CN115225322B - Unmanned intelligent device safety constraint method based on environment side channel information verification - Google Patents
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Abstract
The invention relates to an unmanned intelligent device security constraint method based on environment side channel information verification, which comprises the following steps: a plurality of unmanned intelligent devices in a cooperative working scene respectively acquire side channel information of an environment in which the unmanned intelligent devices are located; each unmanned intelligent device filters the side channel information and classifies the filtered side channel information to obtain a classification result; verifying the classification result of the current unmanned intelligent device by using the classification result of the adjacent unmanned intelligent device to obtain a verification result; and judging whether the current unmanned intelligent device is in a normal working state according to the verification result. The method can realize the implicit and passive safety constraint of the unmanned intelligent device by utilizing the environment side channel information, does not need the active operation of a user, realizes the operation transparency, and meets the device authentication requirement of a plurality of unmanned intelligent devices in a self-organizing network under the cooperative working scene.
Description
Technical Field
The invention belongs to the technical field of identity authentication, and particularly relates to an unmanned intelligent device security constraint method based on environment side channel information verification.
Background
Identity authentication technology has been a hotspot of concern in the security arts. Along with the development of the Internet of things and the industrial Internet, intelligent objects can communicate with each other, things can be detected with each other, and things can interact with each other and also can interact with external environments. With the complexity of the scenario, the conventional authentication mechanism using the password is not in line with the device authentication requirement in the scenario of cooperative work of multiple unmanned intelligent devices in the ad hoc network. Thus, continuous implicit authentication techniques are considered to be a better alternative to continuous non-inductive security constraints for unmanned smart devices.
Currently, existing continuous implicit authentication techniques focus mainly on biometric-based authentication techniques. Xin et al propose an edge-based gait biometric identification, using a deep learning model to authenticate a user; wu et al propose a two-step authentication method based on a self-made fingertip sensor device, by capturing motion data (such as acceleration and angular velocity) and physiological data (such as photoplethysmographic PPG signal); liang et al propose an authentication method based on a multi-layer sensing algorithm, authentication is performed through sensor and touch screen data in a smart phone, and the like.
The continuous implicit authentication technologies achieve good authentication effects under the condition of man-machine interaction, but the accuracy of authentication results can be influenced by factors such as personal emotion, touch screen angle and the like, and interaction actions between a subject and an object are required to support, so that the continuous non-inductive security constraint is not suitable for continuous non-inductive security constraint of unmanned intelligent equipment; in addition, the authentication technology based on the biological characteristics requires additional equipment support and a large amount of fund support, has high cost, and is not suitable for the authentication scene of large-scale equipment.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides an unmanned intelligent device security constraint method based on environment side channel information verification. The technical problems to be solved by the invention are realized by the following technical scheme:
the embodiment of the invention provides an unmanned intelligent device security constraint method based on environment side channel information verification, which comprises the following steps:
a plurality of unmanned intelligent devices in a cooperative working scene respectively acquire side channel information of an environment in which the unmanned intelligent devices are located;
each unmanned intelligent device filters the side channel information and classifies the filtered side channel information to obtain a classification result;
verifying the classification result of the current unmanned intelligent device by using the classification result of the adjacent unmanned intelligent device to obtain a verification result;
and judging whether the current unmanned intelligent equipment is in a normal working state according to the verification result.
In one embodiment of the present invention, the side channel information includes one or more of sound information, temperature information, light information, behavior information, time information, and location information.
In one embodiment of the present invention, each of the unmanned intelligent devices filters the side channel information, classifies the filtered side channel information, and obtains a classification result, including:
each unmanned intelligent device utilizes a wavelet filtering method to filter the side channel information to obtain filtered side channel information;
and classifying the filtered side channel information by using a multi-classification method based on machine learning to obtain a classification result.
In one embodiment of the present invention, verifying the classification result of the current unmanned intelligent device by using the classification result of the adjacent unmanned intelligent device to obtain a verification result includes:
and verifying the real-time environment side channel information classification result of the current unmanned intelligent device by using the initial environment side channel information classification result of the adjacent unmanned intelligent device to obtain a verification result.
