CN108256357A - Hardware Trojan Horse Detection Method Combining Infrared Image and Normal Distribution Analysis - Google Patents
Hardware Trojan Horse Detection Method Combining Infrared Image and Normal Distribution Analysis Download PDFInfo
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- CN108256357A CN108256357A CN201810022018.XA CN201810022018A CN108256357A CN 108256357 A CN108256357 A CN 108256357A CN 201810022018 A CN201810022018 A CN 201810022018A CN 108256357 A CN108256357 A CN 108256357A
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- 238000001514 detection method Methods 0.000 title claims abstract description 35
- 238000004458 analytical method Methods 0.000 title claims abstract description 18
- ZXQYGBMAQZUVMI-GCMPRSNUSA-N gamma-cyhalothrin Chemical compound CC1(C)[C@@H](\C=C(/Cl)C(F)(F)F)[C@H]1C(=O)O[C@H](C#N)C1=CC=CC(OC=2C=CC=CC=2)=C1 ZXQYGBMAQZUVMI-GCMPRSNUSA-N 0.000 claims abstract description 56
- 238000007619 statistical method Methods 0.000 claims abstract description 10
- 238000005070 sampling Methods 0.000 claims abstract description 5
- 238000000034 method Methods 0.000 claims description 26
- 238000010586 diagram Methods 0.000 claims description 18
- 238000012360 testing method Methods 0.000 claims description 13
- 239000000523 sample Substances 0.000 claims description 5
- 239000000284 extract Substances 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 4
- 230000002093 peripheral effect Effects 0.000 claims description 3
- 239000012521 purified sample Substances 0.000 claims description 3
- 230000005611 electricity Effects 0.000 claims description 2
- 238000005259 measurement Methods 0.000 claims description 2
- 230000008901 benefit Effects 0.000 abstract description 2
- 230000008569 process Effects 0.000 description 12
- 230000006872 improvement Effects 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 238000011990 functional testing Methods 0.000 description 4
- 238000012795 verification Methods 0.000 description 4
- 238000005265 energy consumption Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 210000003484 anatomy Anatomy 0.000 description 2
- 241000283086 Equidae Species 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000010668 complexation reaction Methods 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000001066 destructive effect Effects 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- 238000002513 implantation Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- 230000036651 mood Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/70—Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer
- G06F21/71—Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer to assure secure computing or processing of information
- G06F21/76—Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer to assure secure computing or processing of information in application-specific integrated circuits [ASIC] or field-programmable devices, e.g. field-programmable gate arrays [FPGA] or programmable logic devices [PLD]
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/30—Transforming light or analogous information into electric information
- H04N5/33—Transforming infrared radiation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
- G01J2005/0077—Imaging
Abstract
A hardware Trojan horse detection method combining infrared images and normal distribution analysis comprises the following steps: s1: capturing an infrared image; enabling a pure chip sample without a hardware trojan and a tested chip with the same type to start working simultaneously, and capturing infrared images of the pure chip sample without the hardware trojan and the tested chip with the same type by using image acquisition equipment; s2: carrying out first difference on the obtained infrared image; within a period of sampling, each sampling moment has a pair of infrared images, and the infrared images are differentiated to obtain a differentiated infrared image; s3: and carrying out second difference on the differential infrared image at each moment. Extracting each pixel point in the differential infrared image obtained by the first difference, and drawing the differential temperature on a coordinate graph taking time as an abscissa; s4: judging; the differential temperature of the point with the Trojan is higher; s5: and carrying out normal distribution statistical analysis. The invention has the advantages of high detection precision, low detection cost, high detection efficiency and the like.
Description
Technical field
Present invention relates generally to the hardware Trojan horse detection fields of integrated circuit, refer in particular to a kind of infrared image and normal distribution
Analyze the hardware Trojan horse detection method combined.
Background technology
As the cost of integrated circuit production line is higher and higher, many IC designers are selected integrated circuit
It manufactures this link and is contracted out to third party Foundry Works.This outsourcing results in the credible risk of integrated circuit, in ifq circuit
Additional malice circuit may be implanted, this malice circuit is referred to as hardware Trojan horse.Hardware Trojan horse detection has become core
One important topic of piece security fields, a variety of detection methods are suggested, such as function and structured testing method, design for Measurability
Method, reverse post-mortem method, by-passing signal detection method etc..
