore, the definitions of these terms should be based on the content throughout this specification.

[0038] Embodiments of the present invention are described below with reference to the attached drawings so that those skilled in the art can easily implement them. However, the present invention may be embodied in various different forms and is not limited to the embodiments described herein. Furthermore, in order to clearly explain the present invention in the drawings, parts unrelated to the explanation have been omitted, and similar parts throughout the specification are denoted by similar reference numerals.

[0039] Throughout the specification, when a part is described as "including" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0040] Terms and words used in this specification and claims should not be interpreted as being limited to their ordinary or dictionary meanings, but should be interpreted in a meaning and concept consistent with the technical spirit of the invention, based on the principle that the inventor can appropriately define the concept of the terms to best describe his invention.

[0041] Accordingly, the embodiments described in this specification and the configurations illustrated in the drawings are merely some of the most preferred embodiments of the invention and do not represent all of the technical spirit of the invention; therefore, it should be understood that various equivalents and modifications capable of replacing them may exist at the time of filing this application. Furthermore, as used herein, "comprise" or "include" and / or "comprising" or "including" specify the presence of the mentioned features, numbers, steps, actions, parts, elements, and / or groups thereof, and do not exclude the presence or addition of one or more other features, numbers, actions, parts, elements, and / or groups. Additionally, when describing embodiments of the invention, "may" or "may be" may include "one or more embodiments of the invention."

[0042] Additionally, to aid in understanding the invention, the attached drawings are not drawn to actual scale, and the dimensions of some components may be exaggerated. Furthermore, the same reference numerals may be assigned to identical components in different embodiments.

[0043] The statement that two subjects of comparison are 'identical' means that they are 'substantially identical.' Therefore, substantial identity may include deviations considered low in the industry, for example, deviations within 5%. Additionally, the statement that a parameter is uniform in a given area may mean that it is uniform from an average perspective.

[0044] Although terms such as "first," "second," etc., are used to describe various components, it goes without saying that these components are not limited by these terms. These terms are used merely to distinguish one component from another, and unless specifically stated otherwise, the first component may also be the second component.

[0045] Throughout the specification, unless specifically stated otherwise, each component may be singular or plural.

[0046] The fact that any configuration is placed on the “upper (or lower)” of a component or on the “upper (or lower)” of a component may mean not only that the any configuration is placed in contact with the upper (or lower) surface of said component, but also that another configuration may be interposed between said component and any configuration placed on (or below) said component.

[0047] Furthermore, where it is stated that one component is “connected,” “coupled,” or “joined” to another component, it should be understood that while said components may be directly connected or joined to one another, another component may be “interposed” between each component, or that each component may be “connected,” “coupled,” or “joined” through another component. Additionally, when it is stated that a part is electrically coupled to another part, this includes not only cases where they are directly connected but also cases where they are connected with an intermediate element in between.

[0048] Throughout the specification, “A and / or B” means A, B, or A and B unless specifically stated otherwise. That is, “and / or” includes any combination or any combination of the enumerated items. “C to D” means C or more and D or less, unless specifically stated otherwise.

[0050] FIG. 1 is an exemplary diagram showing the schematic configuration of a device for simulating an intentional attack for evaluating the performance of a ToF lidar sensor according to one embodiment of the present invention.

[0051] Referring to FIG. 1, the intentional attack simulation device for evaluating the performance of a ToF lidar sensor according to the present embodiment may include a processor (110), a light source control unit (120), an alignment control unit (130), and a sensor monitoring unit (140).

[0052] The processor (110) is a central computing unit (i.e., main controller) that controls the overall operation of the intentional attack simulation device for evaluating the performance of the ToF lidar sensor.

[0053] The processor (110) can control the output and operation method of the attack light source by adjusting the operation of the light source control unit (120).

[0054] The processor (110) can communicate with the alignment control unit (130) to adjust the alignment state of the attack light source and the ToF lidar sensor.

[0055] The processor (110) can collect data from the sensor monitoring unit (140), analyze the response of the LiDAR sensor, and evaluate the impact of the attack.

[0056] The light source control unit (120) can implement various attack scenarios by controlling the output and operation method of the attack light source.

[0057] The light source control unit (120) can receive a control command from the processor (110) and adjust the output intensity of the attack light source.

[0058] The light source control unit (120) can perform an attack in a pulse wave or continuous wave manner according to the signal processing characteristics of the ToF lidar sensor.

[0059] The light source control unit (120) can adjust the frequency band of the attack light source to perform an attack in a frequency range where a specific sensor is vulnerable.

