[go: up one dir, main page]

WO2019168167A1 - Procédé de vérification, dispositif de vérification, programme de vérification et système de vérification - Google Patents

Procédé de vérification, dispositif de vérification, programme de vérification et système de vérification Download PDF

Info

Publication number
WO2019168167A1
WO2019168167A1 PCT/JP2019/008154 JP2019008154W WO2019168167A1 WO 2019168167 A1 WO2019168167 A1 WO 2019168167A1 JP 2019008154 W JP2019008154 W JP 2019008154W WO 2019168167 A1 WO2019168167 A1 WO 2019168167A1
Authority
WO
WIPO (PCT)
Prior art keywords
side channel
channel information
test device
electromagnetic wave
verification
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.)
Ceased
Application number
PCT/JP2019/008154
Other languages
English (en)
Japanese (ja)
Inventor
藤野　毅
久保田　貴也
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Ritsumeikan Trust
Original Assignee
Ritsumeikan Trust
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Ritsumeikan Trust filed Critical Ritsumeikan Trust
Priority to JP2019572255A priority Critical patent/JPWO2019168167A1/ja
Publication of WO2019168167A1 publication Critical patent/WO2019168167A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/22—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing

Definitions

  • the present disclosure relates to a verification method, a verification device, a computer program, and a verification system.
  • an electronic device may operate abnormally according to illegally input data from a third party called a so-called cyber attack.
  • An abnormal operation refers to the inability to operate or an operation different from the original operation.
  • the fuzzing test is a black box test method in which various data are comprehensively sent to an electronic device to be tested to detect the presence or absence of abnormal operation.
  • Non-Patent Document 1 published at the "Cryptography and Information Security Symposium" in 2017 is based on a hardware simulator that performs the same operation as an electronic device that is the subject of a fuzzing test.
  • the verification method of the target electronic device is proposed by comparing the operation of the hardware simulator when the same data as the target electronic device is input with the operation of the target electronic device.
  • cost increases and complexity due to the use of a hardware simulator are problems. Therefore, it is desired to simplify the verification of the test device.
  • a verification method includes receiving side channel information from a test device provided with fuzz data, verifying operation of the test device based on the received side channel information, and Is provided.
  • the verification apparatus performs an operation of a test device based on an interface that receives input of side channel information from a test device to which fuzz data is given, and side channel information input through the interface. And a processing unit for verifying.
  • the computer program is a computer program for causing a computer to function as a verification device that verifies the operation of the test device, and the computer is configured to receive side channel information from the test device to which fuzz data is given. Based on the side channel information input via the input unit, and a processing unit for verifying the operation of the test device.
  • the verification system measures the operation of the test device based on the measurement device that measures the side channel information from the test device to which the fuzz data is given and the side channel information that is input from the measurement device.
  • a verification device for verification is provided.
  • FIG. 1 is a diagram illustrating an example of a configuration of a verification system and a verification method.
  • FIG. 2 is a diagram for explaining the configuration of the verification apparatus.
  • FIG. 3 is a block diagram illustrating processing executed by the control unit of the verification apparatus.
  • FIG. 4 is a diagram illustrating a specific example of a normal-time electromagnetic wave database.
  • FIG. 5 is a flowchart showing an example of the flow of abnormal operation detection processing in the verification device.
  • FIG. 6 is a diagram illustrating a specific example of the abnormal electromagnetic wave database.
  • FIG. 7 is a flowchart showing another example of the abnormal operation detection process flow in the verification apparatus.
  • FIG. 8 is a diagram showing another example of the abnormal time electromagnetic wave database.
  • FIG. 9 is a flowchart showing another example of the flow of the abnormal operation detection process in the verification device.
  • FIG. 10 is a diagram illustrating an example of the input signal (A) to the test device and the detected electromagnetic wave waveform (B) in the first embodiment.
  • FIG. 11 is an enlarged view of the input signal (A) and the detected electromagnetic wave waveform (B) after the sixth frame in FIG.
  • FIG. 12 is an enlarged view of an electromagnetic wave detected immediately after the sixth frame input in FIG.
  • FIG. 13 is a diagram illustrating another example of the input signal (A) to the test device and the detected electromagnetic wave waveform (B) in the second embodiment. It is a figure for demonstrating an example of arrangement
  • FIG. 17 is a block diagram illustrating processing executed by the control unit of the verification apparatus according to the seventh embodiment.
  • FIG. 18 is a diagram schematically showing the structure of the classifier of FIG. 17 and the learning method of the classifier.
  • the verification method included in the present embodiment includes a step of receiving side channel information from a test device to which fuzz data is given, and verifies the operation of the test device based on the received side channel information. Steps.
  • the side channel information here is synonymous with the side channel information acquired for decryption in a decryption method called a side-channel attack. That is, the side channel information is information obtained from a channel (side channel) other than the regular input / output channel of data in the test device.
  • the side channel information includes, for example, electromagnetic waves, power consumption, temperature, sound, and the like.
  • the verification of the operation of the test device may be the same as the verification performed in fuzzing, for example, verification of an unknown vulnerability in the test device.
  • the verification of the unknown vulnerability is, for example, detection of abnormal operation.
  • the method of verifying the operation based on the side channel information can avoid an increase in cost compared to the case of separately providing a hardware simulator for verification different from the test device, and the operation of the hardware simulator and the operation of the test device. Since it is not necessary to verify the identity of each other, the verification work can be simplified.
  • the step of verifying the operation of the test device includes detecting an abnormal operation of the test device.
  • the abnormal operation is different from the operation instructed by the control signal, for example, an arithmetic operation that performs an operation to divide by zero, an arithmetic operation to calculate a square root of a negative number, an operation that accompanies a buffer overflow, and a watch Such as the expiration of a dog timer.
  • an arithmetic operation that performs an operation to divide by zero
  • an arithmetic operation to calculate a square root of a negative number an operation that accompanies a buffer overflow
  • a watch Such as the expiration of a dog timer.
  • the detection of the abnormal operation is at least one of detection of the presence / absence of the abnormal operation and detection of the type of the abnormal operation.
  • this method it is possible to easily detect at least one of detection of the presence or absence of abnormal operation and detection of the type of abnormal operation.
  • the side channel information is at least one of electromagnetic waves leaking from the operating test device and power consumption of the test device.
  • an abnormal operation of the test device can be detected with high accuracy with a simple apparatus using electromagnetic waves or power consumption of the test device as side channel information.
  • the verifying step includes a step of comparing the received side channel information with reference side channel information stored in a database.