In one embodiment of the present invention, verifying the classification result of the current unmanned intelligent device by using the classification result of the adjacent unmanned intelligent device to obtain a verification result includes:
the current unmanned intelligent device sends the classification result of the current unmanned intelligent device to a plurality of adjacent unmanned intelligent devices respectively;
each adjacent unmanned intelligent device compares and verifies the classification result of the adjacent unmanned intelligent device with the received classification result of the current unmanned intelligent device; when the classification result of the adjacent unmanned intelligent equipment is consistent with the classification result of the current unmanned intelligent equipment, a first verification result is obtained; when the classification result of the adjacent unmanned intelligent device is inconsistent with the classification result of the current unmanned intelligent device, a second verification result is obtained;
and each adjacent unmanned intelligent device sends the first verification result or the second verification result to the current unmanned intelligent device.
In one embodiment of the present invention, verifying the classification result of the current unmanned intelligent device by using the classification result of the adjacent unmanned intelligent device to obtain a verification result includes:
the adjacent unmanned intelligent devices respectively send the classification results of the adjacent unmanned intelligent devices to the current unmanned intelligent device;
the current unmanned intelligent device compares and verifies the classification result of the current unmanned intelligent device with the received classification result of each adjacent unmanned intelligent device; when the classification result of the current unmanned intelligent device is consistent with the classification result of the adjacent unmanned intelligent device, a first verification result is obtained; and when the classification result of the current unmanned intelligent device is inconsistent with the classification result of the adjacent unmanned intelligent device, obtaining a second verification result.
In one embodiment of the present invention, determining whether the current unmanned intelligent device is in a normal working state according to the verification result includes:
the current unmanned intelligent device counts the number of the first verification results and the number of the second verification results, and judges whether the current unmanned intelligent device is in a normal working state or not according to the number of the first verification results and the number of the second verification results.
In one embodiment of the present invention, determining whether the current unmanned intelligent device is in a normal working state according to the number of the first verification results and the number of the second verification results includes:
when the number of the first verification results is larger than the number of the second verification results, the current unmanned intelligent device is in a normal working state;
when the number of the first verification results is smaller than or equal to the number of the second verification results, the current unmanned intelligent device is in an abnormal working state.
In one embodiment of the present invention, verifying the classification result of the current unmanned intelligent device by using the classification result of the adjacent unmanned intelligent device to obtain a verification result includes:
and verifying the classification result of the current unmanned intelligent device by using the classification result of any adjacent unmanned intelligent device in the unmanned intelligent device cluster formed by a plurality of trusted adjacent unmanned intelligent devices to obtain a first verification result or a second verification result.
In one embodiment of the present invention, determining whether the current unmanned intelligent device is in a normal working state according to the verification result includes:
when the verification result is the first verification result, the current unmanned intelligent device is in a normal working state; and when the verification result is the second verification result, the current unmanned intelligent device is in an abnormal working state.
Compared with the prior art, the invention has the beneficial effects that:
1. the security constraint method of the invention verifies between the adjacent unmanned intelligent device and the current unmanned intelligent device by utilizing the environment side channel information, can solve the problem of opaque user operation in the existing authentication mode, achieves the security constraint of the unmanned intelligent device by utilizing the environment side channel information implicitly and passively, does not need the active operation of the user, achieves the operation transparency, and meets the device authentication requirement under the cooperative work scene of a plurality of unmanned intelligent devices in the self-organizing network.
2. The safety constraint method of the invention continuously detects the working condition of the unmanned intelligent equipment by collecting the channel information of the environment side in real time and communicating the unmanned intelligent equipment with each other, thereby realizing continuous detection.
3. The security constraint method can be realized by utilizing a plurality of unmanned intelligent devices in a cooperative working scene, does not need additional device support and a large amount of fund support, has low cost, and is suitable for a scene of large-scale device authentication.
