CN114611106B - Program control flow proving method based on multi-target particle swarm algorithm - Google Patents
Program control flow proving method based on multi-target particle swarm algorithm Download PDFInfo
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- 230000001186 cumulative effect Effects 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 3
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Classifications
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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
- G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
- G06F21/55—Detecting local intrusion or implementing counter-measures
- G06F21/56—Computer malware detection or handling, e.g. anti-virus arrangements
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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
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/44—Program or device authentication
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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
- G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
- G06F21/52—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity ; Preventing unwanted data erasure; Buffer overflow
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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
- G06F21/60—Protecting data
- G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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. According to the method, an 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 hash values of the control flow data under each input range and the input range are stored in a server. After receiving a request initiated by a server, the device operates a target program according to input in the request, performs hash operation on dynamic control flow data in a trusted execution environment to obtain a hash value, signs the hash value and the request to generate a report, and sends the report to the server. And after receiving the report, the server signs the report a priori, checks whether the request is correct, and finally compares the hash value with the expected hash value. The invention can enable the resource-limited equipment to obtain extremely high security with lower performance cost.
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, the embedded device is widely applied to important fields such as families, offices, automobiles, urban management, medical treatment, electric power, industrial control and the like, and once the embedded device in the fields is attacked by an attacker, the embedded device not only threatens personal privacy and safety, but also threatens enterprise information safety, even threatens national key infrastructure, thereby threatening national safety. An attacker attacks software on an embedded device by various means, wherein the most common and most harmful is the control flow hijacking attack.
The control flow hijacking attack is a common attack method aiming at computer software, and the control flow of a process is changed through binary vulnerabilities such as buffer overflow vulnerabilities, so that specific malicious codes are executed, and the purpose of attack is achieved.
Remote attestation techniques allow a resource-rich verifier (entity performing authentication) to obtain the running state of software on a resource-constrained attester (device requiring attestation) to determine if the attester is under attack. Remote attestation is a method for effectively guaranteeing the safety of equipment, and the attestation overhead of resource embedded equipment can be reduced.
The current scheme for resisting the control flow hijacking attack mainly comprises a fine-grained control flow proving scheme, and the security of an authentication scheme is very focused. 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 deployment 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 proving method based on a multi-target particle swarm algorithm, which is used for solving the problem that the safety and the performance cost of control flow proving are not balanced, and optimizing the safety and the performance cost through the multi-target particle swarm algorithm, so that the resource-limited equipment can obtain extremely high safety with lower performance cost.
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 pile inserting 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 under each input range to a server. After receiving a request initiated by a server, the device operates a target program according to input in the request, performs hash operation on dynamic control flow data in a trusted execution environment to obtain a hash value, signs the hash value and the request to generate a report, and sends the report to the server. And after receiving the report, the server signs the report a priori, checks whether the request is correct, and finally compares the hash value with the expected hash value.
The method comprises the following specific steps:
step1: analyzing the function of the target program, obtaining the call times of each function of the target program and the total number of control flow events, inputting the call times and the total number of control flow events into a multi-target particle swarm algorithm, and optimizing the safety and performance cost to obtain an optimal function instrumentation scheme;
further, the number of times of each function call 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.
Further, formalization of the problem of multi-objective particle swarm optimization is described as:
optimization target: max { security (Y) } min { fastening (Y) }, and
constraint conditions: safety (Y) > Safety threshold
spending(Y)＜Spending threshold
Control variable: y= { Y 1 ,y 2 ,y 3 ,...,y n }
Parameter meaning: safety (Y) refers to the safety under the function instrumentation scheme Y;
the mapping (Y) refers to the performance overhead under the function pile inserting scheme Y;
Safety threshold the finger function pile inserting scheme is the safety of the edge function;
Spending threshold the finger function instrumentation scheme is the performance overhead when the core function is adopted;
step2: performing pile inserting processing of combining coarse granularity and fine granularity on a target program according to an optimal function pile inserting 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 an input range key value pair into a database of a server;
further, 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 running process, the control flow data obtained by inserting piles is recorded only once, the follow-up reappearance is recorded only in the execution times of the instruction in the jump node data, the data of the jump node comprises the source address and the jump destination address of the jump instruction 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.
Step3: the server initiates an authentication request to the 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, the dynamic program control flow data is input into a trusted execution environment to perform cumulative hash operation to obtain a hash value, the hash value and the authentication request are signed to generate an authentication report, and finally the report is sent to the server;
further, the hash algorithm uses an SM3 password hash algorithm in China commercial password algorithm standard, and a recursion formula of accumulated hash operation is that
Wherein H represents a hash operation, N n Representing the nth hop node.
Step4: after receiving the authentication report, the server firstly carries out verification on the report, determines whether a submitter of the report is legal, then checks whether the authentication request in the report is legal, finally compares the hash value with an expected hash value input in the authentication request to obtain an authentication result, and if the authentication is successful, the target program on the equipment is not attacked, otherwise, the target program is attacked by the control flow hijacking.
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 China commercial cryptographic algorithm standard.
The beneficial effects of the invention are as follows: the problem that the safety and the performance cost of control flow proving cannot be balanced is solved, the safety and the performance cost are optimized through a multi-target particle swarm algorithm, and the resource-limited equipment can obtain extremely high safety with lower performance cost; the security and performance overhead of the control flow proving scheme are optimized by using a multi-target particle swarm algorithm, so that the control flow proving scheme with the combination of the coarse granularity and the fine granularity can be applied to equipment with limited resources.
Drawings
FIG. 1 is a diagram of the overall architecture of the present invention;
FIG. 2 is an architecture diagram of a device acquiring dynamic control flow information for attestation;
fig. 3 is a remote attestation flowchart.