Reversed anatomy verification method, is substantially that one kind is based on as the most common type destructiveness hardware Trojan horse detection means
The analytical technology that intrusive mood checks.Usually the ifq circuit that production obtains is dissected, the gold in photo will be extracted after photograph
Belong to line pattern to compare with the picture that layout file extracts, the difference between pattern is analyzed, if result of the comparison is shown
The chip layout inversely extracted coincide with original layout, and illustrating domain, there is no hardware Trojan horses, and otherwise, circuit may be implanted
Hardware Trojan horse.The shortcomings that reversed anatomy verification, is：First, the process of chip conversed analysis is extremely complex, and verification process is slow；
2nd, this is a kind of destructive hardware Trojan horse detection mode, and detected chip has been scrapped completely after completing to detect can not be after
It is continuous to use.
Functional test, hardware Trojan horse in order to destroy the normal function of original circuit or leak chip in secret number
According to all inevitably being distorted to the normal function of chip circuit.Functional test in circuit input end mouth by applying
Test vector, whether comparison output is consistent with normal state, and whether hardware Trojan horse is implanted by malice with this assessment circuit.It is theoretical
On say if can traverse all working state and redundant state in chip can trigger hardware Trojan horse to find
Whether hardware Trojan horse has been inserted into circuit, but many chips are on a grand scale at present, complexity is very high, the range of state space
It is exponentially increased, so that the method cost of functional test is too greatly without can be achieved.
The information such as bypass message analytic approach, power consumption, temperature, the delay that circuit generates when working are known as bypass message.It is based on
It, can be from the original for distorting circuit in varying degrees after the principle of bypass analysis hardware Trojan horse is that circuit is implanted hardware Trojan horse circuit
Begin composition and size so that the circuit after implantation hardware Trojan horse shows the bypass message feature different from ifq circuit.Therefore,
Whether sampled and examined by the bypass message to circuit can be implanted hardware Trojan horse in identification chip.
In conclusion the above method or there are cost it is excessively high the problem of or there are detection result it is bad the problem of.
Invention content
The technical problem to be solved in the present invention is that：For technical problem of the existing technology, the present invention provides one
The hardware Trojan horse detection that the infrared image that kind accuracy of detection is high, testing cost is low, detection efficiency is high is combined with normal distribution analysis
Method.
In order to solve the above technical problems, the present invention uses following technical scheme：
The hardware Trojan horse detection method that a kind of infrared image is combined with normal distribution analysis, step are：
S1：Capture infrared image；
The pure chip sample of one piece of no hardware Trojan horse and the chip under test of one piece of same model is allowed to start simultaneously at work,
Then with the infrared image of both in image capture device capture a period of time；
S2：First time difference is carried out to the infrared image of acquisition；
Within a period of time sampled, each sampling instant has a pair of of infrared image, and difference is carried out to the two,
Obtain a differentiated infrared image；Aforesaid operations are carried out to a pair of of image at each moment, when just obtaining each
Carve the difference infrared image of chip under test and purified sample；
S3：Second of difference is carried out to the difference infrared image at each moment.
Each pixel in the difference infrared image that first time difference is obtained extracts, its differential temperature is drawn
In the coordinate diagram using the time as abscissa；
S4：Judge；
In the coordinate diagram obtained in step S3, the differential temperature for having the point of wooden horse can be higher than the differential temperature of no wooden horse point.
S5：Carry out normal distribution statistical analysis；
The data that step S4 is obtained carry out normal distribution statistical, and then using 3 δ confidence interval principles, differential temperature exists
Be considered normal circuit in section, except be then considered wooden horse circuit.
As a further improvement on the present invention：In step s 4, by Kalman filtering, the last observation knot of influence is filtered out
That line corresponding to hardware Trojan horse point is clearly displayed out, reaches better detection result by the noise of fruit.
As a further improvement on the present invention：In step sl, it is sampled by thermal camera.
