CN113055889B - Mobile phone pasture detection and calibration method based on resonance characteristics of inertial measurement unit - Google Patents
Mobile phone pasture detection and calibration method based on resonance characteristics of inertial measurement unit Download PDFInfo
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- CN113055889B CN113055889B CN202110138542.5A CN202110138542A CN113055889B CN 113055889 B CN113055889 B CN 113055889B CN 202110138542 A CN202110138542 A CN 202110138542A CN 113055889 B CN113055889 B CN 113055889B
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Abstract
The invention discloses a mobile phone pasture detection and calibration method based on resonance characteristics of an inertial measurement unit. When one device in the same cluster has high-risk behavior, the whole cluster device is regarded as a high-risk device and the function of the high-risk device is limited. The method utilizes the adjacent characteristic of the physical space of the equipment in the mobile phone pasture, combines the sensitivity of the inertia measurement unit to sound waves, detects the mobile phone pasture and calibrates the equipment, and reduces the damage of black grey products to enterprise activities.
Description
Technical Field
The invention relates to a mobile phone pasture detection and calibration method, in particular to a mobile phone pasture detection and calibration method based on resonance characteristics of an inertia measurement unit.
Background
With the development and popularization of the mobile internet, many offline services are migrated online, and new security threats come with it. However, as the awareness of the privacy of the user increases, developers adopt a great number of technical means to protect the device identifier to prevent the privacy of the user from being leaked, which also means that the difficulty of detecting and identifying the black grey product device increases.
In order to attack black and gray products, the mobile phone pasture needs to be detected and identified aiming at the problem of the mobile phone pasture. Meanwhile, from the perspective of privacy protection, the mobile application side only possesses a few application-related permissions and a part of low-sensitivity data permissions: such as speakers, gyroscopes and accelerometers. Traditional detection means such as multi-factor verification often need more operation authority, and identification means depends on identification codes provided by platforms
In the traditional multi-factor authentication, not only the account password of a user is verified, but also equipment information, time, biological factors and the like are verified. If the user state is detected at any time, when the user logs in at different places, changes people, changes equipment and the like, a self-adaptive safety authentication mechanism is pushed when the user finds abnormal conditions, and verification modes such as short message verification, face recognition, fingerprints and the like can be adopted. These authentication methods are not only cumbersome and time consuming to operate, but also fail to achieve the goal of preventing black and gray production behavior.
The conventional Device identification means identifies devices in manners such as UDID (Unique Device Identifier), UUID (universal Unique Identifier) or IDFA (Identifier For Advertising Identifier) used by the IOS platform, and android id and IMEI (International Mobile Equipment Identifier) commonly used by the android platform. However, as the privacy awareness of users increases, reading of the UDID or UUID is prohibited, while the IDFA can be changed, so that the problem that the device fingerprint changes due to system reset, user restoration and the like exists, and android manufacturers gradually provide a function of hiding or dynamically generating the device fingerprint. The purpose of device fingerprinting is to generate a unique signature that uniquely identifies a particular mobile device. But in different platforms, the way in which device fingerprints are generated depends on the platform characteristics. However, device fingerprints generated by different platforms are often not generalized enough. In addition, the black and gray industry may even intentionally mask, reset, or modify these device identifiers to prevent the ability to continue to make a profit after its own device has been calibrated.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a mobile phone pasture detection and calibration method based on the resonance characteristics of an inertial measurement unit. When one of the devices is in high-risk behavior, the devices in the same cluster are listed in a blacklist, so that the harm of black and gray products is reduced.
The method comprises the following steps: and detecting the mobile phone cluster. When the user enters the mobile application activity interface, the application applies for speaker authority and plays chirp sound with a specific period. Due to the characteristic of physical space adjacency of the devices in the mobile phone pasture, the inertial measurement units of the surrounding devices can generate responses in the same period. Recording the inertial measurement unit data of all the equipment installed with the application in the same area/base station, observing, marking the equipment generating the same period response, and regarding as a cluster of a mobile phone pasture. Meanwhile, unique equipment fingerprints are formed by utilizing the resonance frequency spectrum characteristics of the inertia measurement unit and are used for calibrating and identifying equipment in different clusters. Namely: recording the frequency start and stop points f of resonance on each axis of each equipment inertia measurement unitstart、fend. Any one or more groups of resonance start and stop points are used as the identity of the equipment. The devices generating the same period response comprise a plurality of devices which are generated by the devices playing the chirp sound and respond to the chirp sound in the same period, for example, when a device plays the chirp sound, b devices are detected to generate the same period response, and when b devices detect a device a and a device c during playing, the devices abc are all regarded as the same cluster, so that all devices in the mobile phone pasture can be detected as far as possible. In addition, the rated power of the loudspeaker of the mobile phone is calculated (within 5 w), the coverage range is about 20 cm in diameter, and the detection range can be determined by adjusting the volume of the loudspeaker.
Step two: and (4) blacklisting high-risk equipment. When one device has high-risk activities, all devices in the same cluster with the device are set as blacklists, and mobile phone pasture detection is achieved.
