CN112231695B - Attack method and system based on branch prediction mechanism and storage medium - Google Patents
Attack method and system based on branch prediction mechanism and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of information security, and provides an attack method and system based on a branch prediction mechanism and a storage medium. The method comprises the following steps: constructing a branch prediction information matrix aiming at the target program based on a branch prediction mechanism; selecting plaintext data which accords with the input data rule of the target program; taking the selected plaintext data as input data or partial input data of the target program, and collecting time sample data of the operation of the target program; and determining secret information corresponding to the time sample data according to the branch prediction information matrix. According to the invention, the whole time data of the operation of the target program is utilized in the attack process, the requirement on the acquisition time point position is low, the leaked information (secret information) can be integrally depicted, the control requirement on the target program is reduced, the attack is carried out in a statistical analysis mode, the leaked depiction is more accurate, and the accuracy of time attack detection is improved.
Description
Technical Field
The present invention relates to the field of information security technologies, and in particular, to an attack method based on a branch prediction mechanism, an attack system based on a branch prediction mechanism, and a storage medium.
Background
Side channel attacks are a common technique in the field of hardware security. The side channel attack refers to utilizing various side channel leakage information in the circuit working process, such as: energy consumption, time, faults, electromagnetic radiation and the like, and the secret information is extracted by establishing the relation between the leaked information and key information (such as a secret key) of the cryptographic algorithm. In a computer processor, due to the complexity of the hardware environment, the traditional energy consumption, electromagnetism and other side channel information are difficult to effectively collect and utilize, and the time side channel attack is a main side channel security threat.
In the time attack research based on the processor architecture, besides the cache attack which is widely researched, the branch prediction unit can cause information leakage in the performance optimization process of the micro-architecture. The success or failure of the prediction of the branch direction by the branch prediction unit brings a difference of response time, and the difference is used for attacking a cryptographic algorithm and a system security boundary. The existing side channel attack methods are various, one is: on the cryptographic algorithm attack level, a key which is operated on a processor is obtained by utilizing a spy process through branch prediction analysis, and the processor using a branch predictor is attacked; the other is as follows: in the case of using only the preemptive multitasking, the Montgomery modular multiplication final reduction is detected by counting the number of RSB entries using a Return Stack Buffer (RSB) for branch prediction. For the system security boundary attack level, information is illegally acquired by utilizing the characteristic of branch prediction speculative execution in processor design, or by utilizing branch prediction BTB conflict, ASLR is bypassed to find the layout of a kernel-level virtual address space and a user-level virtual address space. The attack based on the branch prediction mechanism mainly aims at constructing an attack vector, does not depict and utilize overall leaked statistical information, and is low in accuracy of time attack detection.
Disclosure of Invention
The embodiment of the invention aims to provide an attack method and system based on a branch prediction mechanism, so that the leakage information is integrally depicted, and the accuracy of time attack detection is improved.
In order to achieve the above object, a first aspect of the present invention provides an attack method based on a branch prediction mechanism, the method including:
constructing a branch prediction information matrix aiming at the target program based on a branch prediction mechanism;
selecting plaintext data which accords with the input data rule of the target program;
taking the selected plaintext data as input data or partial input data of the target program, and collecting time sample data of the operation of the target program;
and determining secret information corresponding to the time sample data according to the branch prediction information matrix.
Further, the branch prediction information matrix includes a plurality of preset plaintext data and preset secret information values corresponding to the preset plaintext data.
Further, the building of the branch prediction information matrix for the target program based on the branch prediction mechanism includes:
setting an initial state of a branch prediction unit;
configuring the preset plaintext data and preset secret information values corresponding to the preset plaintext data based on the branch prediction component;
determining branch prediction information corresponding to the preset secret information value according to the branch prediction mechanism;
and constructing the branch prediction information matrix according to the branch prediction information corresponding to the preset plaintext data and the preset secret information value.
Further, the determining secret information corresponding to the time sample data according to the branch prediction information matrix includes:
acquiring branch prediction information of a preset secret information value corresponding to plaintext data input by the target program from the branch prediction information matrix;
determining a correlation coefficient between the branch prediction information of the preset secret information value and the time sample data;
and determining the actual secret information value corresponding to the plaintext data according to the correlation coefficient to serve as the secret information corresponding to the time sample data.
Further, the determining secret information corresponding to the time sample data according to the branch prediction information matrix further includes:
selecting a secret information value with the highest correlation coefficient corresponding to the plaintext data as an actual secret information value, and judging whether the actual secret information value meets a preset condition;
if yes, outputting the actual secret information value;
and if not, reselecting the plaintext data input into the target program.