As a further improvement on the present invention：The flow of the step S4 is：
S401：The temperature of any point P is represented with following formula on setting chip：
TP=TmeasurementP+TenvironmentP+TcircuitsP+TprocesP+eTPround
Wherein, TpIt is the total moisture content of P points；TmeasurementPMeasure temperature noise, TenvironmentPIt is ambient temperature noise, this
Two noise likes are all white Gaussian noises；TcircuitsPBe the point included circuit normal work caused by temperature；TprocessPIt is
Temperature deviation caused by process deviation；TProundIt is the temperature that the work of P points peripheral circuit generates；E is a parameter, represents P points week
Enclose influence of the temperature to P point temperature；
S402：When not including any circuit, temperature expression formula is reduced to：
TP=TmeasurementP+TenvironmentP+eTPround
When any contains hardware Trojan horse, temperature expression formula is rewritten as：
TP=TmeasurementP+TenvironmentP+
TcircuitsP+TprocessP+eTPround+TTrojan
S403：For different type point P1, P2, P3, its differentiated temperature expression formula is listed respectively：
ΔTP1=Δ TmeasurementP1+ΔTenvironmentP1
+ΔTprocessP1+eΔTP1round
ΔTP2=Δ TmeasurementP2+ΔTenvironmentP2
+ΔTprocessP2+eΔTP2round+TTrojan
ΔTP3=Δ TmeasurementP3+ΔTenvironmentP3+eΔTP3round
Wherein, P1 types point is the point comprising normal circuit, and corresponding points of this on pure chip are also normal circuit；
P2 types point is the point comprising normal circuit and wooden horse circuit, corresponding points of this on pure chip only comprising normal circuit and
Not comprising wooden horse circuit；P3 types point is white space, and without circuit, the point corresponded on pure chip does not include similarly
Circuit.
As a further improvement on the present invention：By belong to white Gaussian noise measurement temperature noise and ambient temperature noise from
It is erased in differential temperature expression formula, differential temperature expression formula is reduced to following form：
ΔTP1=Δ TprocessP1+eΔTP1round
ΔTP2=Δ TprocessP2+eΔTP2round+TTrojan
ΔTP3=e Δs TP3round。
Compared with prior art, the advantage of the invention is that：
1st, the hardware Trojan horse detection method that infrared image of the invention is combined with normal distribution analysis, when being worked using circuit
Heat signal analyzed, belong to bypass message analysis method.Method proposed by the present invention both overcome reversely verification it is high into
This, and the shortcomings that destroy chip, also without the short slab of functional test not realizability, thus be a kind of low cost, it can be achieved that
Technology.
2nd, the hardware Trojan horse detection method that infrared image of the invention is combined with normal distribution analysis, testing cost is low, only
Chip is needed to start normal work to complete the detection to chip, chip will not be damaged in itself, it is not required that very
The long time.Requirement to detection device is also only a high-precision thermal camera, a set of cooling system and set of complementary
Software systems.
3rd, the hardware Trojan horse detection method that infrared image of the invention is combined with normal distribution analysis, accuracy of detection is high, warp
Experiment is crossed, it is 10 that method of the invention, which can detect energy consumption magnitude,-3Hardware Trojan horse.
Description of the drawings
Fig. 1 is the flow diagram of the method for the present invention.
Fig. 2 is principle schematic of the present invention in concrete application example.
Fig. 3 is the infrared image schematic diagram of present invention chip in a concrete application example.
Fig. 4 is result schematic diagram of the present invention in concrete application example.
Fig. 5 is the schematic diagram one that the present invention carries out normal distribution statistical analysis in concrete application example.
Fig. 6 is the schematic diagram two that the present invention carries out normal distribution statistical analysis in concrete application example.
Fig. 7 is the schematic diagram three that the present invention carries out normal distribution statistical analysis in concrete application example.
Fig. 8 is the schematic diagram four that the present invention carries out normal distribution statistical analysis in concrete application example.
Fig. 9 is the schematic diagram five that the present invention carries out normal distribution statistical analysis in concrete application example.
Figure 10 is the schematic diagram six that the present invention carries out normal distribution statistical analysis in concrete application example.