Drawings
Fig. 1 is a schematic flow chart of an unmanned intelligent device security constraint method based on environment side channel information verification provided by an embodiment of the invention;
fig. 2 is a schematic diagram of authentication of a neighboring unmanned intelligent device according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but embodiments of the present invention are not limited thereto.
Example 1
Referring to fig. 1, fig. 1 is a flow chart of a security constraint method of an unmanned intelligent device based on environment side channel information verification according to an embodiment of the present invention.
The unmanned intelligent device security constraint method based on environment side channel information verification comprises the following steps:
s1, acquiring side channel information of an environment where the unmanned intelligent devices are located by the unmanned intelligent devices in a cooperative working scene.
Specifically, environmental information is collected in real time through different environmental conditions of the unmanned intelligent device for executing different tasks, so that whether the task execution condition of the unmanned intelligent device is normal or not can be authenticated. And when the same task is repeated, the unmanned intelligent devices work cooperatively to jointly execute the task, and the environments of the unmanned intelligent devices of the same task are the same in the process of executing the task. Further, in the process of executing different tasks, the unmanned intelligent devices are different in environments, such as temperature, sound, light and the like, raw data of side channel information of the environments can be obtained through a plurality of sensors on each unmanned intelligent device, such as a temperature sensor, a sound sensor, a light sensor and the like, wherein environment information with large sound signal difference can be obtained through the sound sensor, task scene information with strict temperature requirements can be obtained through the temperature sensor, light sensor can detect environment information sensitive to light and the like. Thus, side channel information including, but not limited to, one or more of voice information, temperature information, light information, behavioral information, time information, location information, any information or combination of information in the environment that may be used to identify the operation of a mobile entity such as a device may be used as side channel information.
It should be noted that, in this embodiment, side channel information is collected according to a change of an actual environment, where the collection may be one type of side channel information or may be multiple types of side channel information; for example, a change from a quiet environment to a factory environment, sound information may be collected; the change from highway to tunnel can collect one or two of light information and position information; the change from the common environment to the low-temperature environment such as a refrigerator and the like can acquire one or two of temperature information and behavior information; etc.
S2, each unmanned intelligent device filters the side channel information and classifies the filtered side channel information to obtain a classification result. The method specifically comprises the following steps:
s21, each unmanned intelligent device filters the side channel information by using a wavelet filtering method to obtain the filtered side channel information.
Specifically, when the signal of the environmental side channel information is acquired, interference of other signals with different frequencies and random signals exists, and a certain method is needed to be adopted to filter the interference signals, so that the accuracy of subsequent authentication is improved. Therefore, in order to prevent data loss or other signal interference from causing certain interference to the accuracy of the authentication result, the embodiment uses a wavelet filtering method to filter the acquired side channel information so as to eliminate irrelevant noise interference existing in the original information, obtain abrupt change information, namely the filtered side channel information, and improve the accuracy of subsequent authentication.
The wavelet filtering method is a time-frequency localization analysis method with fixed window size, namely window area, but changeable window shape, and changeable time window and frequency window, and the method has higher frequency resolution and lower time resolution in the low frequency part, and higher time resolution and lower frequency resolution in the high frequency part, and is very suitable for detecting the components of abrupt signals in normal signals. It can obtain finer low-frequency signal information with long time intervals and obtain high-frequency signal information with short time intervals. In this embodiment, the task execution condition of the unmanned intelligent device is authenticated through abrupt change from a quiet environment to a factory environment, abrupt change from a highway to a tunnel light, abrupt change from a common environment to a low-temperature environment such as a refrigerator, etc., and the conventional fourier transform cannot meet such requirements, and the non-stationary signal can be well processed and extracted through a wavelet filtering method.