  • the database stores at least one reference side channel information during abnormal operation and during normal operation, and is stored in the database with the received side channel information in the comparing step.
  • the database stores at least one reference side channel information during abnormal operation and during normal operation, and is stored in the database with the received side channel information in the comparing step.
  • the database stores side channel information for each type of abnormal operation, and the received side channel information is compared with reference side channel information for each type of abnormal operation in the comparing step. Accordingly, the type of abnormal operation in the test device is detected in the verification step. By this method, it is possible to easily detect the type of abnormal operation.
  • the database stores reference side channel information for each type of test device, and in the comparing step, the received side channel information and the reference side channel information stored in the database are included.
  • the reference side channel information corresponding to the type of the test device is compared.
  • the type of the test device may be a product type of the test device or a manufacturer type of the test device, but is not limited thereto. By this method, an abnormal operation can be detected with higher accuracy according to the type of the test device.
  • the side channel information is time-series data
  • the step of comparing is defined in the received side channel information that is a part less than the whole of the received side channel information.
  • the information according to the period is compared with the reference side channel information stored in the database.
  • the side channel information is information measured from a specific position with respect to the test device, and the database stores reference side channel information for each position with respect to the test device.
  • the received side channel information is compared with the reference side channel information stored in the database according to the position with respect to the test device that has received the channel information.
  • the position with respect to the test device is, for example, the position where the memory of the test device is mounted, the position where the CPU is mounted, or the like.
  • the side channel information measured from a specific position with respect to the test device is, for example, leaked electromagnetic waves, power consumption, heat, sound, and the like. As a result, for each position of the test device, it is possible to detect an abnormal operation of the apparatus mounted at that position.
  • the model includes inputting the received side channel information and obtaining its output.
  • the learning model is provided with a first model for classifying the type of operation based on the feature amount obtained from the side channel information, and the side channel information at the time of abnormal operation is given from the feature amount.
  • a second model that determines whether the side channel information is during normal operation.
  • the first model is, for example, a one-dimensional convolutional neural network (1D-CNN).
  • the second model is a classification algorithm such as One class SVM (Support Vector Vector Machine).
  • the learning model is learned by a learning method called so-called transfer learning.
  • the detected abnormal operation includes a calculation abnormality.
  • the operation abnormality includes, for example, an operation that divides by zero, an operation that calculates a square root of a negative number, and the like.
  • the test device includes an in-vehicle control device.
  • operation of a vehicle-mounted control apparatus can be verified using this verification method.
  • the verification apparatus included in the present embodiment includes an interface that receives input of side channel information from a test device to which fuzz data is given, and a test device based on side channel information input through the interface. And a processing unit for verifying the operation. Since this verification apparatus employs the verification methods (1) to (14), it has the same effects as the verification methods described in (1) to (14).
  • a computer program included in the present embodiment is a computer program for causing a computer to function as a verification device that verifies the operation of a test device, and the computer is connected to a side channel from a test device to which fuzz data is given. It functions as an input unit that receives input of information and a processing unit that verifies the operation of the test device based on side channel information input via the input unit. Since this computer program causes the computer to execute the verification methods (1) to (14), the computer program has the same effects as the verification methods described in (1) to (14).
  • the verification system included in the present embodiment includes a measurement device that measures side channel information from a test device to which fuzz data is given, and a test device that is based on side channel information input from the measurement device.
  • a verification device for verifying the operation Since this verification system is a system that executes the verification methods (1) to (14), it has the same effects as the verification methods described in (1) to (14).
  • FIG. 1 is a diagram showing an example of a configuration of a verification system 100 according to the present embodiment.
  • a verification system 100 is an example of a verification apparatus 1 that executes a fuzz test, an input apparatus 3 that can input a control signal that is fuzz data to a test device D, and side channel information from the test device.
  • an electromagnetic wave measurement probe hereinafter referred to as probe 5.
  • the verification device 1 verifies the operation of the test device D by executing a fuzz test. Verification of the operation of the test device D includes detecting an abnormal operation in the test device D.
  • the abnormal operation is, for example, an operation other than the operation specified for the input control signal.
  • the test device D may be any device as long as it is a device that receives an input of a control signal that is fuzz data from the outside and executes an operation defined for the input control signal.
  • the test device D is a device that does not have a function of outputting a result of the operation, such as a display.
  • the test device D is a device that is mounted on another device and has a low operation frequency, for example.
  • the other device is, for example, a vehicle, and the test device D is, for example, an ECU (Electronic Control Unit) that is an in-vehicle control device. In the following description, it is assumed that the test device D is an ECU.
  • the ECU is connected to an in-vehicle network that adopts a communication standard such as CAN (Controller Area Network), LIN (Local Interconnect Network), Ethernet (registered trademark), or MOST (Media Oriented System Transport: MOST is a registered trademark)
  • CAN Controller Area Network
  • LIN Local Interconnect Network
  • Ethernet registered trademark
  • MOST Media Oriented System Transport: MOST is a registered trademark
  • the input device 3 is connected to the test device D by wire or wireless, and can input a CAN message, which is a control signal according to the CAN communication standard, to the ECU as an example.
  • the input device 3 may be a device independent of the verification device 1 or may be included in the verification device 1. In the following description, it is assumed that the input device 3 is an independent device from the verification device 1 and is connected to the verification device 1 so as to be communicable.
  • the input device 3 inputs a control frame to the ECU according to the CAN message from the verification device 1.
  • the probe 5 acquires side channel information, which is information indicating an operation state, from the test device D without contacting the test device D, or is acquired by being connected to the test device D by wire or wireless. It is an example of an apparatus.
  • the probe 5 receives an electromagnetic wave leaking from the test device D, which is an example of side channel information.
  • the probe 5 can communicate with the verification device 1 by wire or wirelessly, and inputs a signal indicating the received electromagnetic wave to the verification device 1.
