CN108664785A - A kind of device-fingerprint extraction and authentication method based on CPU module electromagnetic radiation - Google Patents
A kind of device-fingerprint extraction and authentication method based on CPU module electromagnetic radiation Download PDFInfo
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- CN108664785A CN108664785A CN201810299278.1A CN201810299278A CN108664785A CN 108664785 A CN108664785 A CN 108664785A CN 201810299278 A CN201810299278 A CN 201810299278A CN 108664785 A CN108664785 A CN 108664785A
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/44—Program or device authentication
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Abstract
The device-fingerprint extraction and authentication method that the invention discloses a kind of based on CPU module electromagnetic radiation realize the certification to current device by the way of collecting device CPU module electromagnetic radiation data.Compared with the existing device authentication technology based on software, this method is fixed against hardware and unconventional software, improves the reliability and stability of this method；The CPU module of equipment is in the fabrication process because of the limitation of processing technology, there are small errors between different CPU modules, and these errors are almost constant in the life cycle of equipment and can not change, mode accuracy higher relative to hardware informations such as traditional addresses foundation mac in this way.Compared with the existing device-fingerprint extracting method based on equipment internal components, the method of the present invention carries out data acquisition independent of device operating system, reduce the certification risk that system is brought by attack, the stability and credibility of certification are improved, the authentication accuracy of the method for the present invention is 98% or so.
Description
Technical field
The invention belongs to internet arenas, are related to a kind of device-fingerprint extraction and certification based on CPU module electromagnetic radiation
Method.
Background technology
In recent years, user authentication becomes application developers and operator's general need.For needing to ensure account peace
Full application, such as electronic wallet application, online shopping application, certification user is its important demand for security.Criminal steals
Account number cipher is taken to happen occasionally the case where being logged in remaining arbitrary equipment and carrying out illegal activities, associated account number and intelligent terminal
Equipment come identify, certification user, can effectively promote the safety of account, ensure the safety of personal property and privacy.Normally,
Browser can identify account and equipment by Cookies, and the app on smart mobile phone is then by asking the ID of equipment, such as
IMEI etc., to identify user.But these general methods have caused concern of the people to privacy of user safety problem, accordingly
Measure be suggested specification these behaviors.Therefore, it is necessary to which finding a kind of new mode identifies user, discriminating user account
It is to be used on which terminal device.
Invention content
The present invention provide it is a kind of based on CPU module electromagnetic radiation device-fingerprint extraction and authentication method, by obtain set
The standby intrinsic difference of CPU module is authenticated equipment, does not depend on device operating system and carries out data acquisition, reduce system by
The certification risk brought is attacked, the stability and credibility of certification are improved.
The device-fingerprint extraction and recognition methods based on CPU module electromagnetic radiation of the present invention, includes the following steps：
1) on Devices to test prepackage can device under test CPU continue, the certification software that consistently encourages；
2) the certification software pre-installed on Devices to test is run, external magnetometric sensor is placed on Devices to test CPU module
Top acquires ELECTROMAGNETIC RADIATION SIGNATURE；
3) magnetometric sensor data are pre-processed, so that it is distributed in [0,1] section using min-max standardized methods
It is interior；
4) mean value (Mean), mean difference (Average Deviation), mark are extracted in the time domain to the data after standardization
5 accurate poor (Std.Deviation), RMS amplitude (RMS Amplitude), the degree of bias (Skewness) features, on frequency domain
Extract mean value (Spec.Mean), standard deviation (Spec.Std.Deviation), extension (Spec.Spread), the degree of bias
(Spec.Skewness), kurtosis (Spec.Kurtosis), K- scramblings (Spec.Irregularity-K), J- are irregular
Property (Spec.Irregularity-J), tonality coefficient (Spec.Flatness), is roll-offed at smoothness (Spec.Smoothness)
Property (Spec.Roll Off) 10 features, and this is amounted into 15 features as device-fingerprint；
5) each known device is trained using the method for ExtraTrees in machine learning and establishes single classification instruction
Practice device, is authenticated in certification using corresponding single classifier and in conjunction with threshold method device under test identity.