[0060] The alignment control unit (130) can optimize the evaluation environment by precisely aligning the attack light source and the ToF lidar sensor.

[0061] The alignment control unit (130) can receive a control signal from the processor (110) and adjust the direction and placement of the attack light source.

[0062] The alignment control unit (130) can adjust the position and height so that the LiDAR sensor targeted for attack and the attack light source can cause optimal interference in a straight line.

[0063] The alignment control unit (130) can simulate the mounting environment in an actual autonomous vehicle by adjusting the fixing device of the sensor.

[0064] The sensor monitoring unit (140) can collect output data from the ToF lidar sensor and analyze the impact of the attack.

[0065] The sensor monitoring unit (140) can collect Point Cloud Data output from the ToF LiDAR sensor and analyze changes before and after the attack.

[0066] The sensor monitoring unit (140) can detect whether the distance value appears as infinite in the output data of the sensor to determine whether jamming (signal interference) is occurring.

[0067] The sensor monitoring unit (140) can analyze how much the distance value detected by the sensor differs from the actual value in order to determine whether there is spoofing (signal modulation).

[0068] The sensor monitoring unit (140) quantitatively measures the impact of an attack and transmits it to the processor (110), enabling the robustness of the ToF lidar sensor to be evaluated based on this.

[0069] In addition, the processor (110) can ensure the reliability of the ToF lidar sensor and analyze its robustness in an attack environment by establishing evaluation sensor log storage and malicious condition performance evaluation specifications and processes for each component (120 to 140).

[0070] For example, the processor (110) can record and store operational data of the ToF LiDAR sensor under evaluation to quantitatively analyze performance changes before and after an attack. The log data stored at this time may include the sensor's Point Cloud data, distance measurements, reflectance, signal strength (SNR), noise level, etc. This data can subsequently be used to compare the sensor's performance and analyze its durability under adverse conditions, and serves as an important criterion for verifying the impact the sensor receives by each type of attack. That is, by comparing the data during the normal operation state of the sensor with the data during an attack, it is possible to evaluate how sensitive or robust the sensor is against a specific type of attack.

[0071] Additionally, the processor (110) can establish performance evaluation criteria for each attack environment of the ToF LiDAR sensor under evaluation and build a process to perform systematic testing and analysis based on these criteria. For example, the evaluation process may include a basic performance evaluation stage (data collection and recording based on the normal operating state of the ToF LiDAR sensor before performing an attack), an attack execution stage (performing attacks such as jamming and spoofing, and measuring the sensor's response and output data for each attack type), a post-attack performance analysis stage (comparative analysis of the sensor's distance measurement error rate, signal distortion phenomenon, reflectance change, etc. after the attack), and a robustness evaluation and improvement plan derivation stage (establishing improvements and security measures for the sensor based on the evaluation results). In particular, different performance evaluation specifications may be applied depending on the characteristics of each sensor and the signal processing method, and evaluations may be performed in various environments (indoors, outdoors, at night, under strong light sources, etc.). Through this, it is possible to verify whether the ToF LiDAR sensor can perform normal signal processing even in actual malicious attack environments and to support the development of robust algorithms and security technologies.

[0072] Meanwhile, in FIG. 1, each component (110 to 140) is described as operating separately, but this is not intended to be limited, and depending on the embodiment, each component (110 to 140) may be integrated into the processor (110).

[0073] FIG. 2 is a flowchart illustrating a control method for a deliberate attack simulation device for evaluating the performance of a ToF lidar sensor according to an embodiment of the present invention.

[0074] Referring to FIG. 2, in the alignment step (S101) of the ToF LiDAR sensor and the attack light source, the alignment control unit (130) can align the position of the attack light source within the detection range of the ToF LiDAR sensor. The alignment control unit (130) can set test conditions by adjusting the height and angle of the attack light source and the ToF LiDAR sensor. Additionally, the processor (110) can save the initial alignment state before performing the attack.

[0075] In the step (S102) for setting attack light source control parameters (e.g., output intensity, frequency, modulation method), the light source control unit (120) can set the output intensity and frequency of the light source to be used for the attack. The processor (110) can select a Pulse Wave or Continuous Wave mode considering the characteristics of the ToF LiDAR sensor. Additionally, the light source control unit (120) can set an appropriate bandwidth and modulation method according to the signal processing characteristics of each sensor.

[0076] In the step (S103) of performing the operation of the attack light source (e.g., Pulse Wave / Continuous Wave), the light source control unit (120) can receive a control signal from the processor (110) and execute a set attack mode. The light source control unit (120) can emit a signal that affects the ToF LiDAR sensor according to the set frequency and modulation method. The alignment control unit (130) can verify whether the attack light source is accurately aimed at a set position.