  • the verification apparatus 1 is composed of a general PC (personal computer) or the like. Using the signal input from the probe 5, an abnormal operation detection process for detecting the presence or absence of an abnormal operation of the ECU that is the test device D is executed.
  • FIG. 2 is a diagram for explaining the configuration of the verification apparatus 1.
  • the verification device 1 includes a control unit 11, a storage unit 12, a communication unit 13, a probe interface (I / F) 14, and a communication unit 13.
  • the control unit 11 includes a CPU (Central Processing Unit).
  • the CPU of the control unit 11 includes one or a plurality of large scale integrated circuits (LSIs).
  • LSIs large scale integrated circuits
  • the plurality of LSIs cooperate to realize the function of the CPU.
  • the CPU of the control unit 11 can read out an application including one or a plurality of programs stored in the storage unit 12 and execute various processes.
  • the application can be transferred in a state of being recorded on a recording medium such as a CD-ROM or a DVD-ROM, or can be transferred by downloading from a computer device such as a server computer.
  • the storage unit 12 includes a flash memory, an EEPROM (Electrically Erasable Programmable Read Only Memory), or a nonvolatile memory element such as a ROM, and a volatile memory element such as a RAM (Random Access Memory).
  • the nonvolatile memory element has a storage area for storing an application or data necessary for executing the application.
  • the volatile memory element has a storage area for storing an update program to be described later.
  • the storage unit 12 stores a database 121.
  • the database 121 stores reference electromagnetic wave waveforms used for detection processing.
  • the communication unit 13 communicates with the input device 3 under the control of the control unit 11 according to the execution of the application, and instructs the input device 3 to input a control signal to the test device D.
  • the probe I / F 14 receives a signal transmitted from the probe 5 to the verification device 1.
  • the signal received by the probe I / F 14 is converted into a digital signal by the AD converter 16 and input to the control unit 11. Note that the probe I / F 14 and the AD converter 16 can be substituted by connecting a commercially available oscilloscope to the verification apparatus 1.
  • FIG. 1 further shows a method for detecting an abnormal operation in the test device D, which is an example of a verification method in the verification system 100.
  • the abnormal operation in the test device D is detected based on electromagnetic waves leaking from the test device D operating in accordance with the input control signal.
  • the input device 3 according to the control of the verification device 1 inputs a communication frame F, which is a control signal, to the ECU that is the test device D (step S1).
  • the ECU executes an operation instructed by the input communication frame F.
  • the probe 5 is brought close to the ECU to a range where electromagnetic waves leaking from the ECU can be received.
  • the probe 5 may be fixed, and the ECU that is the test device D may be arranged in a range in which the leaking electromagnetic wave can be received with respect to the probe 5.
  • the probe 5 may be plate-shaped, and the test device D may be disposed on the plate-shaped probe 5.
  • Probe 5 receives electromagnetic waves leaking from the ECU (step S2).
  • a signal indicating the received electromagnetic wave is input from the probe 5 to the verification apparatus 1.
  • the verification device 1 detects an abnormal operation of the ECU by executing an abnormal operation detection process using a signal indicating the electromagnetic wave input from the probe 5 (step S3).
  • the detection result is notified to the user, for example, by being displayed on the display unit 15 of the verification device 1.
  • the application stored in the storage unit 12 of the verification apparatus 1 is a program that causes the control unit 11 to execute processing for detecting abnormal operation in the test device D using a signal indicating the electromagnetic wave input from the probe 5. .
  • FIG. 3 is a block diagram showing an abnormal operation detection process executed by the control unit 11.
  • the abnormal operation detection process includes a reading process 111, a detection process 112, and a display process 114.
  • Read processing 111 is processing in which the control unit 11 reads the reference electromagnetic wave waveform from the database 121.
  • the control unit 11 reads the reference electromagnetic wave waveform in accordance with the type of ECU used as the test device D and the attribute of the manufacturer.
  • the control unit 11 reads the reference electromagnetic wave waveform in accordance with a control signal input from the input device 3 to the test device D.
  • FIG. 4 is a diagram illustrating a specific example of a normal electromagnetic wave database 121A, which is an example of the database 121 stored in the storage unit 12 in the present embodiment.
  • a normal electromagnetic wave database 121A for each CAN message input from the input device 3 to the ECU that is the test device D, an electromagnetic wave waveform leaking from the ECU that normally operates in accordance with the CAN message (normal time) Electromagnetic wave waveform) is stored.
  • the electromagnetic wave waveforms for each input CAN message are shown for the identification numbers 1 to 4.
  • the database 121 can store an electromagnetic wave waveform for a CAN message (normal CAN message) in which the ECU performs some operation and an electromagnetic wave waveform for a CAN message (other CAN message) that the ECU does not originally operate.
  • a CAN message normal CAN message
  • a CAN message other CAN message
  • “AAABBBCC”, “DDDEEEFF”, and “GGGHHHII” are categorized and registered as normal CAN data
  • JJJJKKLL is categorized and registered as other CAN messages. Is done.
  • the detection process 112 is a process for detecting an abnormal operation of the ECU.
  • the detection process 112 includes a comparison process 113, and the control unit 11 executes the comparison process 113 in the detection process 112.
  • the comparison process 113 is a process for comparing the reference electromagnetic wave waveform read from the database 121 by the reading process 111 and the electromagnetic wave waveform (hereinafter also referred to as a measurement waveform) indicated by the signal input from the probe 5.
  • a general pattern matching method may be employed.
  • the control unit 11 calculates a correlation coefficient between the reference electromagnetic wave waveform and the measurement waveform. That the correlation coefficient is equal to or greater than the threshold means that the measured waveform matches the reference electromagnetic wave waveform. Conversely, a correlation coefficient less than the threshold means that the measured waveform does not match the reference electromagnetic wave waveform.
  • the reference electromagnetic wave waveform is a normal electromagnetic wave waveform
  • the measured waveform matches the electromagnetic wave waveform at the normal time, it means that the ECU which is the test device D performs a normal operation, that is, no abnormal operation occurs.
  • the fact that the measured waveform does not match the normal electromagnetic wave waveform means that the ECU is not operating normally, that is, an abnormal operation is occurring. Therefore, the comparison process 113 in the first embodiment is a process for specifying whether or not there is an abnormal operation in the test device D.
  • the display process 114 is a process for displaying the detection result in the detection process 112 on the display unit 15.
  • a process for displaying at least one detection result of the presence or absence of an abnormal operation on the display unit 15 is performed.
  • FIG. 5 is a flowchart showing an example of the flow of the abnormal operation detection process executed by the control unit 11 in the present embodiment.
  • control unit 11 receives an input of a signal indicating an electromagnetic wave from probe 5 (step S ⁇ b> 101). From the input signal, an electromagnetic wave waveform (waveform A) which is a measurement waveform is specified.
  • control unit 11 reads the corresponding normal electromagnetic wave waveform (waveform B) as the reference electromagnetic wave waveform from the normal electromagnetic wave database 121A (step S103).