After wavelet transformation, the useful signal has energy concentrated in few wavelet coefficients, while the wavelet coefficients of noise points are independent and distributed on all time axes of each scale. The mode maximum value point under each scale of wavelet transformation is reserved, other points are set to zero or reduced to the greatest extent, and then the processed wavelet coefficient is subjected to wavelet inverse transformation, so that the purpose of noise suppression can be achieved. Threshold denoising is carried out by comparing and judging the transform domain coefficient with a threshold value, and then carrying out inverse transformation on the processed coefficient to reconstruct a denoised image.
The specific process of wavelet denoising is as follows:
wavelet decomposition of the image, determining a wavelet function and a decomposition level N, and performing N-layer wavelet decomposition on the image.
And (3) threshold processing, namely selecting a threshold value for each layer coefficient obtained through decomposition, and judging the threshold value for the detail coefficient.
And (3) image reconstruction: and reconstructing an image of the thresholded coefficient through inverse wavelet transform.
S22, classifying the filtered side channel information by using a multi-classification method based on machine learning to obtain a classification result.
Specifically, the machine learning based multi-classification method includes, but is not limited to: a multi-classification method based on a convolutional neural network-Long Short-Term Memory network (Convolutional Neural Network-Long Term Memory, CNN-LSTM) model, a multi-classification method based on a random forest algorithm, a multi-classification method based on a support vector machine (Support Vector Machine, SVM), a multi-classification method based on a generated countermeasure network (Generative Adversarial Network, GAN), and a multi-classification method based on a graph nerve ≡complex (Graph Neural Network, GNN).
The present embodiment is described taking a multi-classification method based on a CNN-LSTM network model as an example.
First, the filtered side channel information is subjected to imaging processing.
Specifically, since deep learning is not ideal for processing one-dimensional data, when the acquired environmental side channel information is one-dimensional data, the data needs to be processed, imaged, and converted into two-dimensional gray-scale image data for input into a network model.
And then classifying the two-dimensional gray image data by using the CNN-LSTM network model to confirm the current environment condition of the unmanned intelligent equipment.
Specifically, the convolutional neural network is a widely applied deep learning model, has strong feature extraction capability, can automatically and efficiently extract features from input data quickly, and has better processing capability on two-dimensional data. The CNN method is poor in authentication effect for some classification tasks whose input depends on time, such as continuous information authentication in the present embodiment. For such tasks, the state at the previous time may have an effect on the subsequent authentication results, and thus authentication requires not only the current information input but also the previous input. Based on this, the present embodiment adopts a combination of a long and short term memory storage unit, i.e. LSTM network and CNN network, to classify side channel information.
LSTM has the ability to remove or add information to the state of cells through a complex structure called a "gate". The gate is a method for selectively allowing information to pass through, and consists of an S-shaped neural network layer and a standing point multiplication operation. The LSTM cell has three gates: forget gate, input gate and output gate for protecting and controlling the state of the unit. In this embodiment, the one-dimensional data is converted into the two-dimensional gray-scale image, but the converted gray-scale image is basically another expression form of the one-dimensional data, and time connection exists between adjacent images, so that the lstm+softmax layer is used as a classifier of the authentication network instead of the full connection layer in the conventional CNN network. That is, the two-dimensional gray image data is classified sequentially through the CNN network, the LSTM network, and the softmax layer, thereby obtaining a classification result.
And S3, verifying the classification result of the current unmanned intelligent device by using the classification result of the adjacent unmanned intelligent device to obtain a verification result.
Referring to fig. 2, fig. 2 is a schematic diagram of authentication of a neighboring unmanned intelligent device according to an embodiment of the present invention.
Specifically, under the self-organizing network, a plurality of unmanned intelligent devices cooperate to jointly execute tasks, and in the process of executing the tasks, the environments of the plurality of unmanned intelligent devices of the same task are the same. And combining the characteristic of cooperative work of a plurality of unmanned intelligent devices in the self-organizing network, and authenticating the task execution condition of the current unmanned intelligent device by utilizing side channel information of the adjacent unmanned intelligent devices. As shown in fig. 2, in the ad hoc network, a plurality of unmanned intelligent devices work cooperatively, and if a certain unmanned intelligent device is attacked, the whole task may not be performed normally. Therefore, through the characteristic that the environments of the unmanned intelligent devices are the same in the process of executing the same task, continuous communication is carried out between different devices and adjacent devices, so that the task execution condition of the current unmanned intelligent device is authenticated by utilizing the environment-side channel information classification results of the adjacent unmanned intelligent devices.