Further, the inertial measurement unit is an accelerometer and/or a gyroscope.
Further, in the first step, the frequency start and stop point f of resonance occurrence on each axis of each equipment inertia measurement unitstart、fendThe judgment process is as follows: when the vibration data distribution of the inertia measurement unit is not accordant withAnd the data of a plurality of axes of the same inertia measurement unit with Gaussian distribution and the same frequency are considered as the resonance of the inertia measurement unit.
Further, in the first step, two points having an amplitude of 0.707 times the peak value of the axis are used as the frequency start and stop points f at which resonance occurs on each axisstart、fend。
Further, in the first step, the resonance start and stop points of the three axes of the accelerometer and the gyroscope are used to form a unique 12-dimensional coordinate as the identity of the corresponding device, that is, the unique 12-dimensional coordinate is used as the identity of the corresponding device
Further, the device fingerprint is reduced in dimension on the premise of acceptable discrimination capacity loss, so that the device discrimination process is accelerated and the computational complexity is reduced. The start and stop points of a frequency band in which at least two axes resonate in the inertial measurement unit are selected as the fast identification fingerprints, i.e. the fingerprint
The equipment fingerprint generation method based on the resonance characteristics of the gyroscope generates the equipment fingerprint aiming at the running equipment of different platforms such as Android, IOS, PC, mobile Internet of things and the like, and forms a cross-platform equipment fingerprint generation standard.
The invention has the advantages that the invention provides a mobile phone pasture detection and calibration method based on the resonance characteristics of an inertia measurement unit, an accelerometer, a gyroscope and a loudspeaker with low sensitivity authority are utilized, equipment in an adjacent physical space is judged by utilizing the sensitivity of the inertia measurement unit to sound waves, equipment fingerprints are formed by starting and stopping points of resonance frequencies of all axes in the equipment fingerprints, a platform-independent equipment fingerprint generation standard is formed, and 10^12 order of magnitude of equipment identification space is supported. Under the condition that computing resources are limited, under the condition of acceptable identification space loss, the characteristic dimensionality can be reduced to further accelerate equipment identification, and meanwhile, the convenience and the practicability are met. When one of the devices is in high-risk behavior, the devices in the same cluster are listed in a blacklist, so that the harm of black and gray products is reduced.
Drawings
FIG. 1 is a block diagram of a mobile phone pasture detection and calibration method;
FIG. 2 is an x-axis resonance start and stop radar plot illustrating an example of 8 BMI160 gyroscopes;
FIG. 3 is a y-axis resonance start and stop radar plot illustrating an 8 BMI160 gyroscope;
FIG. 4 is a z-axis resonance start and stop radar plot illustrating an 8 BMI160 gyroscope;
FIG. 5 is a radar chart of a fast fingerprint identification using 8 BMI160 gyroscopes as an example;
FIG. 6 is a fast fingerprint identification accuracy confusion matrix;
Detailed Description
The present invention will now be described in further detail with reference to the attached drawings, which are illustrative, but not limiting, of the present invention.
The invention relates to a mobile phone pasture detection and calibration method based on resonance characteristics of an inertial measurement unit, which comprises the following steps of:
the method comprises the following steps: and detecting the mobile phone cluster. When the user enters the mobile application activity interface, the application applies for speaker authority and plays chirp sound with a specific period. Due to the characteristic of physical space adjacency of the devices in the mobile phone pasture, the inertial measurement units of the surrounding devices can generate responses in the same period. Therefore, the inertial measurement unit data of all devices installed with the application in the same area/base station are recorded and observed, and the devices in which the same periodic response is generated are marked and regarded as a cluster. The method comprises the following substeps:
(1.1) playing chirp: when the user enters the mobile application activity interface, the application applies for speaker authorization and plays chirped sounds at a specific period and frequency change rate.
(1.2) resonance judgment: and acquiring vibration data of all inertia measurement units of the equipment installed with the application in the same area/base station of the equipment, recording and observing the vibration data, and when the vibration data distribution does not conform to Gaussian distribution and the multiple axis data of the same inertia measurement unit meet the condition of the same frequency, determining that the inertia measurement unit resonates.
(1.3) cluster calibration: when the resonance period of the inertia measurement unit in the equipment is the same as the chirp sound, the equipment is considered to be close to the physical space of the equipment playing the chirp sound. All devices that meet the conditions are recorded and considered as a cluster of cell phone farms.
Step two: and generating and calibrating the device fingerprint. Referring to fig. 2, 3 and 4, the spectral characteristics of the inertial measurement unit resonances are used to form unique device fingerprints for calibrating and identifying devices in different clusters. In the embodiment, the frequency start and stop points f of resonance generation on each axis of two inertia measurement units of a gyroscope and an accelerometer in the equipment are recorded simultaneouslystart、fend. The resonance start point and the resonance stop point form a unique 12-dimensional coordinate as an identity mark of the corresponding equipment, so that the identification space supported by the equipment is higher. The method comprises the following substeps:
(2.1) pretreatment: the peak amplitude values and the corresponding frequencies of the individual axes of the resonant inertial measurement unit are recorded. Two points f with the amplitude of 0.707 times of the peak value of the axis are respectively found on each axisstart、fendThe corresponding frequency is recorded as the point at which resonance occurs.