[0077] In the step of collecting and analyzing sensor response data (Point Cloud Data) (S104), the sensor monitoring unit (140) can collect LiDAR sensor outputs before and after an attack and store Point Cloud Data. The sensor monitoring unit (140) can evaluate the impact of the attack by analyzing information such as distance values, reflectance, and data density detected by the sensor. The processor (110) can compare and analyze the normal state and the post-attack state based on the collected data.

[0078] In the attack impact assessment (analysis of whether jamming or spoofing occurs) step (S105), the sensor monitoring unit (140) can check for data where distance cannot be detected (infinite distance value) to determine whether jamming is occurring. The sensor monitoring unit (140) can analyze the difference between the actual distance and the measured distance value to determine whether spoofing is occurring. The processor (110) quantitatively analyzes the attack results and evaluates the robustness of the ToF LiDAR sensor by comparing it with performance evaluation criteria.

[0079] In the result storage and performance evaluation report generation step (S106), the processor (110) can generate a performance evaluation report by storing the attack execution results and analysis data. The processor (110) can calculate robustness evaluation indicators for each sensor and provide performance evaluation results of the LiDAR sensor based thereon. The sensor monitoring unit (140) can visualize and analyze data changes that occurred during the attack execution.

[0080] As such, this embodiment systematically executes attack scenarios to quantitatively evaluate the performance of the ToF LiDAR sensor, and can automate the entire process from attack impact assessment to report generation.

[0081] FIG. 3 is an exemplary diagram schematically showing the structure of a sensor fixing part that fixes a lidar sensor to be evaluated in a device for simulating an intentional attack for evaluating the performance of a ToF lidar sensor according to one embodiment of the present invention.

[0082] Referring to FIG. 3, the sensor fixing part (210) according to the present embodiment is implemented in a form that stably fixes the lidar sensor to be evaluated and enables performance evaluation to be performed at various angles and heights.

[0083] The sensor fixing part (210) includes a fixing plate, and the lidar sensor to be evaluated is mounted on the fixing plate so that it can be fixed without shaking in a test environment.

[0084] The sensor fixing part (210) includes a mounting height adjustment function, allowing the height of the lidar sensor to be adjusted according to the test environment.

[0085] The sensor fixing part (210) includes an angle adjustment function using an electric motor (not shown) and allows the LiDAR sensor to be adjusted so that it can be aligned with an attack light source from various directions.

[0086] The sensor fixing part (210) is configured to reproduce conditions similar to the sensor mounting position in an actual autonomous vehicle, and to increase the precision of the test conditions.

[0087] In this embodiment, the sensor fixing part (210) ensures that the ToF lidar sensor is stably maintained at a constant position when performing a performance evaluation of the ToF lidar sensor, and allows for the establishment of a more reliable test environment by adjusting the alignment with the attack light source.

[0088] FIG. 4 is an exemplary diagram schematically showing the mounting structure of a lidar sensor to be evaluated in a device for simulating an intentional attack for evaluating the performance of a ToF lidar sensor according to one embodiment of the present invention.

[0089] Referring to FIG. 4, the mounting structure of the lidar sensor to be evaluated (i.e., ToF lidar sensor) according to the present embodiment includes a bracket and an adjustment device to stably fix the lidar sensor to be evaluated in a test environment and to facilitate alignment with an attack light source.

[0090] In the front structure, the lidar sensor under evaluation is mounted on a bracket, allowing the placement status of the lidar sensor under evaluation to be checked from the front.

[0091] The rear structure includes an interface and wiring connected to the rear of the lidar sensor under evaluation, enabling real-time monitoring of the lidar sensor's output.

[0092] The bracket (220) serves to support the lidar sensor under evaluation and is designed to allow height and angle adjustment so as to optimize alignment with the attack light source.

[0093] The joint between the lidar sensor to be evaluated and the bracket (220) is configured to stably fix the sensor, thereby minimizing positional changes of the lidar sensor to be evaluated during testing.

[0094] FIG. 5 is an exemplary diagram shown to explain the structure and operation of a light source fixing part that fixes an attack light source in a device for simulating an intentional attack for evaluating the performance of a ToF lidar sensor according to one embodiment of the present invention.

[0095] Referring to FIG. 5, the attack light source according to the present embodiment is mounted on a rotating stage and configured to perform attacks at various angles and heights.

[0096] The attack light source (320) is mounted on a rotating stage and can be aligned in a specific direction according to the operation of the rotating stage.

[0097] The rotating stage can adjust the direction of the attack light source (320) and allow the attack to be performed at a specific angle.

