CN108256357B - 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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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
The invention mainly relates to the field of hardware Trojan detection of integrated circuits, in particular to a hardware Trojan detection method combining infrared images and normal distribution analysis.
Background
As the cost of integrated circuit production lines increases, many integrated circuit designers choose to outsource this link of integrated circuit manufacturing to third party foundries. This outsourcing creates a trustworthiness risk for the integrated circuit, where additional malicious circuits, known as hardware trojans, may be implanted in the original circuit. Hardware Trojan horse detection has become an important subject in the field of chip security, and various detection methods are proposed, such as a functional and structural test method, a testability design method, a reverse dissection method, a bypass signal detection method and the like.
The reverse anatomical verification method, which is the most common destructive hardware Trojan horse detection method, is essentially an analysis technique based on invasive examination. Generally, an original circuit obtained by production is dissected, a metal wire pattern in a photo extracted after photographing is compared with a picture extracted from a layout file, the difference between the patterns is analyzed, if the comparison result shows that a chip layout extracted reversely is matched with the original layout, the fact that a hardware Trojan does not exist in the layout is indicated, and otherwise, the circuit can be implanted into the hardware Trojan. The disadvantages of the reverse anatomical validation are: firstly, the process of chip reverse analysis is very complex, and the verification process is slow; secondly, the method is a destructive hardware Trojan horse detection mode, and the detected chip is completely scrapped after detection is finished and cannot be used continuously.
In the functional test method, the hardware trojan inevitably needs to tamper the normal function of the chip circuit in order to damage the normal function of the original circuit or leak confidential data in the chip. The functional test is to apply a test vector on the input port of the circuit, compare whether the output is consistent with the normal state, and thus evaluate whether the circuit is maliciously implanted into a hardware Trojan. Theoretically, if all the working states and the redundant states in the chip can be traversed, the hardware trojan can be triggered to find whether the hardware trojan is inserted into the circuit or not, but at present, the scale of a plurality of chips is large, the complexity is high, the breadth of the state space is exponentially increased, and the cost of a function test method is too large to realize.
In the bypass information analysis method, information such as power consumption, temperature, and delay generated when a circuit operates is referred to as bypass information. The principle of analyzing the hardware Trojan based on the bypass is that after the circuit is implanted into a hardware Trojan circuit, the original composition and size of the circuit can be tampered to different degrees, so that the circuit implanted into the hardware Trojan shows different bypass information characteristics from the original circuit. Therefore, whether the hardware trojan is implanted in the chip or not can be identified by sampling and checking the bypass information of the circuit.
In summary, the above methods have problems of excessive cost or poor detection effect.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the technical problems in the prior art, the invention provides the hardware Trojan horse detection method combining the infrared image and the normal distribution analysis, which has high detection precision, low detection cost and high detection efficiency.
In order to solve the technical problems, the invention adopts the following technical scheme:
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 within a period of time;
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; performing the operation on the pair of images at each moment to obtain a differential infrared image of the detected chip and the pure sample at each moment;
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;
in the graph obtained in step S3, the difference temperature of the trojan-containing point is higher than the difference temperature of the trojan-free point.
S5: carrying out normal distribution statistical analysis;
and (4) performing normal distribution statistics on the data obtained in the step (S4), and considering that the differential temperature is in the interval as a normal circuit and the differential temperature is out of the interval as a Trojan circuit by using a 3 delta confidence interval principle.
As a further improvement of the invention: in step S4, by kalman filtering, noise affecting the final observation result is filtered out, and the line corresponding to the hardware trojan point is clearly displayed, thereby achieving a better detection effect.
As a further improvement of the invention: in step S1, sampling is performed by the infrared camera.