Specific embodiment
The present invention is described in further details below with reference to Figure of description and specific embodiment.
Hardware Trojan horse is made of two parts --- triggering part and loading section.Triggering part can be constantly in work shape
State, because it needs the state of observation circuit and the activation loading section work specific at the time of.In running order triggering
Part may require that consumption energy, so as to generate heat and to space scattering.This heat can be captured by thermal camera, this is just
It is the variation caused by hardware Trojan horse on heat bypass message, the present invention is detected using this heat bypass message.
As depicted in figs. 1 and 2, the hardware Trojan horse detection method that infrared image of the invention is combined with normal distribution analysis,
Its step is：
S1：Capture infrared image.
The pure chip sample of one piece of no hardware Trojan horse and the chip under test of one piece of same model is allowed to start simultaneously at work,
Then with the infrared image of both in thermal camera capture a period of time.
S2：First time difference is carried out to the infrared image of acquisition.
Within a period of time sampled with thermal camera, each sampling instant has a pair of of infrared image ---
They carry out difference to the two, obtain a differentiated infrared image respectively from chip under test and purified sample.
Aforesaid operations are carried out to a pair of of image at each moment, it is possible to obtain each moment chip under test with it is pure
The difference infrared image of net sample.
S3：Second of difference is carried out to the difference infrared image at each moment.
Each pixel in the difference infrared image that first time difference is obtained extracts, its differential temperature is drawn
In the coordinate diagram using the time as abscissa.
Since the information of the upper no wooden horse circuit of chip under test will have been supported by the corresponding circuits in pure chip sample
Disappear, and there is wooden horse circuit there is no corresponding circuit on pure chip on chip under test, therefore have the difference temperature of the point of wooden horse
Degree can be higher than the differential temperature of no wooden horse point.
S4：Judge.
In the coordinate diagram obtained in step S3, the differential temperature for having the point of wooden horse can be higher than the differential temperature of no wooden horse point.
S5：Normal distribution statistical is analyzed；
For excluding the interference of detection of the ambient noise to wooden horse.The value of differential temperature meets normal distribution, then using 3
δ confidence interval principles, differential temperature are considered normal circuit in section, except be then considered wooden horse circuit, will obtain
The data arrived carry out normal distribution statistical>>Using 3 δ confidence interval principles, differential temperature is considered normal electricity in section
Road, except be then considered wooden horse circuit.Referring to Fig. 5-Fig. 8, the data in figure in red circle are the information of hardware Trojan horse, referring to figure
9 and Figure 10 does not include hardware Trojan horse in figure.
Step S4 is the information for identifying hardware Trojan horse by naked eyes in coordinate diagram, since wooden horse information is by ambient noise
Interference, it is usually little with the discrimination of normal circuit, in this case can be by identification person by the result of naked eyes identification
The interference of people's subjective factor.There is normal distribution statistical method, the discriminating of hardware Trojan horse can break away from factor and individual subjective factor
Interference, and have a unified mathematics scale.
After the hardware Trojan horse of different energy consumption magnitudes is implanted on FPGA, the FPGA is detected using this method, simultaneously
It it is not implanted into hardware Trojan horse using one contains only the FPGA of pure circuit and compareed as female parent.The experimental results showed that energy consumption is accounted for
Hardware Trojan horse than more than 0.11%, method of the invention are all successfully detected.
Further, in the preferred embodiment, in step s 4, can also influence be filtered out most by Kalman filtering
The noise of result is observed afterwards, so as to clearly display out that line corresponding to hardware Trojan horse point, reaches better detection
Effect.
As shown in figure 3, in a concrete application example, the infrared image schematic diagram for providing a chip is as follows, it is assumed that A
It is pure chip, B is chip under test.With this figure come the problem of illustrating region that hardware Trojan horse is implanted.
See chip to be measured, that is, B first, the pixel on chip can be divided into three types, the first type altogether
Type P1 type points, that is, the point comprising normal circuit, corresponding points of this on pure chip are also normal circuit.Second
It is P2 types point, that is, the point comprising normal circuit and wooden horse circuit, corresponding points of this on pure chip are only comprising just
Normal circuit and not comprising wooden horse circuit.The third is P3 types point, which is white space, without circuit, is corresponded to pure
Point on chip does not include circuit similarly.