[0098] The attack light source (320) is designed to be height-adjustable, allowing for the configuration of an optimal attack scenario according to the mounting position of the ToF LiDAR sensor being evaluated.

[0099] When performing an attack, the attack light source (320) emits light at a set output and frequency band and can operate in a manner that affects the detection performance of the ToF lidar sensor.

[0100] The operation of the attack light source (320) is controlled by the light source control unit (120) and can be modulated in a pulse wave or continuous wave manner.

[0101] The light source control unit (120) moves the attack light source (320) within a predefined attack execution range and enables sequential repeated attacks to be performed.

[0102] The attack light source (320) is placed at an optimal location (designated location) where the ToF lidar sensor can recognize it, and allows the sensor performance in various environments to be evaluated while adjusting the angle.

[0103] The light source fixing part (310) includes a rotating stage and can precisely adjust the rotation angle of the attack light source. The rotating stage is equipped with a digital level (not shown) to measure and record the movement angle of the attack light source (320) in real time. This configuration ensures repeatability, allowing the attack to be performed in the same environment.

[0104] Since the ToF lidar sensor to be evaluated operates in a specific signal band for each sensor, the light source fixing part (310) may use an appropriate attack light source (320) in accordance with the specifications of the ToF lidar sensor to be evaluated.

[0105] The attack light source (320) can have its frequency and output intensity adjusted to match the signal processing characteristics of the lidar sensor, and can evaluate performance in a specific band.

[0106] In order to perform a spoofing attack, the light source fixing part (310) can analyze the light source information (bandwidth, band variable period) of the ToF lidar sensor in advance.

[0107] The attack light source (320) emits a modulated signal in a band that matches the signal frequency of the ToF lidar sensor being evaluated, so that the ToF lidar sensor being evaluated can recognize an incorrect distance value.

[0108] The light source control unit (120) adjusts the wavelength of the attack light source (320) to enable an effective spoofing attack.

[0109] FIG. 6 is an exemplary diagram showing the configuration of an attack light source in a device for simulating an intentional attack for evaluating the performance of a ToF lidar sensor according to one embodiment of the present invention.

[0110] Referring to FIG. 6, the attack light source (320) according to the present embodiment is composed of an 'attack confirmation light source (321)' and a 'LiDAR attack light source (322)', and their respective roles are distinguished.

[0111] The attack confirmation light source (321) emits light in the visible light band to allow the location of the attack light source to be visually confirmed. The attack confirmation light source (321) can be used as a reference point when adjusting alignment with the ToF LiDAR sensor before performing an attack. This is because the attack light source in the infrared band cannot be seen with the naked eye, so a visible light band confirmation light source is added to increase the accuracy of the light source alignment.

[0112] The LiDAR attack light source (322) includes a laser light source in the infrared band that performs an actual attack affecting the ToF LiDAR sensor. The LiDAR attack light source (322) emits infrared light of a specific wavelength band according to the signal processing method of the LiDAR sensor to cause signal interference.

[0113] The LiDAR attack light source (322) has its output intensity and modulation method controlled by the light source control unit (120), enabling performance evaluation in various environments.

[0114] FIG. 7 is an example diagram for explaining the operation of the light source control unit in FIG. 1.

[0115] Referring to FIG. 7, the light source control unit (120) according to the present embodiment can perform various types of intentional attacks by adjusting the output and operation method of the attack light source (320).

[0116] The light source control unit (120) can set two main parameters for performing an attack.

[0117] First, through the output control parameter, the light source control unit (120) can adjust the intensity of the attack by controlling the output of the attack light source (320).

[0118] After adjusting the output, the light source control unit (120) can set the range for performing the attack.

[0119] When setting the attack execution range, the light source control unit (120) can set the spatial range to which the attack is applied so that signal interference or modulation can be performed only in a specific area.

[0120] Through the following operation mode (Pulse / CW selection) parameter, the light source control unit (120) can perform an attack in a Pulse Wave or Continuous Wave manner.

[0121] After setting the operation method, the light source control unit (120) can select (determine) the type of intentional attack to be performed.

[0122] For example, in selecting the type of intentional attack, various attack types such as Jamming and Spoofing can be selected.

[0123] FIG. 8 is an exemplary diagram showing the response of a ToF LiDAR sensor according to the operating method of an attack light source in a device for simulating an intentional attack for evaluating the performance of a ToF LiDAR sensor according to one embodiment of the present invention.

[0124] Referring to FIG. 8, the attack light source (320) according to the present embodiment can operate in a pulse wave or continuous wave manner, and accordingly, the ToF LiDAR sensor can react differently.