The step S3 specifically comprises the steps of:
s31, the current unmanned intelligent device sends the classification result of the current unmanned intelligent device to a plurality of adjacent unmanned intelligent devices respectively.
For example, the unmanned intelligent device a is authenticated. In the task execution process of the unmanned intelligent equipment, various sensors on all the unmanned intelligent equipment continuously acquire the side channel information in real time, and the side channel information is processed by the method in the step S2 to obtain a specific classification result of the current side channel information. Thereafter, at intervals of time t, the unmanned intelligent device a transmits the classification result M within the time t to the adjacent plurality of unmanned intelligent devices B, C, D.
It should be noted that, the unmanned intelligent device a may also transmit the classification result to a plurality of adjacent unmanned intelligent devices B, C, D in real time.
S32, each adjacent unmanned intelligent device compares and verifies the classification result of the adjacent unmanned intelligent device with the received classification result of the current unmanned intelligent device; when the classification result of the adjacent unmanned intelligent equipment is consistent with the classification result of the current unmanned intelligent equipment, a first verification result is obtained; and when the classification result of the adjacent unmanned intelligent device is inconsistent with the classification result of the current unmanned intelligent device, obtaining a second verification result.
Specifically, when the adjacent multiple unmanned intelligent devices B, C, D receive the classification result M, they respectively classify the classification result M i (i=b, c, d) and the received classification result M. Further, when m=m i When the verification result is 1, a first verification result is obtained; and otherwise, the verification result is 0, and a second verification result is obtained.
It should be noted that, when the unmanned intelligent device a transmits the classification result M in the time t to B, C, D, B, C, D compares the classification result in the time t with the received classification result in the time t; when the unmanned intelligent device a transmits the real-time classification result M to B, C, D, B, C, D compares the respective real-time classification result with the received real-time classification result.
S33, each adjacent unmanned intelligent device sends the first verification result or the second verification result to the current unmanned intelligent device.
Specifically, after the verification is completed, B, C, D returns the obtained first verification result or second verification result to the unmanned intelligent device a.
And S4, judging whether the current unmanned intelligent equipment is in a normal working state according to the verification result.
In this embodiment, whether the current unmanned intelligent device is in a normal working state is determined according to the environmental consistency characteristics of the plurality of unmanned intelligent devices in the cooperative working scene.
In a specific embodiment, the current unmanned intelligent device counts the number of the first verification results and the number of the second verification results, and judges whether the current unmanned intelligent device is in a normal working state according to the number of the first verification results and the number of the second verification results. Further, when the number of the first verification results is greater than the number of the second verification results, the current unmanned intelligent device is in a normal working state; when the number of the first verification results is smaller than or equal to the number of the second verification results, the current unmanned intelligent device is in an abnormal working state.
Specifically, the unmanned intelligent device a receives the verification result returned by the adjacent unmanned intelligent device B, C, D in step S3, and confirms whether the current unmanned intelligent device works normally according to the characteristics of the environmental consistency where the plurality of unmanned intelligent devices are located and the proportion of returning 0 and 1 in the cooperative work scene. When the proportion of 1 is greater than half, namely the number of 1 is greater than 0, the task execution of the current unmanned intelligent device is considered to be normal, otherwise, the current unmanned intelligent device is considered to be attacked, emergency measures are taken, and all work is stopped immediately or an alarm is sent out.