During chip production, there are two types of process deviations.A kind of is the process deviation between chip and chip, referred to as
Process deviation between piece；Another kind is the deviation between difference in same chip, referred to as on piece process deviation.These techniques are inclined
Difference will also result in the difference of chip operating temperature, so as to influence the detection of hardware Trojan horse.In general, process deviation is long-range between piece
In on piece process deviation.
The temperature of any point P can be represented with following formula on chip：
TP=TmeasurementP+TenvironmentP+TcircuitsP+TprocessP+eTPround
Wherein, TpIt is the total moisture content of P points.TmeasurementPMeasure temperature noise, TenvironmentPIt is ambient temperature noise, this
Two noise likes are all white Gaussian noises.TcircuitsPBe the point included circuit normal work caused by temperature.TprocessPIt is
Temperature deviation caused by process deviation.TProundIt is the temperature that the work of P points peripheral circuit generates.E is a parameter, represents P points week
Enclose influence of the temperature to P point temperature.
So when not including any circuit, temperature expression formula is reduced to：
TP=TmeasurementP+TenvironmentP+eTPround
When any contains hardware Trojan horse, temperature expression formula is rewritten as：
TP=TmeasurementP+TenvironmentP+
TcircuitsP+TprocessP+eTPround+TTrojan
So for P1, P2, P3 type point in figure, its differentiated temperature expression formula can be listed respectively：
ΔTP1=Δ TmeasurementP1+ΔTenvironmentP1
+ΔTprocessP1+eΔTP1round
ΔTP2=Δ TmeasurementP2+ΔTenvironmentP2
+ΔTprocessP2+eΔTP2round+TTrojan
ΔTP3=Δ TmeasurementP3+ΔTenvironmentP3+eΔTP3round
All it is white Gaussian noise due to measuring temperature noise and ambient temperature noise, gaussian filtering method can be used to remove,
Therefore it for the ease of analysis, is erased from differential temperature expression formula, so as to which differential temperature expression formula to be reduced to following form：
ΔTP1=Δ TprocessP1+eΔTP1round
ΔTP2=Δ TprocessP2+eΔTP2round+TTrojan
ΔTP3=e Δs TP3round
If as shown in figure 4, the differential temperature of all pixels point is drawn in by horizontal axis of the time in coordinate diagram, due to technique
The continuity of deviation, the information of P1 types point can form a process deviation band, and P3 types point can then form another and be sunken to
The process deviation band of bottom.P2 types point can then float on top, so as to reach hardware Trojan horse because of a more hardware Trojan horse item
Information it is clear, so as to detect hardware Trojan horse.
The above is only the preferred embodiment of the present invention, protection scope of the present invention is not limited merely to above-described embodiment,
All technical solutions belonged under thinking of the present invention all belong to the scope of protection of the present invention.It should be pointed out that for the art
For those of ordinary skill, several improvements and modifications without departing from the principles of the present invention should be regarded as the protection of the present invention
Range.
Claims (5)
1. the hardware Trojan horse detection method that a kind of infrared image and normal distribution analysis combine, which is characterized in that step is：
S1：Capture infrared image；
The pure chip sample of one piece of no hardware Trojan horse and the chip under test of one piece of same model is allowed to start simultaneously at work, then
With the infrared image of both in image capture device capture a period of time；
S2：First time difference is carried out to the infrared image of acquisition；
Within a period of time sampled, each sampling instant has a pair of of infrared image, carries out difference to the two, obtains
One differentiated infrared image；Aforesaid operations are carried out to a pair of of image at each moment, just obtain each moment quilt
Survey the difference infrared image of chip and purified sample；
S3：Second of difference is carried out to the difference infrared image at each moment.