  • step S103 a normal electromagnetic wave waveform corresponding to the CAN message input from the input device 3 to the test device D is read.
  • the control unit 11 identifies the presence or absence of abnormal operation in the test device D by comparing the waveforms A and B (step S105). That is, when the waveform A that is the measurement waveform matches the waveform B that is the reference electromagnetic wave waveform (YES in step S107), the control unit 11 indicates that no abnormal operation is detected as a detection result of the abnormal operation of the test device D. A message indicating “normal” is displayed on the display unit 15 (step S109).
  • the control unit 11 displays, as the detection result of the abnormal operation of the test device D, a message indicating “abnormal” indicating that the abnormal operation has been detected. (Step S111).
  • the operation of the test device D is captured by electromagnetic waves. Therefore, even if the test device D is a device that does not output by operation, such as an ECU, or a device that does not have a function of performing output by operation, a hardware simulator other than the test device D is not required. In addition, the operation can be easily detected. Thereby, an apparatus structure can be made easy and an increase in cost can be suppressed. In addition, since it is not necessary to verify the identity of the operation of the test device D and the hardware simulator, the detection work can be simplified.
  • the presence / absence of the abnormal operation of the test device D is detected because the presence / absence of the abnormal operation of the test device D is detected by comparing the detected electromagnetic wave waveform with the normal-time electromagnetic wave waveform. Can be detected easily and with high accuracy.
  • the database 121 may store an abnormal electromagnetic wave waveform instead of the normal electromagnetic wave waveform.
  • the control unit 11 of the verification apparatus 1 detects an abnormal operation by the reverse detection method described above. That is, the control unit 11 detects abnormal operation when the electromagnetic wave waveform measured from the test device D input from the probe 5 includes a waveform that matches the abnormal electromagnetic wave waveform that is the reference electromagnetic wave waveform, and does not include it. In such a case, an abnormal operation is not detected, that is, a normal operation is detected. That is, the verification of the operation of the test device D may be to detect the normal operation of the test device D.
  • the database 121 stores at least one of the normal-time electromagnetic wave waveform and the abnormal-time electromagnetic wave waveform, or both as the reference electromagnetic wave waveform, and the control unit 11 determines the measured electromagnetic wave waveform and the reference electromagnetic wave waveform.
  • the presence / absence of an abnormal operation and / or the presence / absence of a normal operation can be easily detected with high accuracy.
  • the abnormal electromagnetic waveform is stored in the database 121, the normal CAN message and other CAN messages may not be categorized.
  • the verification of the operation of the test device D is not limited to the detection of the presence / absence of abnormal operation and / or the presence / absence of normal operation.
  • the verification of the operation of the test device D may be detection of the type of abnormal operation.
  • the database 121 stored in the storage unit 12 is an abnormal electromagnetic wave database 121B as shown in FIG.
  • FIG. 6 shows another example of the database 121 stored in the storage unit 12 and shows a specific example of the abnormal electromagnetic wave database 121B.
  • the electromagnetic wave database 121B at the time of abnormality stores an electromagnetic wave waveform (abnormality electromagnetic wave waveform) leaked from the ECU when an abnormality occurs for each type of abnormality.
  • an error to perform division by zero division by zero
  • an error to calculate a negative square root negative square root
  • an error due to a buffer overflow buffer overflow
  • expiration of the watchdog timer The electromagnetic wave waveform when each of these occurs is stored. Note that the timing of FIG. 6 is not used in the second embodiment, but will be described in a fourth embodiment to be described later.
  • FIG. 7 is a flowchart showing an example of the flow of the abnormal operation detection process executed by the control unit 11 in the present embodiment.
  • control unit 11 receives an input of a signal indicating an electromagnetic wave from probe 5 (step S201). From the input signal, an electromagnetic wave waveform (waveform A) which is a measurement waveform is specified.
  • the control unit 11 reads out each abnormal electromagnetic wave waveform (waveform B) stored in the abnormal electromagnetic wave database 121B (step S203), and uses the electromagnetic wave waveform (waveform A) input in step S201 for each abnormal time.
  • an abnormal electromagnetic wave waveform that matches the waveform A is specified (step S205). That is, the type of the corresponding abnormality is specified.
  • step S201 When there is no abnormal electromagnetic wave waveform that matches the electromagnetic wave waveform (waveform A) input in step S201 (NO in step S207), the control unit 11 determines that the abnormal operation is “database” as the detection result of the abnormal operation of the test device D. A message indicating “abnormality not existing in” is displayed on the display unit 15 (step S209). In step S209, a normal message indicating that no abnormal operation is detected may be output.
  • the control unit 11 displays a message indicating the type of abnormality corresponding to the abnormal electromagnetic wave waveform as a detection result of the abnormal operation of the test device D. Is displayed on the display unit 15 (step S211).
  • the type of abnormal operation of the test device D is easily detected because the type of abnormal operation of the test device D is detected by comparing the detected electromagnetic wave waveform and the electromagnetic wave waveform at the time of abnormality. In addition, it can be detected with high accuracy.
  • the reference electromagnetic wave waveform stored in the abnormal electromagnetic wave database 121B may be an electromagnetic wave waveform for each type of abnormality for each ECU type or manufacturer. This is because different types of ECUs and manufacturers have different ways of generating an abnormality, and electromagnetic wave waveforms may be different even with the same type of abnormality.
  • FIG. 8 is another example of the database 121 stored in the storage unit 12, and is a diagram showing a specific example of the abnormal electromagnetic wave database 121C.
  • the abnormal electromagnetic wave database 121C an input of a CAN message is assumed for each attribute (manufacturer) of the ECU that is the test device S from the input device 3 and each CAN message input to the ECU.
  • the cause (type) of abnormal operation and the determination result (NG) are stored.
  • the electromagnetic wave waveform for each input CAN message and the type of abnormal operation when the electromagnetic wave waveform is generated for the ECU manufactured by company A, and the determination result are NG. ,It is shown.
  • FIG. 9 is a flowchart showing an example of the flow of the abnormal operation detection process executed by the control unit 11 in the present embodiment.
  • control unit 11 receives an input of a signal indicating an electromagnetic wave from probe 5 (step S301). From the input signal, an electromagnetic wave waveform (waveform A) which is a measurement waveform is specified.
  • the control unit 11 reads the abnormal electromagnetic wave waveform (waveform B) corresponding to the type of the test device as the reference electromagnetic wave waveform from the abnormal electromagnetic wave database 121C (step S303).
  • the type of the test device is specified by a user input to the control unit 11, for example.
  • an abnormal electromagnetic wave waveform stored as an abnormal wave waveform assumed at the time of CAN message input according to the CAN message input from the input device 3 to the test device D is read.
  • a plurality of abnormal electromagnetic wave waveforms may be read.