As a further improvement of the invention: the flow of step S4 is:
s401: the temperature at any point P on the chip is set as follows:
TP＝TmeasurementP+TenvironmentP+TcircuitsP+TprocesP+eTPround
wherein, TpIs the total temperature at point P; t ismeasurementPMeasuring temperature noise, TenvironmentPIs the environmentTemperature noise, both of which are white gaussian noise; t iscircuitsPIs the temperature at which the circuit contained in the spot operates normally; t isprocessPIs the temperature deviation caused by process deviation; t isProundIs the temperature generated by the circuit working around the point P; e is a parameter representing the effect of temperature around point P on the temperature at point P;
s402: when no circuitry is involved at all, the temperature expression is reduced to:
TP＝TmeasurementP+TenvironmentP+eTPround
when a point contains a hardware trojan, the temperature expression is rewritten as:
TP＝TmeasurementP+TenvironmentP+
TcircuitsP+TprocessP+eTPround+TTrojan
s403: for the different type points P1, P2, P3, their differentiated temperature expressions are listed, respectively:
ΔTP1＝ΔTmeasurementP1+ΔTenvironmentP1
+ΔTprocessP1+eΔTP1round
ΔTP2＝ΔTmeasurementP2+ΔTenvironmentP2
+ΔTprocessP2+eΔTP2round+TTrojan
ΔTP3＝ΔTmeasurementP3+ΔTenvironmentP3+eΔTP3round
the P1 type point is a point containing a normal circuit, and the corresponding point of the point on the pure chip is also the normal circuit; the P2 type point is a point containing a normal circuit and a trojan circuit, and the corresponding point on the clean chip only contains the normal circuit and does not contain the trojan circuit; the P3 type dots are empty areas with no circuitry, corresponding to dots on a clean chip that also contain no circuitry.
As a further improvement of the invention: erasing the measured temperature noise and the ambient temperature noise which belong to Gaussian white noise from the differential temperature expression, and simplifying the differential temperature expression into the following form:
ΔTP1＝ΔTprocessP1+eΔTP1round
ΔTP2＝ΔTprocessP2+eΔTP2round+TTrojan
ΔTP3＝eΔTP3round。
compared with the prior art, the invention has the advantages that:
1. the invention discloses a hardware Trojan horse detection method combining infrared images and normal distribution analysis, which utilizes a heat signal generated when a circuit works to analyze and belongs to a bypass information analysis method. The method provided by the invention overcomes the defects of high cost and chip damage of reverse verification, and has no short board with unrealistic function test, thereby being a low-cost and realizable technology.
2. The hardware Trojan horse detection method combining the infrared image and the normal distribution analysis has low detection cost, can finish the detection of the chip only by starting the normal work of the chip, does not damage the chip and does not need long time. The requirements for the detection equipment are only one high-precision infrared camera, one set of heat dissipation system and one set of matched software system.
3. The hardware Trojan horse detection method combining the infrared image and the normal distribution analysis has high detection precision, and experiments prove that the method can detect the energy consumption level of 10-3The hardware trojan.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention.
Fig. 2 is a schematic diagram of the principle of the invention in a specific application example.
FIG. 3 is a diagram of an infrared image of a chip in an embodiment of the present invention.
FIG. 4 is a graph showing the results of the present invention in a specific application example.
FIG. 5 is a first schematic diagram of a normal distribution statistical analysis performed in an embodiment of the present invention.
FIG. 6 is a second schematic diagram of a normal distribution statistical analysis performed in an embodiment of the present invention.
FIG. 7 is a third schematic diagram of a statistical analysis of normal distribution in a specific application example of the present invention.
FIG. 8 is a fourth schematic diagram of a statistical analysis of normal distribution in a specific application example of the present invention.
FIG. 9 is a fifth schematic diagram of a statistical analysis of normal distribution in an embodiment of the present invention.
FIG. 10 is a sixth schematic diagram illustrating a statistical analysis of normal distribution in an exemplary embodiment of the present invention.
Detailed Description
The invention will be described in further detail below with reference to the drawings and specific examples.
The hardware trojan consists of two parts, a trigger part and a load part. The trigger part will always be in operation because it needs to monitor the state of the circuit and activate the load part to operate at a specific moment. The active trigger portion may consume energy, thereby generating heat and scattering it into space. This heat can be captured by an infrared camera, which is a change in the heat bypass information caused by the hardware trojan, and the present invention utilizes this heat bypass information for detection.
As shown in fig. 1 and fig. 2, the hardware Trojan horse detection method combining the infrared image and the normal distribution analysis of the present invention comprises the following steps:
s1: an infrared image is captured.
A pure chip sample without a hardware Trojan horse and a tested chip with the same type are enabled to work simultaneously, and then an infrared camera is used for capturing infrared images of the two chips within a period of time.
S2: and carrying out first difference on the obtained infrared image.
During a period of sampling by the infrared camera, a pair of infrared images, which are respectively from the chip to be detected and the pure sample, are obtained at each sampling moment, and the two infrared images are differentiated to obtain a differentiated infrared image.
By performing the above operation on the pair of images at each moment, a differential infrared image of the chip to be tested and the pure sample at each moment can be obtained.
S3: and carrying out second difference on the differential infrared image at each moment.
Each pixel point in the differential infrared image obtained by the first difference is extracted, and the differential temperature is drawn on a coordinate graph with time as an abscissa.