[0125] When performing a pulse wave attack, the ToF LiDAR sensor is primarily designed to be affected by jamming.

[0126] Jamming attacks can operate by interfering with a sensor's ability to measure normal distance values. For instance, a jamming attack can emit interference signals at a specific frequency to prevent the sensor from receiving signals or to cause it to return infinite distance values. In other words, a jamming attack can neutralize the receiver's function by transmitting a jamming signal to the receiver of a ToF LiDAR sensor, thereby significantly lowering the Signal-to-Noise Ratio (SNR) of the original carrier signal that should be detected by the receiver.

[0127] When a Continuous Wave attack is performed, the ToF LiDAR sensor may be affected by Spoofing or Jamming.

[0128] Spoofing attacks can distort distances, causing ToF LiDAR sensors to recognize distance values ​​different from the actual ones. Continuous waves can cause continuous interference in the sensor's signal band, preventing the sensor from interpreting normal reflected signals. For example, if the sensor detects spoofing, it may output incorrect measurements indicating a distance that is closer or farther than the actual distance. In other words, spoofing attacks transmit fake signals that do not actually exist to the receiver of a ToF LiDAR sensor, thereby causing the sensor to perceive non-existent objects as present or to perceive the distance of existing objects as being farther or closer.

[0129] For reference, depending on the purpose and performance of each LiDAR sensor, an attack light source may be used to match the band of another sensor. If a specific wavelength band is insufficient, an artificial sunlight light source can be used to carry out the attack. To perform spoofing, wavelength information must be verified using the LiDAR light source information (e.g., bandwidth, band-variable period).

[0130] FIG. 9 is an exemplary diagram illustrating the rotational movement of an attack light source and the process of performing an attack on a sensor in a device for simulating an intentional attack for evaluating the performance of a ToF lidar sensor according to one embodiment of the present invention.

[0131] Referring to FIG. 9, the attack light source (320) is mounted on the rotating stage of the light source fixing part (310), thereby allowing attacks to be performed from various angles.

[0132] The attack light source (320) is placed on a rotating stage and can perform a deliberate attack on the ToF lidar sensor while rotating at a constant angle.

[0133] The rotating stage rotates according to the command of the light source control unit (120) and can emit a signal aimed in a specific direction.

[0134] The laser emitted from the attack light source (320) is directed toward the ToF lidar sensor being evaluated and may cause interference in the sensor's signal collection process.

[0135] The sensor fixing part (210) stably fixes the ToF lidar sensor to be evaluated and enables repetitive performance evaluation at a fixed position.

[0136] The sensor fixing part (210) maintains alignment with the attack light source (320) so that the test can be performed in a uniform environment.

[0137] The impact of an attack can be analyzed by comparing data in an environment where the ToF lidar sensor being evaluated operates normally and in an environment where the attack light source (320) operates.

[0138] The rotating stage of the light source fixing part (310) can adjust the direction of the attack light source (320) and allows the measurement of performance changes at a specific angle with respect to the ToF lidar sensor to be evaluated.

[0139] By using a digital level (not shown) mounted on the light source fixing part (310), the angle of the attack light source (320) can be precisely measured and recorded.

[0140] This embodiment enables the evaluation of signal interference effects based on the position and alignment of the ToF LiDAR sensor to be evaluated by performing attacks from various angles using the light source fixing part (310) and the sensor fixing part (210).

[0141] FIG. 10 is an example diagram for comparing and explaining the signal change when normal signal processing and a jamming attack are performed in a device for simulating an intentional attack for evaluating the performance of a ToF lidar sensor according to one embodiment of the present invention.

[0142] Referring to Figure 10, the left graph (Normal) shows the reflection and detection process of a normal ToF lidar signal, and the right graph (Jamming) shows the process in which the signal of the ToF lidar sensor being evaluated is interfered with by an attack signal.

[0143] In a normal ToF LiDAR signal, the transmitted signal (TX) is emitted and the reflected signal (RX) is reliably received, and the ToF LiDAR sensor performs distance measurement based on the arrival time of the reflected signal. Under normal conditions, the reflected signal has a distinct peak, which allows the ToF LiDAR sensor to calculate the accurate distance to an object.

[0144] If a jamming attack is performed, the jamming signal can affect the received signal of the ToF lidar sensor, interfering with normal distance measurement.

[0145] In this case, a jamming attack inserts an interference signal between the transmitted signal (TX) and the received signal (RX), causing the LiDAR sensor to fail to recognize normal reflected signals.