  • the control unit 11 identifies the presence or absence of an abnormal operation assumed in the test device D by comparing the waveforms A and B (step S305). That is, when the waveform A that is the measurement waveform matches the waveform B that is the reference electromagnetic wave waveform (YES in step S307), the control unit 11 detects that the abnormal operation is detected as the detection result of the abnormal operation of the test device D. Is displayed on the display unit 15 (step S309).
  • step S305 the control unit 11 determines the electromagnetic wave waveform (waveform A) and each waveform.
  • the abnormal electromagnetic wave waveform matching the waveform A is specified. That is, a corresponding abnormality type is specified from a plurality of assumed abnormality types.
  • the control unit 11 detects the abnormal operation as a result of the abnormal operation of the test device D. A message indicating the type of abnormality corresponding to the electromagnetic wave waveform is displayed on the display unit 15 (step S309).
  • step S307 when there is no abnormal electromagnetic wave waveform that matches the waveform A (NO in step S307), the control unit 11 indicates that the abnormal operation is “abnormality not existing in the database” as the detection result of the abnormal operation of the test device D. Is displayed on the display unit 15 (step S311). In step S311, a normal message indicating that no abnormal operation is detected may be output.
  • the detected electromagnetic wave waveform and the prepared electromagnetic wave waveform are prepared by preparing in advance an electromagnetic wave waveform corresponding to the type of abnormal operation assumed for each input control signal.
  • the type of abnormal operation of the test device D can be detected in more detail.
  • the abnormal electromagnetic wave waveform for each type of test device D is stored in the database 121, but in combination with the first embodiment, the normal time for each type of test device D
  • the electromagnetic wave waveform may be stored in the database 121. In this case, the presence or absence of abnormal operation can be detected with high accuracy according to the type of the test device D.
  • the database 121 may further store information defining the comparison timing.
  • information defining the timing for comparison with the abnormal electromagnetic wave waveform is stored in the abnormal electromagnetic wave waveform database 121 ⁇ / b> B.
  • the timing of the comparison is, for example, an elapsed time from the input of the control signal, an elapsed time from the time when a specified feature point of the electromagnetic wave waveform input from the probe 5 is detected, and the like.
  • Information defining the timing of comparison may be stored in a database different from the database 121.
  • the time from when the control signal is input to when the operation according to the control signal (for example, division) is started is defined by the test device D software. Therefore, the time is known in advance by the software designer.
  • the comparison timing defined by the information stored in the database 121 is based on the above time. For example, the timing of comparison is when the above time has elapsed since the input of the control signal, or when a time shorter than the above time by a predetermined period since the input of the control signal. In the former case, the comparison timing is the timing at which the operation according to the control signal is started. In the latter case, the comparison timing is slightly before the operation according to the control signal is started.
  • the control unit 11 of the verification apparatus 1 obtains the electromagnetic wave waveform obtained from the signal at the defined timing among the signals input from the probe 5. That is, the presence / absence of abnormal operation and / or abnormality is determined only by comparing the waveform of the electromagnetic wave waveform measured from the test device D at the position defined by the database 121 and the waveform stored in the database 121. The type of operation can be specified.
  • the control unit 11 of the verification apparatus 1 determines whether or not the position where the waveform that matches the reference electromagnetic wave waveform appears among the electromagnetic wave waveforms input from the probe 5 is a specified position. The presence / absence of an operation and / or the type of abnormal operation can be specified. In this case, for each position of the electromagnetic wave waveform input from the probe 5, a database (normal electromagnetic wave waveform database 121A or abnormal electromagnetic wave waveform database 121B, 121C) storing a reference electromagnetic wave waveform used for comparison processing is prepared. May be. In this case, the control unit 11 of the verification apparatus 1 reads the reference electromagnetic wave waveform from the corresponding database and uses it for the comparison process for each position used for the comparison process among the electromagnetic wave waveforms input from the probe 5.
  • the abnormal operation of the test device can be detected with high accuracy if the timing at which the electromagnetic wave waveform is output differs depending on the abnormal operation. it can.
  • Side channel information is not limited to electromagnetic waves.
  • the power consumption of the test device D may be used.
  • the verification system 100 includes a measuring device for measuring the current value instead of the probe 5.
  • the measuring instrument is, for example, a device that can be inserted into a power supply line of the test device D, and can measure a current value by detecting a voltage difference between both ends of a minute resistance.
  • the database 121 stores a change in power consumption per unit time while the test device D is operating as a reference.
  • the control unit 11 of the verification apparatus 1 calculates a change in power consumption per unit time in the test device D by multiplying the current value obtained from the measuring instrument by a voltage value acquired in advance.
  • the control unit 11 compares the calculated change in the power consumption with the change in the reference current value stored in the database 121 and the change in the measured current value, so that the presence or absence of abnormal operation, or Detect the type of abnormal operation.
  • the side channel information is at least one of electromagnetic waves leaking from the test device D and power consumption of the test device D.
  • the side channel information may be other than electromagnetic waves and power consumption.
  • the side channel information may be heat (temperature) or sound (frequency, volume) generated during the operation of the test device D.
  • the side channel information is not limited to electromagnetic waves, but can be captured by other information. Thereby, the abnormal operation of the test device D can be detected with high accuracy by a simple apparatus.
  • the leaked electromagnetic wave waveform differs for each device (memory, CPU, etc.) mounted on the test device D.
  • the probe 5 is disposed at a position corresponding to a device that detects the presence or absence of abnormal operation in the test device D.
  • FIG. 14 is a diagram for explaining an example of the arrangement of the probes 5.
  • the test device D includes, as an example, a CPU 51, a memory 52, and an interface (I / F) 53.
  • FIG. 14 exemplifies a configuration in which the CPU 51, the memory 52, and the I / F 53 chip are arranged on one substrate.
  • the CPU 51 when the coordinates are set with the substrate surface as the XY plane, the CPU 51 is arranged in the second quadrant, the memory 52 is arranged in the third quadrant, and the I / F 53 is arranged in the fourth quadrant.
  • the probe 5 When detecting the presence or absence of abnormal operation of the CPU 51 of the test device D, the probe 5 is placed in the second quadrant position 5A to receive the leaking electromagnetic wave. When detecting the presence or absence of abnormal operation of the memory 52, the probe 5 is placed in the position 5B of the third quadrant to receive the leaking electromagnetic wave. When detecting the presence / absence of abnormal operation of the I / F 53, the probe 5 is placed in the fourth quadrant position 5C to receive the leaking electromagnetic wave.
  • the database 121 stores a reference electromagnetic wave waveform corresponding to a device that detects the presence or absence of abnormal operation.
  • the database which stores a reference electromagnetic wave waveform according to the device which detects the presence or absence of abnormal operation may be prepared.