Since the information of the Trojan-free circuit on the tested chip is offset by the corresponding circuit on the clean chip sample, and the Trojan-containing circuit on the tested chip has no corresponding circuit on the clean chip, the differential temperature of the Trojan-containing point is higher than that of the Trojan-free point.
S4: and (6) judging.
In the graph obtained in step S3, the difference temperature of the trojan-containing point is higher than the difference temperature of the trojan-free point.
S5: carrying out normal distribution statistical analysis;
to exclude ambient noise from interfering with the detection of trojans. And if the value of the differential temperature accords with normal distribution, adopting a 3 delta confidence interval principle, regarding the differential temperature in the interval as a normal circuit, and regarding the differential temperature outside the interval as a Trojan circuit, namely performing normal distribution statistics on the obtained data > and regarding the differential temperature in the interval as a normal circuit and regarding the differential temperature outside the interval as a Trojan circuit by utilizing the 3 delta confidence interval principle. Referring to fig. 5-8, the data in the red circle is the information of the hardware trojan, and referring to fig. 9 and 10, the hardware trojan is not included in the figure.
Step S4 is to identify the information of the hardware trojan by naked eyes on the coordinate graph, because the trojan information is interfered by the environmental noise, the discrimination from the normal circuit is often not great, in this case, the result identified by naked eyes is interfered by the personal subjective factor of the identifier. With the normal distribution statistical method, the identification of the hardware trojan can get rid of the interference of personal subjective factors, and has a uniform mathematical scale.
After hardware trojans with different energy consumption levels are implanted into the FPGA, the FPGA is detected by the method, and meanwhile, the FPGA which is not implanted with the hardware trojans and only contains pure circuits is used as a female parent for comparison. Experimental results show that the method disclosed by the invention can be used for successfully detecting hardware trojans with the energy consumption of more than 0.11%.
Further, in a preferred embodiment, in step S4, noise affecting the final observation result may be filtered out through kalman filtering, so that the line corresponding to the hardware trojan point is clearly displayed, and a better detection effect is achieved.
In one specific example, an infrared image of a chip is shown as follows, assuming that A is a clean chip and B is a chip under test, as shown in FIG. 3. The problem of the area where the hardware trojan is implanted is illustrated with this figure.
First, it is seen that the pixels on the chip to be tested, i.e. B, can be classified into three types, i.e. the first type P1 type point, i.e. the point containing the normal circuit, and the corresponding point of the point on the pure chip is also the normal circuit. The second is a P2 type dot, i.e., a dot containing normal and trojan circuits, the corresponding dot on a clean chip containing only normal circuits and no trojan circuits. The third is a P3 type dot, which is a blank area without circuitry, corresponding to a dot on a clean chip that also contains no circuitry.
During chip production, there are two process variations. One is the process variation from chip to chip, called the inter-chip process variation; the other is the deviation between different points within the same chip, called on-chip process deviation. These process variations can also cause differences in the operating temperature of the chip, thereby affecting the detection of hardware trojans. In general, the inter-chip process variation is much larger than the on-chip process variation.
The temperature at any point P on the chip can be represented by the following equation:
TP＝TmeasurementP+TenvironmentP+TcircuitsP+TprocessP+eTPround
wherein, TpIs the total temperature at point P. T ismeasurementPMeasuring temperature noise, TenvironmentPIs ambient temperature noise, and both of these types of noise are white gaussian noise. T iscircuitsPIs the temperature at which the circuitry contained at that point operates normally. T isprocessPIs the temperature deviation caused by the process deviation. T isProundIs the temperature generated by the circuit operating around the P point. e is a parameter representing the effect of temperature around point P on the temperature at point P.