The security constraint method of the embodiment verifies between the adjacent unmanned intelligent device and the current unmanned intelligent device by utilizing the environment-side channel information, can solve the problem that the user operation is opaque in the existing authentication mode, achieves the purpose of implicitly and passively performing security constraint by utilizing the environment-side channel information under the condition that the unmanned intelligent device is not perceived, does not need the active operation of the user, achieves the operation transparency, and meets the equipment authentication requirement under the cooperative work scene of a plurality of unmanned intelligent devices in the self-organizing network.
The safety constraint method of the embodiment continuously detects the working condition of the unmanned intelligent equipment through the real-time acquisition of the environmental side channel information and the mutual communication between the unmanned intelligent equipment, and realizes continuous uninterrupted authentication.
The security constraint method of the embodiment can be realized by utilizing a plurality of unmanned intelligent devices in a cooperative working scene, does not need additional device support and a large amount of fund support, has low cost, and is suitable for a scene of large-scale device authentication.
Example two
On the basis of the first embodiment, the present embodiment provides another unmanned intelligent device security constraint method based on environment-side channel information verification, where the method includes the steps of:
s1, acquiring side channel information of an environment where the unmanned intelligent devices are located by the unmanned intelligent devices in a cooperative working scene.
S2, each unmanned intelligent device filters the side channel information and classifies the filtered side channel information to obtain a classification result.
And S3, verifying the classification result of the current unmanned intelligent device by using the classification result of the adjacent unmanned intelligent device to obtain a verification result. The method specifically comprises the following steps:
s31, the adjacent unmanned intelligent devices respectively send the classification results of the adjacent unmanned intelligent devices to the current unmanned intelligent device.
For example, the unmanned intelligent device a is authenticated. In the task execution process of the unmanned intelligent equipment, various sensors on all the unmanned intelligent equipment continuously acquire the side channel information in real time, and the side channel information is processed by the method in the step S2 to obtain a specific classification result of the current side channel information. Then, the adjacent unmanned intelligent devices B, C, D respectively classify the classification results M i (i=b, c, d) to the unmanned intelligent device a.
Note that B, C, D may classify the result M within the time t at intervals t i (i=b, c, d) to the unmanned intelligent device a, or the classification result M may be transmitted in real time i (i=b, c, d) to the unmanned intelligent device a.
S32, the current unmanned intelligent equipment respectively compares and verifies the classification result of the current unmanned intelligent equipment with the received classification result of each adjacent unmanned intelligent equipment; when the classification result of the current unmanned intelligent device is consistent with the classification result of the adjacent unmanned intelligent device, a first verification result is obtained; and when the classification result of the current unmanned intelligent device is inconsistent with the classification result of the adjacent unmanned intelligent device, obtaining a second verification result.
Specifically, the unmanned intelligent device a receives the classification result M i (i=b, c, d), the classification result M of the user is compared with the received classification result M i (i=b, c, d) comparative verification was performed. Further, when m=m i When the verification result is 1, a first verification result is obtained; and otherwise, the verification result is 0, and a second verification result is obtained. After the verification is completed, the unmanned intelligent device A can obtain a first verification result or a second verification result.
And S4, judging whether the current unmanned intelligent equipment is in a normal working state according to the verification result.
The specific implementation steps of steps S1, S2, and S4 are shown in embodiment one, and the technical effects achieved by this embodiment are shown in embodiment one, which is not repeated here.
Example III
On the basis of the first embodiment and the second embodiment, the present embodiment provides another unmanned intelligent device security constraint method based on environment side channel information verification, where the method includes the steps of:
s1, acquiring side channel information of an environment where the unmanned intelligent devices are located by the unmanned intelligent devices in a cooperative working scene.
S2, each unmanned intelligent device filters the side channel information and classifies the filtered side channel information to obtain a classification result.
And S3, verifying the classification result of the current unmanned intelligent device by using the classification result of the adjacent unmanned intelligent device to obtain a verification result.
Specifically, the classification result of the current unmanned intelligent device is verified by using the classification result of any adjacent unmanned intelligent device in the unmanned intelligent device cluster formed by a plurality of trusted adjacent unmanned intelligent devices, and a first verification result or a second verification result is obtained.