A second aspect of the present invention provides an attack system based on a branch prediction mechanism, the system comprising:
the data acquisition module is used for selecting plaintext data which accords with an input data rule of a target program, taking the selected plaintext data as input data or partial input data of the target program, and acquiring time sample data of the operation of the target program;
and the data analysis module is used for constructing a branch prediction information matrix aiming at the target program based on a branch prediction mechanism and determining the secret information corresponding to the time sample data according to the branch prediction information matrix.
Further, the data analysis module includes:
a branch prediction information prefetch unit to:
setting an initial state of a branch prediction unit;
configuring preset plaintext data and preset secret information values corresponding to the preset plaintext data;
determining branch prediction information corresponding to the preset secret information value according to the branch prediction mechanism;
constructing the branch prediction information matrix according to the preset plaintext data and the branch prediction information corresponding to the preset secret information value;
and the time attack unit is used for determining the secret information corresponding to the time sample data according to the branch prediction information matrix.
Further, the time attack unit is configured to:
acquiring branch prediction information of a preset secret information value corresponding to plaintext data input by the target program from the branch prediction information matrix;
determining a correlation coefficient between the branch prediction information of the preset secret information value and the time sample data;
and determining the actual secret information value corresponding to the plaintext data according to the correlation coefficient to serve as the secret information corresponding to the time sample data.
Further, the data acquisition module comprises:
the plaintext data selection unit is used for selecting the plaintext data which accords with the input data rule of the target program;
a program running unit for running the target program by using the selected plaintext data as input data or partial input data;
and the time acquisition unit is used for acquiring time sample data of the operation of the target program.
In another aspect, the present invention further provides a storage medium, on which computer program instructions are stored, and when executed, the computer program instructions implement the above-mentioned attack method based on the branch prediction mechanism.
According to the attack method and system based on the branch prediction mechanism, provided by the embodiment of the invention, the plaintext data input into the target program is selected, the time sample data of the operation of the target program is acquired based on the selected plaintext data, the secret information is determined according to the constructed branch prediction information matrix, the whole time data of the operation of the target program is utilized in the attack process, the requirement on the acquisition time point position is low, the leaked information (secret information) can be integrally depicted, the control requirement on the target program is reduced, the attack is carried out in a statistical analysis mode, the depiction of the leakage is more accurate, and the accuracy of time attack detection is improved.
In addition, the invention is not limited to a specific processor or a specific branch prediction mechanism, can be applied to different processor scenes, and has strong universality. Moreover, the method can effectively attack the sensitive data of various target programs under the condition of meeting the attack scene, has low requirement on the capability of an attacker, and has strong system expandability.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
FIG. 1 is a flow chart of an attack method based on a branch prediction mechanism according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for constructing a branch prediction information matrix according to an attack method based on a branch prediction mechanism provided by an embodiment of the present invention;
FIG. 3 is a flowchart of obtaining actual secret information of an attack method based on a branch prediction mechanism according to an embodiment of the present invention;
FIG. 4 is a block diagram of an attack system based on a branch prediction mechanism according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
FIG. 1 is a flowchart of an attack method based on a branch prediction mechanism according to an embodiment of the present invention. As shown in fig. 1, an embodiment of the present invention provides an attack method based on a branch prediction mechanism, where the method includes:
the method comprises the following steps of firstly, constructing a branch prediction information matrix aiming at a target program based on a branch prediction mechanism.
The branch prediction information matrix comprises a plurality of preset plaintext data and preset secret information values corresponding to the preset plaintext data.
Fig. 2 is a flowchart of constructing a branch prediction information matrix of an attack method based on a branch prediction mechanism according to an embodiment of the present invention. As shown in fig. 2, the steps of constructing the branch prediction information matrix in the present embodiment are:
s11, setting the initial state of the branch prediction component, for example, modifying the state of the branch prediction component to any initial state by a controllable program.
And S12, configuring preset plaintext data and preset secret information values corresponding to the preset plaintext data based on the branch prediction component. Specifically, possible input plaintext data and possible secret information values are determined empirically for branches of the target program, the possible input plaintext data is used as preset plaintext data, and the possible secret information values are used as preset secret information values.
And S13, determining branch prediction information corresponding to the preset secret information value according to the branch prediction mechanism. The branch prediction information comprises two types of prediction success and prediction failure.
S14, constructing the branch prediction information matrix according to the branch prediction information corresponding to the preset plaintext data and the preset secret information values. Specifically, all possible combinations of branch prediction information are encoded (for example, 1 indicates that prediction is successful, 0 indicates that prediction is failed, and all branch prediction information is encoded in a form of a combination of 1 and 0), and a branch prediction information matrix is established when it is determined that a combination of preset plaintext data (possibly input plaintext data) and preset secret information values (possibly secret information values) covers all possibilities. For example, the number of possible plaintext data to be input is N, the possible secret information takes the value of M, and the size of the established branch prediction information matrix is N × M.