First time difference obtain difference infrared image in each pixel extract, by its differential temperature be drawn in
Time is in the coordinate diagram of abscissa；
S4：Judge；
In the coordinate diagram obtained in step S3, the differential temperature for having the point of wooden horse can be higher than the differential temperature of no wooden horse point；
S5：Carry out normal distribution statistical analysis；
The data that step S4 is obtained carry out normal distribution statistical, and then using 3 δ confidence interval principles, differential temperature is in section
Interior is considered normal circuit, except be then considered wooden horse circuit.
2. the hardware Trojan horse detection method that infrared image according to claim 1 is combined with normal distribution analysis, feature
It is, in step s 4, by Kalman filtering, filters out the noise for influencing last observation result, hardware Trojan horse point institute is right
That line answered clearly displays out, reaches better detection result.
3. the hardware Trojan horse detection method that infrared image according to claim 1 is combined with normal distribution analysis, feature
It is, in step sl, is sampled by thermal camera.
4. the hardware Trojan horse detection method that the infrared image according to claims 1 or 2 or 3 is combined with normal distribution analysis,
It is characterized in that, the flow of the step S4 is：
S401：The temperature of any point P is represented with following formula on setting chip：
TP=PmeasurementP+TenvironmentP+TcircuitsP+TprocessP+eTPround
Wherein, TpIt is the total moisture content of P points；TmeasurementPMeasure temperature noise, TenvironmentPIt is ambient temperature noise, this two class
Noise is all white Gaussian noise；TcircuitsPBe the point included circuit normal work caused by temperature；TprocessPIt is technique
Temperature deviation caused by deviation；TProundIt is the temperature that the work of P points peripheral circuit generates；E is a parameter, is represented warm around P points
Spend the influence to P point temperature；
S402：When not including any circuit, temperature expression formula is reduced to：
TP=TmeasurementP+TenvironmentP+eTPround
When any contains hardware Trojan horse, temperature expression formula is rewritten as：
TP=TmeasurementP+TenvironmetP+
TciruitsP+TprocessP+eTPround+TTrojan
S403：For different type point P1, P2, P3, its differentiated temperature expression formula is listed respectively：
ΔTP1=Δ TmeasurementP1+ΔTenvironmentP1
+ΔTprocessP1+eΔTPlround
ΔTP2=Δ TmeasurementP2+ΔTenvironmentP2
+ΔTprocessP2+eΔTP2round+TTrojan
ΔTP3=Δ TmeasurementP3+ΔTenvironmentP3+eΔTP3round
Wherein, P1 types point is the point comprising normal circuit, and corresponding points of this on pure chip are also normal circuit；P2 classes
Type point is the point comprising normal circuit and wooden horse circuit, and corresponding points of this on pure chip are only comprising normal circuit without wrapping
Circuit containing wooden horse；P3 types point is white space, and without circuit, the point corresponded on pure chip does not include electricity similarly
Road.
5. the hardware Trojan horse detection method that infrared image according to claim 4 is combined with normal distribution analysis, feature
It is, the measurement temperature noise for belonging to white Gaussian noise and ambient temperature noise is erased from differential temperature expression formula, it will be poor
Point temperature expression formula is reduced to following form：
ΔTP1=Δ TprocessP1+eΔTPlround
ΔTP2=Δ TprocessP2+eΔTP2round+TTrojan
ΔTP3=e Δs TP3round。
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| CN109272502A (en) * | 2018-09-28 | 2019-01-25 | 电子科技大学 | PCB hardware security detection method based on temperature field-effect |
| CN110866290A (en) * | 2018-11-21 | 2020-03-06 | 哈尔滨安天科技集团股份有限公司 | Chip malicious tampering detection method and device, electronic equipment and storage medium |
| CN110232278A (en) * | 2019-05-10 | 2019-09-13 | 中国人民解放军国防科技大学 | Frequency-reducing time-sharing A2 Trojan horse detection method and device based on composite ring oscillator |
| CN110232278B (en) * | 2019-05-10 | 2021-03-16 | 中国人民解放军国防科技大学 | Frequency-reducing time-sharing A2 Trojan horse detection method and device based on composite ring oscillator |
| CN110298200A (en) * | 2019-07-05 | 2019-10-01 | 电子科技大学 | Asic chip hardware back door detection method based on temperature statistics signature analysis |
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