It will be appreciated that several adjacent unmanned intelligent devices are arranged to be mutually trusted such that the several adjacent unmanned intelligent devices form a cluster of unmanned intelligent devices. Further, when the current unmanned intelligent device is authenticated, because a plurality of adjacent unmanned intelligent devices in the cluster trust each other, the classification result of any adjacent unmanned intelligent device in the cluster is only required to be used for verifying the classification result of the current unmanned intelligent device.
Further, when the classification result of any one of the neighboring unmanned intelligent devices in the cluster is used to verify the classification result of the current unmanned intelligent device, the method for performing contrast verification in the neighboring unmanned intelligent device described in embodiment one may be adopted, and the method for performing contrast verification in the current unmanned intelligent device described in embodiment two may also be adopted, which is not repeated in this embodiment.
Further, when the classification result of any one of the adjacent unmanned intelligent devices in the cluster is consistent with the classification result of the current unmanned intelligent device, the verification result is 1, and a first verification result is obtained; and when the classification result of any adjacent unmanned intelligent device in the cluster is inconsistent with the classification result of the current unmanned intelligent device, the verification result is 0, and a second verification result is obtained.
And S4, judging whether the current unmanned intelligent equipment is in a normal working state according to the verification result.
Specifically, when the verification result is 1 as the first verification result, the current unmanned intelligent device is in a normal working state; and when the verification result is 0 as a second verification result, the current unmanned intelligent device is in an abnormal working state.
The specific implementation steps of steps S1 and S2 are shown in embodiment one, and the technical effects achieved by this embodiment are shown in embodiment one, which is not described in detail.
Example IV
On the basis of the first embodiment, the second embodiment and the third embodiment, the present embodiment provides a further unmanned intelligent device security constraint method based on environment side channel information verification, and the method includes the steps of:
s1, acquiring side channel information of an environment where the unmanned intelligent devices are located by the unmanned intelligent devices in a cooperative working scene.
S2, each unmanned intelligent device filters the side channel information and classifies the filtered side channel information to obtain a classification result.
And S3, verifying the classification result of the current unmanned intelligent device by using the classification result of the adjacent unmanned intelligent device to obtain a verification result.
Specifically, the initial environment side channel information classification result of the adjacent unmanned intelligent device is utilized to verify the real-time environment side channel information classification result of the current unmanned intelligent device, and a verification result is obtained.
For example, the unmanned intelligent device a is authenticated. After the unmanned intelligent device a transmits the classification result M to the adjacent plurality of unmanned intelligent devices B, C, D, the result of the environmental side channel information matched by the task allocation at the initial time of the adjacent plurality of unmanned intelligent devices B, C, D is compared with the received classification result M for verification. It can be seen that, in this embodiment, the classification results of the adjacent multiple unmanned intelligent devices B, C, D at the initial time are always compared with the real-time classification results of the unmanned intelligent device a.
And S4, judging whether the current unmanned intelligent equipment is in a normal working state according to the verification result.
The specific implementation steps of steps S1, S2, and S4 are shown in embodiment one, and the technical effects achieved by this embodiment are shown in embodiment one, which is not repeated here.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the invention.
