CN114611106A - Program control flow proving method based on multi-target particle swarm algorithm - Google Patents
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Abstract
The invention relates to a program control flow proving method based on a multi-target particle swarm algorithm, and belongs to the technical field of computer information remote proving. The optimal pile inserting scheme is obtained through a multi-target particle swarm algorithm, all possible control flow data of a target program are obtained according to the scheme, and the hash value and the input range of the control flow data in each input range are stored in a server. After receiving a request initiated by a server, the device runs a target program according to the input in the request, performs hash operation on dynamic control flow data in a trusted execution environment to obtain a hash value, performs signature on the hash value and the request to generate a report, and sends the report to the server. The server receives the report and signs a priori, checks whether the request is correct, and finally compares the hash value with an expected hash value. The invention can ensure that the equipment with limited resources can obtain extremely high safety with lower performance expense.
Description
Technical Field
The invention relates to a program control flow proving method based on a multi-target particle swarm algorithm, and belongs to the technical field of computer information remote proving.
Background
With the continuous development of the internet of things technology, embedded devices are widely applied to important fields such as families, offices, automobiles, city management, medical treatment, electric power and industrial control, once the embedded devices in the fields are attacked by attackers, not only can the privacy and the safety of individuals be threatened, but also the information safety of enterprises can be threatened, even the key infrastructure of the country can be threatened, and therefore the national safety is threatened. Attackers attack the software on embedded devices by various means, the most common and most harmful of which is the control flow hijacking attack.
The control flow hijacking attack is a common attack method aiming at computer software, and generally changes the control flow of a process through binary bugs such as buffer overflow bugs and the like, so as to execute specific malicious codes and achieve the purpose of attack.
Remote attestation techniques allow a rich-resource verifier (the entity performing the authentication) to obtain the operating state of software on a resource-constrained prover (the device requiring the attestation) to determine whether the prover is under attack. Remote attestation is a method for effectively guaranteeing the security of equipment and can reduce the attestation overhead of resource embedded equipment.
The current schemes for resisting control flow hijacking attack mainly adopt a fine-grained control flow certification scheme and pay great attention to the security of an authentication scheme. But for some resource-constrained devices, none of these schemes can be implemented on them. While coarse-grained control flow attestation schemes, while capable of being deployed on resource-constrained devices, have limited security.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a program control flow certification method based on a multi-target particle swarm algorithm, which is used for solving the problem that the safety and the performance expenditure of control flow certification can not be balanced, and the safety and the performance expenditure are optimized through the multi-target particle swarm algorithm, so that the equipment with limited resources can obtain extremely high safety with lower performance expenditure.
The technical scheme of the invention is as follows: a program control flow proving method based on a multi-target particle swarm algorithm obtains an optimal instrumentation scheme through the multi-target particle swarm algorithm, obtains all possible control flow data of a target program according to the scheme, and stores hash values and input ranges of the control flow data in each input range to a server. After receiving a request initiated by a server, the device runs a target program according to the input in the request, performs hash operation on dynamic control flow data in a trusted execution environment to obtain a hash value, performs signature on the hash value and the request to generate a report, and sends the report to the server. The server receives the report and signs a priori, checks whether the request is correct, and finally compares the hash value with an expected hash value.
The method comprises the following specific steps:
step 1: analyzing the function of the target program, acquiring the calling times of each function and the total number of control flow events of the target program, inputting the calling times and the control flow events into a multi-target particle swarm algorithm, and optimizing safety and performance overhead to obtain an optimal function instrumentation scheme;
furthermore, each function call frequency and the total number of control flow events of the target program are average values of a plurality of running paths, and the control flow events are jump instructions in program assembly codes.
Further, the formalization description of the problem optimized by the multi-objective particle swarm optimization is as follows:
optimizing the target: max { safety (Y) } min { spinning (Y) }
Constraint conditions are as follows: safety (Y) > Safetythreshold
spending(Y)＜Spendingthreshold
Control variables: y ═ Y1,y2,y3,...,yn}
The meaning of the parameters is as follows: safety (Y) refers to security under the function instrumentation scheme Y;
blending (Y) refers to the performance overhead under the function instrumentation scheme Y;
Safetythresholdsafety when all function pile inserting schemes are edge functions is indicated;
Spendingthresholdperformance overhead when all function instrumentation schemes are core functions is referred to;
step 2: performing coarse-fine granularity combined instrumentation on a target program according to an optimal function instrumentation scheme, analyzing the target program, determining an input range of a function, operating the processed target program on equipment according to the determined input range to obtain all control flow data, performing cumulative hash operation on the control flow data to obtain an expected hash value, and storing the expected hash value and the input range into a database of a server in a key value pair mode;
furthermore, the control flow data is composed of a plurality of jump nodes, each jump node is unique, if the same jump node appears for a plurality of times in the operation process, the control flow data obtained by pile insertion can be recorded only once, the subsequent reappearance can be recorded in the execution times of the instruction in the jump node data, the data of the jump node comprises the source address of the jump instruction, the jump destination address and the execution times of the instruction, wherein the jump instruction comprises a direct jump instruction, an indirect jump instruction, a conditional jump instruction, a function call instruction and a function return instruction.