[0146] In particular, applying irregular modulation to the signal can make it difficult to detect peaks in the received signal, or distorting the signal strength can cause the sensor to be unable to interpret normal data.

[0147] If such a jamming attack is performed, the ToF LiDAR sensor is highly likely to fail to recognize normal reflected signals (RX) or calculate incorrect distance values.

[0148] For example, ToF LiDAR sensors may perceive distance values ​​as infinite or output arbitrary incorrect distance values, and the detection performance of the ToF LiDAR sensor may degrade due to increased signal uncertainty.

[0149] The intentional attack simulation device according to the present embodiment enables the analysis of the robustness of a ToF LiDAR sensor by quantitatively evaluating such jamming attacks. This supports the development of a defense algorithm to enable the ToF LiDAR sensor to operate normally even in an environment of signal interference, and enables reliable sensor performance evaluation.

[0150] FIG. 11 is an example diagram for comparing and explaining the signal distortion phenomenon when a spoofing attack is performed in a device for simulating an intentional attack for evaluating the performance of a ToF lidar sensor according to one embodiment of the present invention.

[0151] Referring to Figure 11, both the left graph and the right graph contain spoofing attack signals and indicate that the location where the spoofing attack is performed is different.

[0152] The graph on the left (first spoofing attack scenario) illustrates a situation where the distance measurement of the ToF LiDAR sensor is distorted due to an attack signal interfering between the transmitted signal (TX) and the received signal (RX). In other words, the ToF LiDAR sensor fails to accurately detect the transmitted signal (TX) and the received signal (RX).

[0153] As a spoofing signal is inserted in a manner similar to an actual reflected signal, the ToF lidar sensor may incorrectly perceive the presence of an additional reflected signal.

[0154] Consequently, instead of recognizing the location of actual objects, ToF LiDAR sensors are highly likely to detect non-existent objects or output distorted distance measurements.

[0155] The graph on the right (second spoofing attack scenario) illustrates a situation where attack signals are inserted in a different manner, inducing the sensor to confuse distance measurements at specific locations. In other words, the ToF LiDAR sensor fails to accurately recognize the transmitted signal (TX) and the received signal (RX).

[0156] When an attack signal is received along with a reflected signal including a specific time delay, the sensor may recognize the attack signal as the signal of the actual object rather than the original reflected signal.

[0157] This can cause distance measurements to appear closer or farther than the actual distance, increasing the likelihood of the sensor malfunctioning in a distorted environment.

[0158] The attack signal includes an intentionally modulated reflected signal, which is modulated to resemble a normal RX signal to confuse the ToF lidar sensor.

[0159] Consequently, the ToF LiDAR sensor may incorrectly recognize the attack signal as the actual reflected signal (RX) and output an incorrect distance value. As the frequency of the attack signal continuously changes or a constant delay is applied to distort the reflected signal, the ToF LiDAR sensor may measure the position of an object differently from the actual location.

[0160] As a result, ToF LiDAR sensors are more likely to detect non-existent objects or perceive the location of actual objects at incorrect distances.

[0161] If a spoofing attack occurs in this way, the ToF LiDAR sensor may fail to provide normal distance data and output distorted information.

[0162] By experimentally reproducing such attacks, it is possible to develop defense algorithms that enable ToF LiDAR sensors to provide accurate data even in signal modulation environments.

[0163] The intentional attack simulation device of the present invention enables the analysis of the robustness of a ToF lidar sensor by quantitatively evaluating such spoofing attacks.

[0164] Referring to FIGS. 10 and 11, the sensor monitoring unit (140) according to the present embodiment monitors the output data of the ToF lidar sensor in real time when Jamming and Spoofing attacks are performed, and performs the role of analyzing the signal distortion phenomenon of the sensor.

[0165] In the analysis of the Jamming attack illustrated in FIG. 10, the sensor monitoring unit (140) can detect the effect of the Jamming signal in the received signal (RX) of the ToF lidar sensor and analyze the data distortion caused by signal interference.

[0166] When a jamming attack is performed, the sensor monitoring unit (140) can determine whether swept frequency or fixed frequency interference occurs in the signal band of the ToF lidar sensor. That is, the sensor monitoring unit (140) can determine whether the ToF lidar sensor returns an infinite distance value due to jamming, or whether normal signal reception becomes impossible. Based on this data, the robustness of the ToF lidar sensor against jamming attacks can be evaluated.

[0167] In the analysis of the spoofing attack illustrated in FIG. 11, the sensor monitoring unit (140) can analyze the impact of the spoofing attack by comparing the distance value detected by the sensor with the actual distance value.