Claims (8)
1. The unmanned intelligent device safety constraint method based on environment side channel information verification is characterized by comprising the following steps:
a plurality of unmanned intelligent devices in a cooperative working scene respectively acquire side channel information of an environment in which the unmanned intelligent devices are located;
each unmanned intelligent device filters the side channel information and classifies the filtered side channel information to obtain a classification result;
verifying the classification result of the current unmanned intelligent device by using the classification result of the adjacent unmanned intelligent device to obtain a verification result;
the method for verifying the classification result of the current unmanned intelligent device by using the classification result of the adjacent unmanned intelligent device to obtain a verification result comprises the following steps:
the current unmanned intelligent device sends the classification result of the current unmanned intelligent device to a plurality of adjacent unmanned intelligent devices respectively;
each adjacent unmanned intelligent device compares and verifies the classification result of the adjacent unmanned intelligent device with the received classification result of the current unmanned intelligent device; when the classification result of the adjacent unmanned intelligent equipment is consistent with the classification result of the current unmanned intelligent equipment, a first verification result is obtained; when the classification result of the adjacent unmanned intelligent device is inconsistent with the classification result of the current unmanned intelligent device, a second verification result is obtained;
each adjacent unmanned intelligent device sends the first verification result or the second verification result to the current unmanned intelligent device;
judging whether the current unmanned intelligent device is in a normal working state according to the verification result;
the step of judging whether the current unmanned intelligent device is in a normal working state according to the verification result comprises the following steps:
when the number of the first verification results is larger than the number of the second verification results, the current unmanned intelligent device is in a normal working state;
when the number of the first verification results is smaller than or equal to the number of the second verification results, the current unmanned intelligent device is in an abnormal working state.
2. The unmanned intelligent device security constraint method based on environmental side channel information verification of claim 1, wherein the side channel information comprises one or more of sound information, temperature information, light information, behavior information, time information, and location information.
3. The unmanned intelligent device security constraint method based on environment side channel information verification according to claim 1, wherein each unmanned intelligent device filters the side channel information and classifies the filtered side channel information to obtain a classification result, and the method comprises the following steps:
each unmanned intelligent device utilizes a wavelet filtering method to filter the side channel information to obtain filtered side channel information;
and classifying the filtered side channel information by using a multi-classification method based on machine learning to obtain a classification result.
4. The security constraint method of an unmanned intelligent device based on environment-side channel information verification according to claim 1, wherein verifying the classification result of the current unmanned intelligent device by using the classification result of the adjacent unmanned intelligent device to obtain the verification result comprises:
and verifying the real-time environment side channel information classification result of the current unmanned intelligent device by using the initial environment side channel information classification result of the adjacent unmanned intelligent device to obtain a verification result.
5. The security constraint method of an unmanned intelligent device based on environment-side channel information verification according to claim 1, wherein verifying the classification result of the current unmanned intelligent device by using the classification result of the adjacent unmanned intelligent device to obtain the verification result comprises:
the adjacent unmanned intelligent devices respectively send the classification results of the adjacent unmanned intelligent devices to the current unmanned intelligent device;
the current unmanned intelligent device compares and verifies the classification result of the current unmanned intelligent device with the received classification result of each adjacent unmanned intelligent device; when the classification result of the current unmanned intelligent device is consistent with the classification result of the adjacent unmanned intelligent device, a first verification result is obtained; and when the classification result of the current unmanned intelligent device is inconsistent with the classification result of the adjacent unmanned intelligent device, obtaining a second verification result.
6. The security constraint method of the unmanned intelligent device based on the environment-side channel information verification according to claim 5, wherein the step of judging whether the current unmanned intelligent device is in a normal working state according to the verification result comprises the following steps:
the current unmanned intelligent device counts the number of the first verification results and the number of the second verification results, and judges whether the current unmanned intelligent device is in a normal working state or not according to the number of the first verification results and the number of the second verification results.
7. The security constraint method of an unmanned intelligent device based on environment-side channel information verification according to claim 1, wherein verifying the classification result of the current unmanned intelligent device by using the classification result of the adjacent unmanned intelligent device to obtain the verification result comprises:
and verifying the classification result of the current unmanned intelligent device by using the classification result of any adjacent unmanned intelligent device in the unmanned intelligent device cluster formed by a plurality of trusted adjacent unmanned intelligent devices to obtain a first verification result or a second verification result.
8. The security constraint method of the unmanned intelligent device based on the environment-side channel information verification according to claim 7, wherein the step of judging whether the current unmanned intelligent device is in a normal working state according to the verification result comprises the following steps:
when the verification result is the first verification result, the current unmanned intelligent device is in a normal working state; and when the verification result is the second verification result, the current unmanned intelligent device is in an abnormal working state.
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