  • FIG. 15 is a diagram illustrating another example of the database 121 stored in the storage unit 12 and an example of a normal electromagnetic wave database 121D for each position with respect to the test device D.
  • the normal-time electromagnetic wave database 121 ⁇ / b> D has an electromagnetic wave waveform that leaks from a normally operating device (normal time) for each position corresponding to the device that detects the presence or absence of abnormal operation in the test device D.
  • Electromagnetic wave wave waveform is stored.
  • the normal-time electromagnetic wave waveforms of the CPU 51, the memory 52, and the I / F 53 are stored.
  • illustration of each concrete electromagnetic wave waveform is abbreviate
  • FIG. 16 is a flowchart showing an example of the flow of the abnormal operation detection process executed by the control unit 11 in the present embodiment.
  • control unit 11 accepts selection of a position (for example, one of the first quadrant to the fourth quadrant) according to a device that detects presence / absence of abnormal operation in test device D.
  • a position for example, one of the first quadrant to the fourth quadrant
  • a user input from an input device may be received.
  • a detector not shown
  • the detection result of the position of the probe 5 for example, any one of positions 5A to 5C
  • Input may be accepted.
  • step S400 After the position is selected in step S400, the same processing as steps S101 to S111 in FIG. 5 is performed. That is, the control unit 11 receives an input of a signal indicating an electromagnetic wave from the probe 5 (step S401), and compares the input electromagnetic wave waveform with a reference electromagnetic wave waveform stored in the normal time electromagnetic wave database 121D (step S403). , S405). In the present embodiment, in step S403, the control unit 11 reads the reference electromagnetic wave waveform corresponding to the selected position from the normal electromagnetic wave database 121D. The control unit 11 outputs “normal” when the input electromagnetic wave waveform matches the reference electromagnetic wave waveform, and outputs “abnormal” when it does not (steps S407 to S411).
  • the reference electromagnetic wave waveform is a normal electromagnetic wave waveform.
  • the reference electromagnetic wave waveform may be an abnormal electromagnetic wave waveform.
  • “abnormal” is output when the input electromagnetic wave waveform matches the reference electromagnetic wave waveform corresponding to the selected position. Is done.
  • the configuration of the test device D is not limited to the configuration of FIG.
  • the verification system 100 by using the side channel information detected from the position corresponding to the apparatus that detects the presence or absence of abnormal operation in the test device D, it is more accurate and more detailed. An abnormal operation of the test device D can be detected.
  • FIG. 17 is a block diagram illustrating an abnormal operation detection process executed by the control unit 11 of the verification apparatus 1 according to the seventh embodiment.
  • the detection process 112 includes a determination process 116 instead of the comparison process 113 illustrated in FIG.
  • the determination process 116 is a process for determining whether the measurement waveform is a normal electromagnetic wave waveform or an abnormal electromagnetic wave waveform using the classifier 122.
  • a classifier 122 is stored in the storage unit 12 of the verification device 1 according to the seventh embodiment.
  • the classifier 122 is a learning model learned to output a normal-time electromagnetic wave waveform or an abnormal-time electromagnetic wave waveform when an electromagnetic wave waveform is given. As shown in FIG. 17, the classifier 122 includes a first model 122A and a second model 122B.
  • the first model 122A is learned using normal-time electromagnetic wave waveforms with respect to a plurality of CAN messages. When a certain electromagnetic wave waveform is given, the first model 122A is machine-learned to output information indicating the CAN message corresponding to the electromagnetic wave waveform. It is a learning model.
  • FIG. 18 is a diagram schematically showing the structure of the classifier 122.
  • the first model 122A is a one-dimensional convolutional neural network (1D-CNN).
  • the first model 122A may be another deep learning model obtained by executing deep learning using a plurality of the above combinations.
  • the first model 122A includes an input layer 61 that receives an input of an electromagnetic wave waveform, a convolution layer 62, a pooling layer 63, a total coupling layer 64, and an output layer 65.
  • the convolution layer 62 performs a filtering process on the input electromagnetic wave waveform to extract a feature amount.
  • the pooling layer 63 aggregates the feature values obtained by the convolution layer 62.
  • the total connection layer 64 combines the aggregated results in the pooling layer 63.
  • the output layer 65 outputs information indicating the corresponding CAN message that is the classification result based on the coupling result in the all coupling layer 64.
  • each of CAN messages M1 to M4 is input to the ECU serving as test device D (step S501).
  • the messages M1 to M4 are CAN messages for instructing operations such as vehicle speed acquisition, engine speed acquisition, and air flow meter value acquisition, for example.
  • the normal electromagnetic wave waveform from the test device D when each CAN message M1 to M4 is input is measured (step S503).
  • the normal-time electromagnetic wave waveforms a1 to a4 are measured with respect to the input of the CAN message M1.
  • the normal-time electromagnetic wave waveforms a1 to a4 measured with respect to the input of the CAN message M1 are set as input values to the first model 122A (step S505).
  • the first model 122A receives the normal-time electromagnetic wave waveforms a1 to a4 from the input layer 61, and processes the convolutional layer 62, the pooling layer 63, and the total coupling layer 64 for each input waveform. Through the processing in, the probabilities of the CAN messages M1 to M4 are output from the output layer 65.
  • a certain waveform (for example, a normal electromagnetic wave waveform a1) is input from the input layer 61, and the input waveform is classified into four classes of CAN messages M1 to M4.
  • the output is 95%, the probability of being a CAN message M2 is 5%, the probability of being a CAN message M3 is 3%, and the probability of being a CAN message M4 is 2%.
  • the coefficient of the function used in the calculation in each of the layers 62 to 64 is adjusted so as to increase the probability of the CAN message M1 corresponding to the inputted normal electromagnetic wave waveform a1 (step S507).
  • the total coupling layer is set so that the probability (95%) of the CAN message M1 output from the output layer 65 when the normal-time electromagnetic wave waveform a1 is input from the input layer 61 is higher.
  • the weighting factor at 64 is changed.
  • the first model 122A is learned by performing steps S501 to S507 of FIG. 18 for each of the CAN messages M1 to M4 for the combination of the CAN message and the normal electromagnetic wave waveform measured when the CAN message is input.
  • the accuracy of classifying the input electromagnetic wave waveform into corresponding CAN messages can be improved.
  • the second model 122B is a learning model that is machine-learned so as to output a determination result of whether the target electromagnetic wave waveform is a normal electromagnetic wave waveform or an abnormal electromagnetic wave waveform based on the feature quantity of the electromagnetic wave waveform.
  • the second model 122B is a classification algorithm such as One class SVM (Support Vector Vector Machine). In the example of FIG. 18, the second model 122B is One class SVM.
  • the second model 122B may be another deep learning model obtained by executing deep learning using a plurality of feature amounts obtained from the normal electromagnetic wave waveform.
  • the second model 122B is learned by a so-called transfer learning method as shown in FIG. 18 as an example. That is, learning of the first model 122A is the first-stage learning, and as the second-stage learning, the second model 122B is calculated from each electromagnetic wave waveform in the entire coupling layer 64 of the learned first model 122A.