Step 3: the method comprises the steps that a server initiates an authentication request to equipment, after the equipment receives the authentication request, the equipment inputs a target program after operation processing according to a program in the authentication request to obtain dynamic program control flow data, inputs the dynamic program control flow data into a trusted execution environment to carry out cumulative hash operation to obtain a hash value, signs the hash value and the authentication request to generate an authentication report, and finally sends the report to the server;
further, the hash algorithm uses SM3 cipher hash algorithm in China commercial cipher algorithm standard, and the recurrence formula of cumulative hash operation is
Wherein H represents a hash operation, NnRepresenting the nth hop node.
Step 4: after receiving the authentication report, the server firstly checks the report, determines that the submitter of the report is legal, then checks whether the authentication request in the report is legal, and finally compares the hash value with an expected hash value input in the authentication request to obtain an authentication result.
Further, the authentication request includes a random number, a target program identifier and an input value of the target program.
Further, the signature algorithm uses a digital signature algorithm in an SM2 elliptic curve public key cryptographic algorithm in the China commercial cryptographic algorithm standard.
The invention has the beneficial effects that: the problem that the safety and performance expenditure proved by control flow cannot be balanced is solved, the safety and performance expenditure is optimized through a multi-objective particle swarm algorithm, and the equipment with limited resources can obtain extremely high safety with low performance expenditure; the safety and performance overhead of the control flow proving scheme are optimized by using a multi-objective particle swarm algorithm, so that the control flow proving scheme combining the thickness and the granularity can be applied to equipment with limited resources.
Drawings
FIG. 1 is an overall architecture diagram of the present invention;
FIG. 2 is an architecture diagram of a device obtaining dynamic control flow information for attestation;
fig. 3 is a remote attestation flow diagram.
Detailed Description
The invention is further described with reference to the following drawings and detailed description.
Example 1: as shown in fig. 1, a program control flow proving method based on a multi-target particle swarm algorithm specifically comprises the following steps:
step 1: analyzing the function of the target program to obtain the calling times of each function and the total number of control flow events of the target program, inputting the calling times and the control flow events into a multi-target particle swarm algorithm, and optimizing the maximized security and the minimized performance cost to obtain an optimal function instrumentation scheme;
step 2: performing coarse-fine granularity combined instrumentation on a target program according to an optimal function instrumentation scheme to obtain a processed target program, analyzing the target program, determining an input range of a function, operating the processed target program on equipment according to the determined input range to obtain all control flow data, performing cumulative hash operation on the control flow data to obtain an expected hash value, and storing the expected hash value and the input range into a database of a server in a key value pair mode;
step 3: the method comprises the steps that a server initiates an authentication request to equipment, after the equipment receives the authentication request, the equipment inputs a target program after operation processing according to a program in the authentication request to obtain dynamic program control flow data, inputs the dynamic program control flow data into a trusted execution environment to carry out cumulative hash operation to obtain a hash value, signs the hash value and the authentication request to generate an authentication report, and finally sends the report to the server;
step 4: after receiving the authentication report, the server firstly checks the report, determines that the submitter of the report is legal, then checks whether the authentication request in the report is legal, and finally compares the hash value with an expected hash value input in the authentication request to obtain an authentication result.
Example 2: as shown in fig. 2, a program control flow proving method based on a multi-target particle swarm algorithm includes the following specific steps of obtaining dynamic control flow information by the device for proving:
first, a function requiring coarse-grained instrumentation is referred to as an edge function, and a function requiring fine-grained instrumentation is referred to as a core function. After the device receives the authentication request from the server, the running target program is input according to the program designated by the server. When the function call and the function return of the edge function are executed, the operation tracker jumps to the coarse-grained interceptor; the runtime tracker jumps to the fine-grain interceptor when function calls and function returns to the core function and internal control flow branch instructions are executed. When the program execution is finished, the interceptor sends the intercepted dynamic control flow data to a hash module in the trusted execution environment for cumulative hash operation to obtain a final hash value. And the signature module in the trusted execution environment signs the hash value and the authentication request by using a private key of the equipment, generates an authentication report and sends the authentication report to the server.
Example 3: as shown in fig. 3, a program control flow certification method based on a multi-objective particle swarm algorithm includes the following specific steps:
step 1: the server packages the program id to be authenticated, the random number N and the input i of the program into a request c, and sends the request c to the equipment as an authentication request. The random number is to prevent replay attacks.