And secondly, selecting the plaintext data which accords with the input data rule of the target program. For example, the target program only contains one input data, the input data is binary data with 2 bits, and the random number generator is adopted to select the input plaintext data within the value range of 0-3.
And thirdly, taking the selected plaintext data as input data or partial input data of the target program, and collecting time sample data of the operation of the target program. Specifically, any timing scheme supported by the target processor may be employed to collect time sample data of the target program.
And fourthly, determining secret information corresponding to the time sample data according to the branch prediction information matrix.
Fig. 3 is a flowchart for obtaining actual secret information of an attack method based on a branch prediction mechanism according to an embodiment of the present invention. As shown in fig. 3, the steps of acquiring actual secret information in the present embodiment are:
and S41, obtaining branch prediction information of the value of the preset secret information corresponding to the plaintext data input by the target program from the branch prediction information matrix.
And S42, determining the correlation coefficient between the branch prediction information of the preset secret information value and the time sample data. For example, if the number of plaintext data set elements selected to be input into the target program is n, the number of possible values of the secret information is M, and the size of the time sample data is n × 1, the size of the branch prediction information data is n × M.
And S43, determining the actual secret information value corresponding to the plaintext data according to the correlation coefficient to serve as the secret information corresponding to the time sample data. Specifically, a secret information value with the highest correlation coefficient corresponding to the plaintext data may be selected as an actual secret information value, and it is determined whether the actual secret information value satisfies a preset condition (e.g., whether the credibility of the actual secret information value is within a preset credibility range); if yes, outputting the actual secret information value; and if not, reselecting the plaintext data of the input target program.
In the above method steps provided in this embodiment, the first step (building a branch prediction information matrix for the target program based on the branch prediction mechanism) may be completed after the third step and before the fourth step.
FIG. 4 is a block diagram of an attack system based on a branch prediction mechanism according to an embodiment of the present invention. As shown in fig. 4, an attack system based on a branch prediction mechanism according to an embodiment of the present invention includes a data collection module and a data analysis module. The data acquisition module is used for selecting the plaintext data which accords with the input data rule of the target program, taking the selected plaintext data as the input data or part of the input data of the target program, and acquiring the time sample data of the operation of the target program. The data analysis module is used for constructing a branch prediction information matrix aiming at the target program based on a branch prediction mechanism, and determining secret information corresponding to the time sample data according to the branch prediction information matrix.
The data acquisition module comprises a plaintext data selection unit, a program operation unit and a time acquisition unit. The plaintext data selection unit is used for selecting the plaintext data which accords with the input data rule of the target program. And the program running unit is used for running the target program by taking the selected plaintext data as input data or part of the input data. The time acquisition unit is used for acquiring time sample data of the operation of the target program.
The data analysis module comprises a branch prediction information pre-fetching unit and a time attack unit. The branch prediction information prefetch unit to: setting an initial state of a branch prediction unit; configuring preset plaintext data and preset secret information values corresponding to the preset plaintext data; determining branch prediction information corresponding to the preset secret information value according to the branch prediction mechanism; and constructing the branch prediction information matrix according to the preset plaintext data and the branch prediction information corresponding to the preset secret information value. The time attack unit is used for determining secret information corresponding to the time sample data according to the branch prediction information matrix. The method specifically comprises the following steps: acquiring branch prediction information of a preset secret information value corresponding to plaintext data input by the target program from the branch prediction information matrix; determining a correlation coefficient between the branch prediction information of the preset secret information value and the time sample data; and determining the actual secret information value corresponding to the plaintext data according to the correlation coefficient to serve as the secret information corresponding to the time sample data. The specific way of determining the actual secret information value corresponding to the plaintext data may be: selecting a secret information value with the highest correlation coefficient corresponding to the plaintext data as an actual secret information value, and judging whether the actual secret information value meets a preset condition (for example, whether the credibility of the actual secret information value is within a preset credibility range); if yes, outputting the actual secret information value; and if not, reselecting the plaintext data of the input target program.
According to the attack method and system based on the branch prediction mechanism, provided by the embodiment of the invention, the plaintext data input into the target program is selected, the time sample data of the operation of the target program is acquired based on the selected plaintext data, the secret information is determined according to the constructed branch prediction information matrix, the whole time data of the operation of the target program is utilized in the attack process, the requirement on the acquisition time point position is low, the leaked information (secret information) can be integrally depicted, the control requirement on the target program is reduced, the attack is carried out in a statistical analysis mode, the depiction of the leakage is more accurate, and the accuracy of time attack detection is improved.