[0168] When a spoofing signal is inserted by modulating it to resemble the normal receiving signal (RX) of the ToF lidar sensor, the sensor monitoring unit (140) can determine whether the ToF lidar sensor mistakes it for an actual reflected signal. That is, the sensor monitoring unit (140) can analyze whether the ToF lidar sensor detects an object that does not exist or measures the distance of an actual object by distorting it.

[0169] In the diagram, "?" indicates that it is unclear whether it is an actual signal.

[0170] The sensor monitoring unit (140) monitors the continuity of data output by the ToF lidar sensor and can detect patterns that fluctuate abnormally due to spoofing attacks.

[0171] The sensor monitoring unit (140) can quantitatively evaluate the impact of the attack on the performance of the ToF lidar sensor by comparing and analyzing data before and after the attack.

[0172] The sensor monitoring unit (140) can verify the performance of the defense algorithm by analyzing the accuracy and reliability of the data output by the ToF lidar sensor after Jamming and Spoofing attacks are performed.

[0173] The sensor monitoring unit (140) can quantify the level of performance degradation by measuring factors such as the distance value error rate, data loss rate, and response time delay of the ToF lidar sensor.

[0174] The sensor monitoring unit (140) can evaluate the limits that the ToF lidar sensor can withstand for each type of attack and provide criteria for strengthening the security of the ToF lidar sensor.

[0175] This embodiment enables the evaluation of the sensor's robustness by precisely simulating a malicious physical attack on a ToF LiDAR sensor, and also enables the establishment of a repeatable performance evaluation environment by controlling the output and operation method of the attack light source.

[0176] This embodiment enables the quantitative evaluation of the impact of attacks such as jamming and spoofing by analyzing the output data of a ToF LiDAR sensor, and enables the verification of performance in various environments by applying attack scenarios tailored to the signal characteristics of each LiDAR sensor.

[0177] This embodiment improves the reliability of LiDAR sensors used in autonomous driving and security systems, thereby enhancing safety.

[0178] The implementations described herein may be implemented, for example, as methods or processes, devices, software programs, data streams, or signals. Even if discussed only in the context of a single form of implementation (e.g., discussed only as a method), the implementation of the discussed features may also be implemented in other forms (e.g., devices or programs). Devices may be implemented in appropriate hardware, software, and firmware, etc. Methods may be implemented in devices such as processors, which generally refer to processing devices including, for example, computers, microprocessors, integrated circuits, or programmable logic devices. Processors also include communication devices such as computers, cell phones, portable / personal digital assistants ("PDAs"), and other devices that facilitate the communication of information between end-users.

[0179] Although the present invention has been described above by limited embodiments and drawings, the present invention is not limited thereto, and it is obvious that various modifications and variations are possible within the scope of the technical spirit of the present invention and the equivalent scope of the claims described below by those skilled in the art to which the present invention belongs. Explanation of the symbols

[0181] 110 : Processor 120 : Light source control unit 130 : Alignment control unit 140 : Sensor monitoring unit 210: Sensor fixing part 220 : Bracket 310 : Light source fixing part 320 : Attack Light Source 321: Attack confirmation light source 322 : LiDAR attack light source