In above-mentioned technical proposal, further, the certification software device under test CPU described in step 1) carry out continue,
The method consistently encouraged is as follows：
(1) the current CPU working frequencies of Devices to test are checked, if current CPU working frequencies are normal, are thened follow the steps (2)；If
Current CPU working frequencies are less than normal value, then after waiting for 10 seconds, re-execute step (1)；
(2) it checks the highest priority of the currently running user thread of Devices to test, and the priority is denoted as
current_priority_level；
(3) core number that Devices to test CPU is supported, the Logic Core generated including Intel Hyper-Threadings are checked
Calculation mesh, and the number is denoted as N；
(4) N number of thread is set, per thread main body is While (1) idle loop, and thread priority is current_
Priotiry_level+1, and N number of thread is tied to respectively on N number of core cpu；
(5) while N number of thread in operating procedure (4), run time are 0.5 second；
(6) priority whether is higher than the line of current_priotiry_level+1 in checking step (5) implementation procedure
Journey occurs；If so, then abandoning the data that current external magnetometric sensor is collected, and re-executed since step (1)；If nothing,
Execute step (7)；
(7) CPU working frequencies whether there is less than normal working frequency phenomenon in checking step (5) implementation procedure；If so,
The data that current external magnetometric sensor is collected then are abandoned, and are re-executed since step (1)；If nothing, excitation is completed.
Further, the Devices to test CPU module described in step 2) by Devices to test CPU and its attached DC/DC
Power-switching circuit forms.
Further, the threshold method described in step 5) is：When known device fingerprint in Devices to test fingerprint and single classifier
When similarity is more than threshold value, then it is assumed that certification passes through.The threshold value is empirical value, generally may be set to 0.6.
The beneficial effects of the invention are as follows：
The present invention realizes the certification to current device by the way of collecting device CPU module electromagnetic radiation.With existing base
It is compared in the device authentication technology of software, this method is fixed against hardware and unconventional software, improves the reliability of this method
And stability；The CPU module of equipment is in the fabrication process because of the limitation of processing technology, and there are micro- between different CPU modules
Small error, and these errors are almost constant in the life cycle of equipment and can not change, institute in this way relative to
The mode accuracy higher of the hardware informations such as traditional addresses foundation mac.With the existing device-fingerprint based on equipment internal components
Extracting method is compared, and the method for the present invention carries out data acquisition independent of device operating system, reduces system by attack band
The certification risk come, improves the stability and credibility of certification.It is examined by large number of equipment, the certification of the method for the present invention is accurate
Degree is 98% or so.
Description of the drawings
Fig. 1 is the method flow diagram of the embodiment of the present invention.
Fig. 2 is the physical expressions of 15 features in the present invention.
Specific implementation mode
With reference to embodiment and Figure of description, the present invention will be further described.
The method flow of the embodiment of the present invention, as shown in Figure 1.
1) certification software is pre-installed on Devices to test, function is that device under test CPU continue, consistently swashed
It encourages；It is as follows that software device under test CPU carries out the method for continuing, consistently encouraging：
(1) the current CPU working frequencies of Devices to test are checked, if current CPU working frequencies are normal, are thened follow the steps (2)；If
Current CPU working frequencies are less than normal value, then after waiting for 10 seconds, re-execute step (1)；
(2) it checks the highest priority of the currently running user thread of Devices to test, and the priority is denoted as
current_priority_level；
(3) core number that Devices to test CPU is supported, the Logic Core generated including Intel Hyper-Threadings are checked
Calculation mesh, and the number is denoted as N；
(4) N number of thread is set, per thread main body is While (1) idle loop, and thread priority is current_
Priotiry_level+1, and N number of thread is tied to respectively on N number of core cpu；
(5) while N number of thread in operating procedure (4), run time are 0.5 second；
(6) priority whether is higher than the line of current_priotiry_level+1 in checking step (5) implementation procedure
Journey occurs；If so, then abandoning the data that current external magnetometric sensor is collected, and re-executed since step (1)；If nothing,
Execute step (7)；
(7) CPU working frequencies whether there is less than normal working frequency phenomenon in checking step (5) implementation procedure；If so,
The data that current external magnetometric sensor is collected then are abandoned, and are re-executed since step (1)；If nothing, excitation is completed.
2) the certification software pre-installed on Devices to test is run, external magnetometric sensor is placed on Devices to test CPU module
Top acquires ELECTROMAGNETIC RADIATION SIGNATURE；Devices to test CPU module is converted by the CPU of Devices to test and its attached DC/DC power supplys
Circuit forms.
3) magnetometric sensor data B is pre-processed, so that it is distributed in [0,1] area using min-max standardized methods
In.The computational methods of wherein standardization result M are as follows：
4) mean value (Mean), mean difference (Average Deviation), mark are extracted in the time domain to the data after standardization
5 accurate poor (Std.Deviation), RMS amplitude (RMS Amplitude), the degree of bias (Skewness) features, on frequency domain
Extract mean value (Spec.Mean), standard deviation (Spec.Std.Deviation), extension (Spec.Spread), the degree of bias
(Spec.Skewness), kurtosis (Spec.Kurtosis), K- scramblings (Spec.Irregularity-K), J- are irregular
Property (Spec.Irregularity-J), tonality coefficient (Spec.Flatness), is roll-offed at smoothness (Spec.Smoothness)
Property (Spec.Roll Off) 10 features, feature calculation method is as shown in Fig. 2, wherein x is the initial data of feature to be extracted
Time domain expression-form, y are the frequency domain presentation form of the initial data of feature to be extracted, ymAnd yfIt is divided into amplification coefficient and frequency window
Mouthful, N is x or ymAnd yfThe number of middle data.And this is amounted into 15 features as device-fingerprint.