In addition, the invention is not limited to a specific processor or a specific branch prediction mechanism, can be applied to different processor scenes, and has strong universality. Moreover, the method can effectively attack the sensitive data of various target programs under the condition of meeting the attack scene, has low requirement on the capability of an attacker, and has strong system expandability.
Embodiments of the present invention also provide a machine-readable storage medium, on which computer program instructions are stored, and when executed, the computer program instructions implement the above-mentioned attack method based on the branch prediction mechanism.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, systems and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (5)
1. An attack method based on a branch prediction mechanism, the method comprising:
constructing a branch prediction information matrix aiming at the target program based on a branch prediction mechanism;
selecting plaintext data which accords with the input data rule of the target program;
taking the selected plaintext data as input data or partial input data of the target program, and collecting time sample data of the operation of the target program;
determining secret information corresponding to the time sample data according to the branch prediction information matrix;
the branch prediction information matrix comprises a plurality of preset plaintext data and preset secret information values corresponding to the preset plaintext data;
the method for constructing the branch prediction information matrix aiming at the target program based on the branch prediction mechanism comprises the following steps:
setting an initial state of a branch prediction unit;
configuring the preset plaintext data and preset secret information values corresponding to the preset plaintext data based on the branch prediction component;
determining branch prediction information corresponding to the preset secret information value according to the branch prediction mechanism;
constructing the branch prediction information matrix according to the branch prediction information corresponding to the preset plaintext data and the preset secret information value;
the determining secret information corresponding to the time sample data according to the branch prediction information matrix includes:
acquiring branch prediction information of a preset secret information value corresponding to plaintext data input by the target program from the branch prediction information matrix;
determining a correlation coefficient between the branch prediction information of the preset secret information value and the time sample data;
and determining the actual secret information value corresponding to the plaintext data according to the correlation coefficient to serve as the secret information corresponding to the time sample data.
2. The method according to claim 1, wherein said determining secret information corresponding to the time sample data according to the branch prediction information matrix further comprises:
selecting a secret information value with the highest correlation coefficient corresponding to the plaintext data as an actual secret information value, and judging whether the actual secret information value meets a preset condition;
if yes, outputting the actual secret information value;
and if not, reselecting the plaintext data input into the target program.
3. An attack system based on a branch prediction mechanism, the system comprising:
the data acquisition module is used for selecting plaintext data which accords with an input data rule of a target program, taking the selected plaintext data as input data or partial input data of the target program, and acquiring time sample data of the operation of the target program;
the data analysis module is used for constructing a branch prediction information matrix aiming at the target program based on a branch prediction mechanism and determining secret information corresponding to the time sample data according to the branch prediction information matrix;
the data analysis module includes:
a branch prediction information prefetch unit to:
setting an initial state of a branch prediction unit;
configuring preset plaintext data and preset secret information values corresponding to the preset plaintext data;
determining branch prediction information corresponding to the preset secret information value according to the branch prediction mechanism;
constructing the branch prediction information matrix according to the preset plaintext data and the branch prediction information corresponding to the preset secret information value;
the time attack unit is used for determining secret information corresponding to the time sample data according to the branch prediction information matrix;
the time attack unit is used for:
acquiring branch prediction information of a preset secret information value corresponding to plaintext data input by the target program from the branch prediction information matrix;
determining a correlation coefficient between the branch prediction information of the preset secret information value and the time sample data;
and determining the actual secret information value corresponding to the plaintext data according to the correlation coefficient to serve as the secret information corresponding to the time sample data.
4. The branch prediction mechanism based attack system according to claim 3, wherein the data collection module comprises:
the plaintext data selection unit is used for selecting the plaintext data which accords with the input data rule of the target program;
a program running unit for running the target program by using the selected plaintext data as input data or partial input data;
and the time acquisition unit is used for acquiring time sample data of the operation of the target program.
5. A storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed, implement the method of attack based on a branch prediction mechanism of claim 1 or 2.
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| CN110347432A (en) * | 2019-06-17 | 2019-10-18 | 海光信息技术有限公司 | Processor, branch predictor and its data processing method, branch prediction method |
| CN111638913A (en) * | 2019-09-19 | 2020-09-08 | 中国科学院信息工程研究所 | Processor chip branch predictor security enhancement method based on randomized index and electronic device |
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| CN110347432A (en) * | 2019-06-17 | 2019-10-18 | 海光信息技术有限公司 | Processor, branch predictor and its data processing method, branch prediction method |
| CN111638913A (en) * | 2019-09-19 | 2020-09-08 | 中国科学院信息工程研究所 | Processor chip branch predictor security enhancement method based on randomized index and electronic device |
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