Step 2: after the device receives the authentication request of the server, it executes the program a using the input i, and sends the intercepted program control flow information CF to the hash module located in the trusted execution environment through the runtime tracker. The Hash module carries out Hash on the obtained control flow information CF in an accumulative Hash operation mode to obtain a final Hash value h, and then the Hash value h is sent to the signature module. The hash value h and the received request c are signed using the private key sk of the device, generating an authentication report r.
Step 3: the device sends an authentication report r to the server.
Step 4: and after receiving the authentication report, the server checks the signature of the authentication report r by using the public key pk of the equipment. If the verification is correct, whether the received request c is a request c' sent to the device by the server in the earlier stage is checked, if not, the request c sent to the device is modified, if so, whether the received hash value h is consistent with an expected hash value input under i in the database is continuously checked, if so, the program A is not attacked, and if not, the program A is attacked by control flow hijacking.
While the present invention has been described in detail with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, and various changes can be made without departing from the spirit and scope of the present invention.
Claims (7)
1. A program control flow proving method based on a multi-target particle swarm algorithm is characterized in that:
step 1: analyzing the function of the target program, acquiring the calling times of each function and the total number of control flow events of the target program, inputting the calling times and the control flow events into a multi-target particle swarm algorithm, and optimizing safety and performance overhead to obtain an optimal function instrumentation scheme;
step 2: performing coarse-fine granularity combined instrumentation on a target program according to an optimal function instrumentation scheme, analyzing the target program, determining an input range of a function, operating the processed target program on equipment according to the determined input range to obtain all control flow data, performing cumulative hash operation on the control flow data to obtain an expected hash value, and storing the expected hash value and the input range into a database of a server in a key value pair mode;
step 3: the method comprises the steps that a server initiates an authentication request to equipment, after the equipment receives the authentication request, the equipment inputs a target program after operation processing according to a program in the authentication request to obtain dynamic program control flow data, inputs the dynamic program control flow data into a trusted execution environment to carry out cumulative hash operation to obtain a hash value, signs the hash value and the authentication request to generate an authentication report, and finally sends the report to the server;
step 4: after receiving the authentication report, the server firstly checks the report, determines that the submitter of the report is legal, then checks whether the authentication request in the report is legal, and finally compares the hash value with an expected hash value input in the authentication request to obtain an authentication result.
2. The program control flow certification method based on the multi-target particle swarm algorithm according to claim 1, characterized in that: the number of function calls of the target program and the total number of control flow events are average values of a plurality of running paths, and the control flow events are jump instructions in program assembly codes.
3. The program control flow proving method based on the multi-target particle swarm algorithm as claimed in claim 1, wherein the multi-target particle swarm algorithm is specifically:
optimizing the target: max { safety (Y) } min { spinning (Y) }
Constraint conditions are as follows: safety (Y) > Safetythreshold
spending(Y)＜Spendingthreshold
Control variables: y ═ Y1,y2,y3,...,yn}
The meaning of the parameters is as follows: safety (Y) refers to security under the function instrumentation scheme Y;
blending (Y) refers to the performance overhead under the function instrumentation scheme Y;
Safetythresholdsafety when all function pile inserting schemes are edge functions is indicated;
Spendingthresholdperformance overhead when all function instrumentation schemes are core functions is referred to;
4. the program control flow certification method based on the multi-target particle swarm algorithm according to claim 1, characterized in that: the control flow data is composed of a plurality of jump nodes, each jump node is unique, if the same jump node appears for a plurality of times in the operation process, the control flow data obtained by pile insertion can be recorded only once, the control flow data can be recorded in the execution times of the instruction in the jump node data after the same jump node appears again, the data of the jump node comprises the source address of the jump instruction, the jump destination address and the execution times of the instruction, and the jump instruction comprises a direct jump instruction, an indirect jump instruction, a conditional jump instruction, a function call instruction and a function return instruction.
5. The program control flow certification method based on the multi-target particle swarm algorithm according to claim 1, characterized in that: the hash algorithm uses SM3 cipher hash algorithm in China commercial cipher algorithm standard, and the recurrence formula of cumulative hash operation is
Wherein H represents a hash operation, NnRepresenting the nth hop node.
6. The program control flow certification method based on the multi-target particle swarm algorithm according to claim 1, characterized in that: the authentication request comprises a random number, a target program identification and an input value of the target program.
7. The program control flow certification method based on the multi-target particle swarm algorithm according to claim 1, characterized in that: the signature algorithm uses a digital signature algorithm in an SM2 elliptic curve public key cryptographic algorithm in the China commercial cryptographic algorithm standard.
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