(2.2) fingerprint generation: the resonance start and stop points of the three axes of the accelerometer and gyroscope constitute a unique 12-dimensional coordinate as the identity of the device, i.e. the device is identified
Due to the high accuracy of the measurement step (e.g. 1Hz step), the discrimination space for a single axis is often greater than 100Hz, and thus the discrimination space for the entire device fingerprint is as high as 1024.
As another approach, fingerprints are quickly identified: referring to fig. 5, the device fingerprint is reduced in dimension to speed up the device identification process and reduce computational complexity with acceptable loss of identification capacity. The start and stop points of a frequency band in which at least two axes resonate in the inertial measurement unit are selected as the fast identification fingerprints, i.e. the fingerprint
While using a fast identification fingerprint as a device fingerprint reduces the dimensionality of the features, it can still distinguish millions of devices, which is sufficient to cover most scenarios. To demonstrate stability, 12 gyroscope chips were tested one month after the initial measurement of the quick identification fingerprint, including 8 BMI160 (nos. 1-8), 4L 3GD20 (nos. 9-12) gyroscopes. The result shows that the accuracy of rapid fingerprint identification is 96.32%, see fig. 6.
The equipment fingerprint generation method based on the resonance characteristics of the gyroscope generates the equipment fingerprint aiming at the running equipment of different platforms such as Android, IOS, PC and the Internet of things, and forms a cross-platform equipment fingerprint generation standard.
Step three: and (4) blacklisting high-risk equipment. When high-risk activities of one device are detected through a script detection technology, manual verification, user reporting, picture verification codes and the like, all devices in the same cluster are set as blacklists, the detection efficiency of a mobile phone pasture is improved, and the harm of black and grey products is reduced.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (6)
1. A mobile phone pasture detection and calibration method based on resonance characteristics of an inertial measurement unit is characterized by comprising the following steps:
the method comprises the following steps: detecting a mobile phone cluster: entering mobile application activities by a userWhen the interface is used, applying for the authority of a loudspeaker and playing chirp sound at a specific period and frequency change rate; recording vibration data of inertia measurement units of all equipment installed with the application in the same area or base station, observing the vibration data, marking the equipment generating the same periodic response, and regarding the equipment as a cluster; simultaneously recording the frequency start and stop points of resonance generation on each axis of each equipment inertia measurement unit
、
(ii) a Any one or more groups of resonance start and stop points are used as the identity marks of the corresponding equipment;
step two: blacklist of high risk equipment: when one device has high-risk activity, all devices in the same cluster with the device are set as a blacklist, and the mobile phone pasture detection is realized.
2. The mobile phone ranch detection and calibration method based on the resonance characteristics of the inertial measurement unit according to claim 1, wherein in the first step, the frequency start and stop points of resonance occurrence on each axis of each inertial measurement unit of each device
、
The judgment process is as follows: and when the vibration data distribution of the inertia measurement unit does not conform to the Gaussian distribution and the data of a plurality of axes of the same inertia measurement unit meet the condition that the frequencies are the same, the inertia measurement unit is considered to resonate.
3. The mobile phone ranch detection and calibration method based on the resonance characteristics of the inertial measurement unit according to claim 1, wherein the inertial measurement unit is an accelerometer and/or a gyroscope.
4. The method for detecting and calibrating the pasture of the mobile phone based on the resonance characteristics of the inertial measurement unit of claim 3, wherein in the first step, the resonance start and stop points of the three axes of the accelerometer and the gyroscope are used to form a unique 12-dimensional coordinate as the identity of the corresponding device, that is, the unique 12-dimensional coordinate is used as the identity of the corresponding device
5. The mobile phone ranch detection and calibration method based on resonance characteristics of inertial measurement unit according to claim 2, characterized in that the start and stop points of the frequency band where at least two axes in the inertial measurement unit resonate are selected as the fast identification fingerprint, i.e. the fast identification fingerprint
6. The mobile phone ranch detection and calibration method based on the resonance characteristics of the inertial measurement unit according to any one of claims 1-5, characterized in that in the first step, two points with the amplitude of 0.707 times of the peak value are adopted
Frequency start and stop points of resonance occurrence on each axis
、
。
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| CN112187373A (en) * | 2020-08-28 | 2021-01-05 | 浙江大学 | Concealed channel communication method based on gyroscope resonance |
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| JP4387987B2 (en) * | 2004-06-11 | 2009-12-24 | 株式会社オクテック | Microstructure inspection apparatus, microstructure inspection method, and microstructure inspection program |
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|---|---|---|---|---|
| CN206369546U (en) * | 2016-12-29 | 2017-08-01 | 福建师范大学 | A kind of hybrid location system based on smart mobile phone |
| CN112187373A (en) * | 2020-08-28 | 2021-01-05 | 浙江大学 | Concealed channel communication method based on gyroscope resonance |
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