5) method for using ExtraTrees in machine learning carries out fingerprint training to each known device and establishes single point
Class training aids.Authentication is carried out using corresponding single classifier device under test in certification, and to authentication result application threshold
Value method.Rule of thumb given threshold generally may be set to 0.6, when Devices to test fingerprint is similar to the device-fingerprint of earlier registration
When degree is more than the threshold value, then it is assumed that certification passes through.
Claims (5)
1. it is a kind of based on CPU module electromagnetic radiation device-fingerprint extraction and authentication method, which is characterized in that this method include with
Lower step：
1) on Devices to test prepackage can device under test CPU continue, the certification software that consistently encourages；
2) the certification software pre-installed on Devices to test is run, external magnetometric sensor is placed on above Devices to test CPU module
Acquire ELECTROMAGNETIC RADIATION SIGNATURE；
3) magnetometric sensor data are pre-processed, so that it is distributed in [0,1] section using min-max standardized methods；
4) mean value (Mean), mean difference (Average Deviation), standard deviation are extracted in the time domain to the data after standardization
(Std.Deviation), 5 RMS amplitude (RMS Amplitude), the degree of bias (Skewness) features, are extracted on frequency domain
Mean value (Spec.Mean), standard deviation (Spec.Std.Deviation), extend (Spec.Spread), the degree of bias
(Spec.Skewness), kurtosis (Spec.Kurtosis), K- scramblings (Spec.Irregularity-K), J- are irregular
Property (Spec.Irregularity-J), tonality coefficient (Spec.Flatness), is roll-offed at smoothness (Spec.Smoothness)
Property (Spec.Roll Off) 10 features, and this is amounted into 15 features as device-fingerprint；
5) single classification based training device is trained and established to each known device using the method for ExtraTrees in machine learning,
It is authenticated in certification using corresponding single classifier and in conjunction with threshold method device under test identity.
2. device-fingerprint extraction and authentication method, feature according to claim 1 based on CPU module electromagnetic radiation exist
In it is as follows that the certification software device under test CPU carries out the method for continuing, consistently encouraging：
(1) the current CPU working frequencies of Devices to test are checked, if current CPU working frequencies are normal, are thened follow the steps (2)；If current
CPU working frequencies are less than normal value, then after waiting for 10 seconds, re-execute step (1)；
(2) it checks the highest priority of the currently running user thread of Devices to test, and the priority is denoted as current_
priority_level；
(3) core number that Devices to test CPU is supported, the Logic Core calculation generated including Intel Hyper-Threadings are checked
Mesh, and the number is denoted as N；
(4) N number of thread is set, per thread main body is While (1) idle loop, and thread priority is current_
Priotiry_level+1, and N number of thread is tied to respectively on N number of core cpu；
(5) while N number of thread in operating procedure (4), run time are 0.5 second；
(6) thread of the priority higher than current_priotiry_level+1 whether goes out in checking step (5) implementation procedure
It is existing；If so, then abandoning the data that current external magnetometric sensor is collected, and re-executed since step (1)；If nothing, execute
Step (7)；
(7) CPU working frequencies whether there is less than normal working frequency phenomenon in checking step (5) implementation procedure；If so, then putting
The data that current external magnetometric sensor is collected are abandoned, and are re-executed since step (1)；If nothing, excitation is completed.
3. device-fingerprint extraction and authentication method, feature according to claim 1 based on CPU module electromagnetic radiation exist
In the Devices to test CPU module is made of the CPU and its attached DC/DC power-switching circuits of Devices to test.
4. device-fingerprint extraction and authentication method, feature according to claim 1 based on CPU module electromagnetic radiation exist
In the threshold method described in step 5) is：When known device fingerprint similarity is more than threshold value in Devices to test fingerprint and single classifier
When, then it is assumed that certification passes through.
5. device-fingerprint extraction and authentication method, feature according to claim 4 based on CPU module electromagnetic radiation exist
In the threshold value may be set to 0.6.
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Cited By (2)
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| CN109640096A (en) * | 2018-12-06 | 2019-04-16 | 浙江大学 | A kind of concealed communication method based on video decoding electromagnetic leakage |
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Cited By (3)
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| CN108664785B (en) | 2020-12-08 |
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