Claims

Claim 1 A device for simulating an intentional attack for evaluating the performance of a ToF (Time-of-Flight) LiDAR sensor, comprising: a sensor fixing unit for fixing a ToF LiDAR sensor; a light source control unit equipped with a rotating stage to which an attack light source is fixed and which controls the output and operation method of the attack light source; a sensor monitoring unit for monitoring the signal output of the ToF LiDAR sensor; and a processor that controls the overall operation of the sensor fixing unit, the light source control unit, and the sensor monitoring unit, and, in conjunction with the light source control unit and the alignment control unit, adjusts the alignment state and output characteristics of the attack light source for the ToF LiDAR sensor; wherein the processor generates a performance evaluation report of the ToF LiDAR sensor by comparing data before and after the attack based on the analysis results of the sensor monitoring unit. Claim 2 A device for simulating an intentional attack for evaluating the performance of a ToF LiDAR sensor according to claim 1, wherein the processor, in conjunction with the light source control unit, sets the output intensity, frequency, and modulation method of the attack light source before performing an attack on the ToF LiDAR sensor, selects a Pulse Wave or Continuous Wave mode according to the characteristics of the ToF LiDAR sensor, and adjusts the rotation angle of the attack light source through the rotation of the rotation stage to perform an attack on the ToF LiDAR sensor from various angles. Claim 3 A device for simulating an intentional attack for evaluating the performance of a ToF LiDAR sensor, characterized in that, in claim 1, the alignment control unit adjusts the relative position and angle of the attack light source and the ToF LiDAR sensor so that the attack light source is aligned within the detection range of the ToF LiDAR sensor, and the processor stores the alignment state of the attack light source and the ToF LiDAR sensor to maintain test conditions for repeated reproduction. Claim 4 In claim 1, the light source control unit is characterized by adjusting the frequency band of the attack light source to match the signal processing characteristics of the ToF lidar sensor so that the ToF lidar sensor can perform an attack in a frequency range where it is vulnerable. Claim 5 A device for simulating an intentional attack for evaluating the performance of a ToF LiDAR sensor, characterized in that, in claim 1, the sensor monitoring unit collects point cloud data output from the ToF LiDAR sensor, analyzes changes before and after the execution of an attack, determines whether a jamming attack has occurred based on whether a distance value is not detected or whether a distance value returns infinity, and determines whether a spoofing attack has occurred by analyzing the difference between the distance value detected by the ToF LiDAR sensor and the actual distance value. Claim 6 In claim 1, the sensor fixing part is designed to allow the ToF lidar sensor to be evaluated to be positioned at various heights and angles, and the mounting environment of the ToF lidar sensor is adjusted to a specified height and angle to increase the repeatability of the test, thereby forming an intentional attack simulation device for evaluating the performance of a ToF lidar sensor. Claim 7 A device for simulating an intentional attack for evaluating the performance of a ToF LiDAR sensor, characterized in that, in claim 1, the attack light source is formed to include an attack confirmation light source and a LiDAR attack light source, the attack confirmation light source is a light source that emits light in the visible light band to enable visual confirmation of the location of the attack light source, and the LiDAR attack light source includes a laser light source in the infrared band that performs an actual attack affecting the ToF LiDAR sensor. Claim 8 A method for simulating an intentional attack for evaluating the performance of a ToF LiDAR sensor, comprising: a step in which a processor of an intentional attack simulation device controls the output and operation method of an attack light source through a light source control unit, wherein a ToF LiDAR sensor is fixed to a sensor fixing unit and the attack light source is fixed to a light source fixing unit in the intentional attack simulation device; a step in which the processor, in conjunction with the light source control unit and the alignment control unit, adjusts the rotation angle, alignment state, and output characteristics of the attack light source for the ToF LiDAR sensor through the rotation stage; and a step in which the processor monitors the signal output of the ToF LiDAR sensor through a sensor monitoring unit; wherein, in the step of controlling the output and operation method of the attack light source, the processor, in conjunction with the light source control unit, sets the output intensity, frequency, and modulation method of the attack light source before performing an attack on the ToF LiDAR sensor, and selects a Pulse Wave or Continuous Wave mode according to the characteristics of the ToF LiDAR sensor. Claim 9 A method for simulating an intentional attack for evaluating the performance of a ToF LiDAR sensor, characterized in that, in the step of adjusting the alignment state and output characteristics of the attack light source, the alignment control unit adjusts the relative position and angle of the attack light source and the ToF LiDAR sensor so that the attack light source is aligned within the detection range of the ToF LiDAR sensor, and the processor stores the alignment state of the attack light source and the ToF LiDAR sensor to maintain test conditions for repeated reproduction. Claim 10 A method for simulating an intentional attack for evaluating the performance of a ToF LiDAR sensor, characterized in that, in the step of controlling the output and operation method of the attack light source, the light source control unit adjusts the frequency band of the attack light source in accordance with the signal processing characteristics of the ToF LiDAR sensor so that the ToF LiDAR sensor can perform an attack in a frequency range where it is vulnerable. Claim 11 A method for simulating an intentional attack for evaluating the performance of a ToF LiDAR sensor, characterized in that, in the step of monitoring the signal output of the ToF LiDAR sensor, the sensor monitoring unit collects point cloud data output from the ToF LiDAR sensor and analyzes changes before and after the execution of an attack, determines whether a jamming attack is occurring based on whether a distance value is not detected or whether the distance value returns infinity, and determines whether a spoofing attack is occurring by analyzing the difference between the distance value detected by the ToF LiDAR sensor and the actual distance value. Claim 12 A method for simulating an intentional attack for evaluating the performance of a ToF LiDAR sensor, characterized in that, in the step of adjusting the alignment state and output characteristics of the attack light source, the processor adjusts the rotation angle of the attack light source through the rotation of a rotation stage of a light source fixing part to which the attack light source is fixed, thereby performing an attack on the ToF LiDAR sensor at various angles, and also generates a performance evaluation report of the ToF LiDAR sensor by comparing data before and after the attack based on the analysis results of the sensor monitoring part.

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