Then when no circuitry is contained at all, the temperature expression reduces to:
TP＝TmeasurementP+TenvironmentP+eTPround
when a point contains a hardware trojan, the temperature expression is rewritten as:
TP＝TmeasurementP+TenvironmentP+
TcircuitsP+TprocessP+eTPround+TTrojan
then for the P1, P2, P3 type points in the figure, their differentiated temperature expressions can be listed respectively:
ΔTP1＝ΔTmeasurementP1+ΔTenvironmentP1
+ΔTprocessP1+eΔTP1round
ΔTP2＝ΔTmeasurementP2+ΔTenvironmentP2
+ΔTprocessP2+eΔTP2round+TTrojan
ΔTP3＝ΔTmeasurementP3+ΔTenvironmentP3+eΔTP3round
because the measured temperature noise and the ambient temperature noise are both gaussian white noise and can be removed by using a gaussian filtering method, the measured temperature noise and the ambient temperature noise are erased from the differential temperature expression for the convenience of analysis, so that the differential temperature expression is simplified into the following form:
ΔTP1＝ΔTprocessP1+eΔTP1round
ΔTP2＝ΔTprocessP2+eΔTP2round+TTrojan
ΔTP3＝eΔTP3round
as shown in fig. 4, if the differential temperatures of all the pixel points are plotted on the graph with time as the axis, the information of the P1 type point forms a process deviation band due to the continuity of the process deviation, and the P3 type point forms another process deviation band sinking to the bottom. The type P2 point floats on the top because of one more hardware trojan item, so that the hardware trojan information is displayed, and the hardware trojan is detected.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (3)
1. A hardware Trojan horse detection method combining infrared images and normal distribution analysis is characterized by comprising 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 within a period of time;
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; performing the operation on the pair of images at each moment to obtain a differential infrared image of the detected chip and the pure sample at each moment;
s3: carrying out second difference on the difference 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;
on the graph obtained in step S3, the differential temperature of the point with the trojan is higher than the differential temperature of the point without the trojan;
s5: carrying out normal distribution statistical analysis;
performing normal distribution statistics on the data obtained in the step S4, and considering that the differential temperature is in the interval as a normal circuit and the differential temperature is out of the interval as a Trojan circuit by using a 3 delta confidence interval principle;
the flow of step S4 is:
s401: the temperature at any point P on the chip is set as follows:
TP＝TmeasurementP+TenvironmentP+TcircuitsP+TprocessP+eTPround
wherein, TpIs the total temperature at point P; t ismeasurementPMeasuring temperature noise, TenvironmentPIs the ambient temperature noise, and both the two types of noise are Gaussian white noise; t iscircuitsPIs the temperature at which the circuit contained in the spot operates normally; t isprocessPIs the temperature deviation caused by process deviation; t isProundIs the temperature generated by the circuit working around the point P; e is a parameter representing the effect of temperature around point P on the temperature at point P;
s402: when no circuitry is involved at all, the temperature expression is reduced to:
TP＝TmeasurementP+TenvironmentP+eTPround
when a point contains a hardware trojan, the temperature expression is rewritten as:
TP＝TmeasurementP+TenvironmentP+TcircuitsP+TprocessP+eTPround+TTrojan
wherein, TTrojanTemperature generated for the trojan circuit;
s403: for the different type points P1, P2, P3, their differentiated temperature expressions are listed, respectively:
ΔTP1＝ΔTmeasurementP1+ΔTenvironmentP1+ΔTprocessP1+eΔTP1round
ΔTP2＝ΔTmeasurementP2+ΔTenvironmentP2+ΔTprocessP2+eΔTP2round+TTrojan
ΔTP3＝ΔTmeasurementP3+ΔTenvironmentP3+eΔTP3round
the P1 type point is a point containing a normal circuit, and the corresponding point of the point on the pure chip is also the normal circuit; the P2 type point is a point containing a normal circuit and a trojan circuit, and the corresponding point on the clean chip only contains the normal circuit and does not contain the trojan circuit; the P3 type dots are blank areas without circuits, and the dots corresponding to the pure chips also do not contain circuits;
erasing the measured temperature noise and the ambient temperature noise which belong to Gaussian white noise from the differential temperature expression, and simplifying the differential temperature expression into the following form:
ΔTP1＝ΔTprocessP1+eΔTP1roundn
ΔTP2＝ΔTprocessP2+eΔTP2round+TTrojan
ΔTP3＝eΔTP3round。
2. the hardware Trojan detection method combining the infrared image and the normal distribution analysis according to claim 1, wherein in step S4, the noise affecting the final observation result is filtered out through kalman filtering, and the line corresponding to the hardware Trojan point is clearly displayed, thereby achieving a better detection effect.
3. The hardware Trojan horse detection method by combining infrared images and normal distribution analysis according to claim 1, wherein in step S1, sampling is performed by an infrared camera.
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| CN104614660A (en) * | 2015-01-09 | 2015-05-13 | 中国电子科技集团公司第五十八研究所 | Method for detecting hardware Trojan based on active optical watermark |
| US10303878B2 (en) * | 2016-01-22 | 2019-05-28 | Yu-Liang Wu | Methods and apparatus for automatic detection and elimination of functional hardware trojans in IC designs |
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| US10303878B2 (en) * | 2016-01-22 | 2019-05-28 | Yu-Liang Wu | Methods and apparatus for automatic detection and elimination of functional hardware trojans in IC designs |
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