  • the above-described feature quantity group of the electromagnetic wave waveform is given as an input value. Specifically, referring to FIG. 18, calculation is performed for a layer including all coupling layers 64 when a normal-time electromagnetic wave waveform (for example, waveform a ⁇ b> 1) in input layer 61 of learned first model 122 ⁇ / b> A is input.
  • the feature amount F is given to the second model 122B as an input value (step S509).
  • the layer having the total coupling layer 64 is, for example, the second or third layer from the pooling layer 63.
  • the second model 122B stores the feature value F input to the second model 122B in step S509 as normal values obtained from the normal electromagnetic wave waveform a1 of the CAN message M1. And the 2nd model 122B sets the boundary surrounding the input feature-value F as the identification boundary B which is a boundary of the normal value and abnormal value of the feature-value from the measured electromagnetic wave waveform about the CAN message M1. (Step S511).
  • the second model 122B is learned and set for each CAN message by performing steps S509 and S511 of FIG. 18 for each of the CAN messages M1 to M4 with respect to each normal electromagnetic wave waveform measured when the CAN message is input.
  • the accuracy of the identification boundary B can be improved.
  • the classifier 122 may be stored in the storage unit 12 in advance.
  • the control unit 11 further executes a learning process 115 for creating the classifier 122.
  • the learning process 115 is a process represented by steps S501 to S511. In the learning process 115, at least a part of steps S501 to S511 may be executed by another device.
  • the determination process 116 inputs a measured waveform to the learned classifier 122, and determines from the output value whether it is a normal electromagnetic wave waveform or an abnormal electromagnetic wave waveform. That is, when the measured waveform is input to the classifier 122, the learned first model 122A calculates the feature value and gives it to the second model 122B. The learned second model 122B calculates the divergence between the identification boundary B set by learning and the feature value, and outputs information indicating the determination result of whether the electromagnetic wave waveform at normal time is the electromagnetic wave waveform at abnormal time based on the divergence. . The output information is once written in the storage unit 12.
  • the divergence may be, for example, data indicating whether the input feature quantity is within or outside the range surrounded by the identification boundary B.
  • the determination process 116 reads the output value from the classifier 122 from the storage unit 12 and detects whether the measurement waveform is a normal electromagnetic wave waveform or an abnormal electromagnetic wave waveform based on the value. For example, when it is within the range surrounded by the identification boundary B, it is determined that the electromagnetic wave waveform is normal, that is, normal operation, and when it is outside the above range, it is determined that the electromagnetic wave waveform is abnormal, that is, the operation is abnormal. The determination result is once written in the storage unit 12.
  • the measurement waveform and the verification apparatus 1 are used. Even if random noise is included in the reference electromagnetic wave waveform, it becomes possible to detect with high accuracy. Further, even when an interrupt process or the like occurs during execution of a program of a device to be verified (for example, an ECU), the feature point interval of the measurement waveform can be detected with high accuracy.
  • the machine learning model shown in the classifier 122 may be an auto encoder as another example.
  • the auto encoder is a learning model that is machine-learned using the feature quantity obtained by compressing the input waveform and reducing it after reducing the dimension, and the feature quantity of the input waveform. This is a machine-learned model.
  • the learned auto encoder outputs a waveform that is almost the same as the input waveform. Therefore, when an unlearned waveform, that is, an abnormal electromagnetic wave waveform that is not a normal electromagnetic wave waveform is given as an input value, substantially the same waveform is not output. Thereby, it determines with it being an electromagnetic wave waveform at the time of abnormality.
  • FIG. 10 is a diagram illustrating a measurement result of Example 1 in which the inventors verified the operation of the test device D using the verification apparatus 1 of the present application.
  • a commercially available head-up display was used as the test device D.
  • 10A is a control signal (CAN message) input to the test device D
  • FIG. 10B is an electromagnetic wave waveform measured by the probe 5 from the test device D.
  • 11 is an enlarged view of a part (dotted line part) of FIG. 10
  • FIG. 12 is an enlarged view of a part (solid line part) of FIG.
  • FIG. 11 is an enlarged view of the CAN message F6 and the electromagnetic wave waveform when the CAN message F6 is input, which is a range surrounded by a dotted line in FIG.
  • the time after the elapse of t [us] from the input of the CAN message F6 is defined as the comparison timing. Therefore, the control unit 11 of the verification apparatus 1 compares the electromagnetic wave waveform (comparison waveform) of the portion surrounded by the solid line in FIG. 11 with the reference electromagnetic wave waveform stored in the database 121.
  • FIG. 12 is an enlarged view of the comparative waveform of FIG.
  • the control unit 11 of the verification device 1 compares the comparison waveform with the reference electromagnetic wave waveform, and detects that the abnormal waveform AW1 is included in the comparison waveform.
  • the head-up display used as the test device D in the measurement of Example 1 ends abnormally due to abnormal operation.
  • abnormal operation was detected using the electromagnetic wave waveform input from the probe 5 before abnormal termination due to abnormal operation. Therefore, it was verified that the operation of the test device D can be verified with high accuracy using the verification apparatus 1.
  • FIG. 13 is a diagram illustrating a measurement result of Example 2 in which the inventors verified the operation of the test device D using the verification apparatus 1 of the present application.
  • an arithmetic unit equipped with software is used as the test device D, and a control signal for performing an operation for dividing by a number other than zero (normal division) is input, and an operation for dividing by zero (division by zero) ) Is input when a control signal is input.
  • FIGS. 13A and 13B show the measurement results of the electromagnetic wave waveform of the test device D in each.
  • the arithmetic unit used as the test device D executes division according to the control signal from the normal state (the state where division is not executed), and returns to the normal state when the division is completed.
  • the database 121 stores the electromagnetic wave waveform being divided as the reference electromagnetic wave waveform
  • the control unit 11 of the verification apparatus 1 patterns the electromagnetic wave waveform measured from the input point of the control signal with the reference electromagnetic wave waveform. Match.
  • an abnormal waveform AW2 indicating that the division is being executed is detected for the period At2 from the input time of the control signal.
  • An electromagnetic wave waveform W2 indicating a normal state was detected after the passage of At2.
  • the period At2 was 1.0 [ ⁇ s].
  • the electromagnetic wave waveform indicating that the division is being performed is continued for a period At2 shorter than the period At1 indicating the normal division execution time, and thereafter the electromagnetic wave waveform W2 is detected, whereby the test device Division by zero at D, ie, an abnormal operation was detected.
  • the test device D As described above, in Example 2, division by zero, that is, a specific abnormal operation was detected by the test device D using the electromagnetic wave waveform input from the probe 5. Therefore, it was verified that the operation of the test device D can be verified using the verification apparatus 1 according to the position where the electromagnetic wave waveform that matches the reference electromagnetic wave waveform among the electromagnetic wave waveforms input from the probe 5 appears.

Landscapes

  • Engineering & Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Quality & Reliability (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Test And Diagnosis Of Digital Computers (AREA)
  • Tests Of Electronic Circuits (AREA)

Abstract

L'invention concerne un procédé de vérification qui comprend : une étape S2 qui consiste à recevoir des informations de canal latéral en provenance d'un dispositif de test auquel des données frelatées sont données ; et une étape S3 qui consiste à vérifier un fonctionnement du dispositif de test sur la base des informations de canal latéral reçues.
PCT/JP2019/008154 2018-03-02 2019-03-01 Procédé de vérification, dispositif de vérification, programme de vérification et système de vérification Ceased WO2019168167A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
JP2019572255A JPWO2019168167A1 (ja) 2018-03-02 2019-03-01 検証方法、検証装置、コンピュータプログラム、及び、検証システム

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2018037058 2018-03-02
JP2018-037058 2018-03-02

Publications (1)

Publication Number Publication Date
WO2019168167A1 true WO2019168167A1 (fr) 2019-09-06

Family

ID=67805448

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2019/008154 Ceased WO2019168167A1 (fr) 2018-03-02 2019-03-01 Procédé de vérification, dispositif de vérification, programme de vérification et système de vérification

Country Status (2)

Country Link
JP (1) JPWO2019168167A1 (fr)
WO (1) WO2019168167A1 (fr)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021089659A1 (fr) * 2019-11-06 2021-05-14 Robert Bosch Gmbh Procédé de détermination d'un écart inadmissible du comportement du système d'un dispositif technique par rapport à une plage de valeurs standard
CN113630235A (zh) * 2021-08-06 2021-11-09 深圳技术大学 一种侧信道分析及其模型构建的方法与装置
EP4002768A1 (fr) * 2020-11-20 2022-05-25 Institute for Information Industry Appareil et procédé de test à données aléatoires
JP2022138170A (ja) * 2021-03-10 2022-09-26 矢崎総業株式会社 評価装置
CN119246971A (zh) * 2024-12-03 2025-01-03 中国人民解放军国防科技大学 一种基于深度学习的电磁环境监测系统及方法

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11582060B2 (en) 2020-08-21 2023-02-14 Geotab Inc. Telematics system for identifying manufacturer-specific controller-area network data
US11546427B2 (en) 2020-08-21 2023-01-03 Geotab Inc. Method and system for collecting manufacturer-specific controller-area network data
US11212135B1 (en) 2020-08-21 2021-12-28 Geotab Inc. System for identifying manufacturer-specific controller-area network data
CN116527796B (zh) * 2023-07-04 2024-02-13 北京前景无忧电子科技股份有限公司 一种基于双模通信对电表高精度授时的方法

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160342791A1 (en) * 2015-05-22 2016-11-24 Power Fingerprinting Inc. Systems, methods, and apparatuses for intrusion detection and analytics using power characteristics such as side-channel information collection
JP2017214049A (ja) * 2016-05-27 2017-12-07 ローベルト ボッシュ ゲゼルシャフト ミット ベシュレンクテル ハフツング セキュリティ検査システム、セキュリティ検査方法、機能評価装置、及びプログラム
US20180011130A1 (en) * 2016-07-06 2018-01-11 Power Fingerprinting Inc. Methods and apparatuses for characteristic management with side-channel signature analysis

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH02219109A (ja) * 1989-02-20 1990-08-31 Nippondenso Co Ltd 電子制御回路診断装置
JP2003150406A (ja) * 2001-11-15 2003-05-23 Nec Infrontia Corp コンピュータシステムにおける障害予防システム
CN1261319C (zh) * 2004-11-11 2006-06-28 北京电巴科技有限公司 一种电动公交系统
EP2144166A4 (fr) * 2007-03-29 2011-01-12 Nec Corp Système de diagnostic
JP2010135881A (ja) * 2008-12-02 2010-06-17 Nec Corp サイドチャネル攻撃耐性評価装置、サイドチャネル攻撃耐性評価方法、サイドチャネル攻撃耐性評価プログラム
JP5691723B2 (ja) * 2011-03-25 2015-04-01 富士通株式会社 監視方法、情報処理装置および監視プログラム
JP6559600B2 (ja) * 2016-03-17 2019-08-14 株式会社東芝 情報処理装置、情報処理プログラムおよび検査システム
US20180013779A1 (en) * 2016-07-06 2018-01-11 Power Fingerprinting Inc. Methods and apparatuses for integrity validation of remote devices using side-channel information in a power signature analysis
JP2019008718A (ja) * 2017-06-28 2019-01-17 株式会社東芝 診断装置

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160342791A1 (en) * 2015-05-22 2016-11-24 Power Fingerprinting Inc. Systems, methods, and apparatuses for intrusion detection and analytics using power characteristics such as side-channel information collection
JP2017214049A (ja) * 2016-05-27 2017-12-07 ローベルト ボッシュ ゲゼルシャフト ミット ベシュレンクテル ハフツング セキュリティ検査システム、セキュリティ検査方法、機能評価装置、及びプログラム
US20180011130A1 (en) * 2016-07-06 2018-01-11 Power Fingerprinting Inc. Methods and apparatuses for characteristic management with side-channel signature analysis

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021089659A1 (fr) * 2019-11-06 2021-05-14 Robert Bosch Gmbh Procédé de détermination d'un écart inadmissible du comportement du système d'un dispositif technique par rapport à une plage de valeurs standard
EP4002768A1 (fr) * 2020-11-20 2022-05-25 Institute for Information Industry Appareil et procédé de test à données aléatoires
JP2022138170A (ja) * 2021-03-10 2022-09-26 矢崎総業株式会社 評価装置
JP7289864B2 (ja) 2021-03-10 2023-06-12 矢崎総業株式会社 評価装置
CN113630235A (zh) * 2021-08-06 2021-11-09 深圳技术大学 一种侧信道分析及其模型构建的方法与装置
CN113630235B (zh) * 2021-08-06 2023-07-25 深圳技术大学 一种侧信道分析及其模型构建的方法与装置
CN119246971A (zh) * 2024-12-03 2025-01-03 中国人民解放军国防科技大学 一种基于深度学习的电磁环境监测系统及方法

Also Published As

Publication number Publication date
JPWO2019168167A1 (ja) 2020-04-16

Similar Documents

Publication Publication Date Title
WO2019168167A1 (fr) Procédé de vérification, dispositif de vérification, programme de vérification et système de vérification
CN102566421B (zh) 机器状态监控中异常检测的条件相关性建模的系统和方法
CN104598342B (zh) 内存的检测方法和装置
CN112884697B (zh) 晶圆图的辨识方法与计算机可读取记录介质
TWI878492B (zh) 微控制器程式指令執行之指紋化及入侵偵測
US11528152B2 (en) Watermarking for electronic device tracking or verification
CN110954152A (zh) 提供用于传感器集成电路的补偿参数
EP2135144B1 (fr) Surveillance de l'état d'une machine à l'aide de règles à motifs
CN111198815B (zh) 用户界面的兼容性测试方法及装置
CN111508588A (zh) 训练装置、训练方法和计算机可读记录介质
CN100458457C (zh) 检查电路板及其设备的方法、设备和系统
CN118625244B (zh) 电流互感器的性能评估方法及系统
CN111507934B (zh) 训练设备、训练方法和计算机可读记录介质
CN118534291B (zh) 一种用于测试电源芯片的方法、系统、设备及存储介质
US20220398318A1 (en) Apparatuses, methods, and computer programs for detecting an impostor system
JP6494887B1 (ja) 検査装置、検査方法及び検査プログラム
JP7323202B2 (ja) 検査制御装置、情報処理装置、検査制御方法及びプログラム
CN113407408A (zh) 数据传输规则验证方法、装置、设备和存储介质
JP4312799B2 (ja) 信頼性評価プログラムおよび信頼性評価装置
CN113704085A (zh) 用于检查技术系统的方法和设备
CN112286788A (zh) 用于验证对技术系统的仿真的方法和设备
US12204397B2 (en) Fault diagnosis apparatus, non-transitory computer-readable recording medium, and fault diagnosis method
JP7287093B2 (ja) 学習プログラム、学習方法および学習装置
EP4502848A1 (fr) Dispositif de simulation, procédé de commande du dispositif de simulation et programme de dispositif de simulation
CN119988131A (zh) 基于大数据的计算机设备温度识别方法及装置、存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19760857

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2019572255

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19760857

Country of ref document: EP

Kind code of ref document: A1