Detailed Description
The invention will be further described with reference to the 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 comprises the following specific steps:
step1: analyzing the function of the target program, obtaining the call times of each function of the target program and the total number of control flow events, inputting the call times and the total number of control flow events into a multi-target particle swarm algorithm, and optimizing the maximized safety and minimized performance cost to obtain an optimal function instrumentation scheme;
step2: performing pile inserting processing of combining coarse granularity and fine granularity on a target program according to an optimal function pile inserting 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 an input range key value pair into a database of a server;
step3: the server initiates an authentication request to the 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, the dynamic program control flow data is input into a trusted execution environment to perform cumulative hash operation to obtain a hash value, the hash value and the authentication request are signed to generate an authentication report, and finally the report is sent to the server;
step4: after receiving the authentication report, the server firstly carries out verification on the report, determines whether a submitter of the report is legal, then checks whether the authentication request in the report is legal, finally compares the hash value with an expected hash value input in the authentication request to obtain an authentication result, and if the authentication is successful, the target program on the equipment is not attacked, otherwise, the target program is attacked by the control flow hijacking.
Example 2: as shown in fig. 2, a program control flow proving method based on a multi-target particle swarm algorithm, wherein the specific steps of the device for obtaining dynamic control flow information for proving are as follows:
the function requiring coarse-grained instrumentation is first referred to as the edge function, and the function requiring fine-grained instrumentation is referred to as the core function. After the device receives the authentication request of the server, the operation target program is input according to the program specified by the server. When executing the function call to the edge function and the function return, the runtime tracker jumps to the coarse-granularity interceptor; the runtime tracker jumps to the fine-grained interceptor when executing function calls and function returns to the core function and internal control flow branch instructions. When the program execution is finished, the interceptor sends the intercepted dynamic control stream data to a hash module in the trusted execution environment for cumulative hash operation to obtain a final hash value. The signature module in the trusted execution environment signs the hash value and the authentication request by using the 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 proving method based on a multi-target particle swarm algorithm, wherein the specific steps of the remote proving are as follows:
step1: the server packages the program id to be authenticated, the random number N and the input i of the program as a request c, and sends the request c to the device as an authentication request. Wherein the random number is to prevent replay attacks.
Step2: after the device receives the authentication request of the server, the device uses the input i to execute the program A, and the intercepted program control flow information CF is sent to a hash module in a trusted execution environment through the runtime tracker. The hash module hashes the obtained control flow information CF in a cumulative hash operation mode to obtain a final hash value h, and then sends the hash value h 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.
Step3: the device sends an authentication report r to the server.
Step4: after receiving the authentication report, the server uses the public key pk of the device to check the authentication report r. If the check is correct, checking whether the received request c is a request c' sent to the device by the server in the earlier stage, if not, indicating that the request c sent to the device is modified, if so, continuously checking whether the received hash value h is consistent with the expected hash value under the input i in the database, if so, indicating that the program A is not attacked, and if not, indicating that the program A is attacked by the control flow hijacking.
While the present invention has been described in detail with reference to the drawings, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.
Claims (4)
1. A program control flow proving method based on a multi-target particle swarm algorithm is characterized in that:
step1: analyzing the function of the target program, obtaining the call times of each function of the target program and the total number of control flow events, inputting the call times and the total number of control flow events into a multi-target particle swarm algorithm, and optimizing the safety and performance cost to obtain an optimal function instrumentation scheme;
step2: performing pile inserting processing of combining coarse granularity and fine granularity on a target program according to an optimal function pile inserting 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 an input range key value pair into a database of a server;
step3: the server initiates an authentication request to the 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, the dynamic program control flow data is input into a trusted execution environment to perform cumulative hash operation to obtain a hash value, the hash value and the authentication request are signed to generate an authentication report, and finally the report is sent to the server;
step4: after receiving the authentication report, the server firstly carries out verification on the report, determines that the submitter of the report is legal, then checks whether the authentication request in the report is legal or not, finally compares the hash value with the expected hash value input in the authentication request to obtain an authentication result, and if the authentication is successful, the target program on the equipment is not attacked, otherwise, the target program is attacked by the control flow hijacking;
the number of times of each function call of the target program and the total number of control flow events are all average values of a plurality of running paths, and the control flow events are jump instructions in program assembly codes;
the multi-target particle swarm algorithm specifically comprises the following steps:
optimization target: max { security (Y) } min { fastening (Y) }, and
constraint conditions: safety (Y)>Safety threshold
spending(Y)<Spending threshold
Control variable: y= { Y 1 ,y 2 ,y 3 ,...,y n }
Parameter meaning: safety (Y) refers to the Safety under the function instrumentation scheme Y;
the mapping (Y) refers to the performance overhead under the function pile inserting scheme Y;
Safety threshold the finger function pile inserting scheme is the safety of the edge function;
Spending threshold the finger function instrumentation scheme is the performance overhead when the core function is adopted;
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 is recorded once, the follow-up reappearance is recorded in the execution times of the jump instructions in the jump node data, the data of the jump nodes comprises the source address of the jump instructions, the jump destination address and the execution times of the jump instructions, wherein the jump instructions comprise direct jump instructions, indirect jump instructions, conditional jump instructions, function call instructions and function return instructions.
2. The method for proving program control flow based on multi-objective particle swarm algorithm according to claim 1, wherein: the hash algorithm uses SM3 password hash algorithm in China commercial password algorithm standard, and a recursion formula of accumulated hash operation is that
Wherein H represents a hash operation, N n Representing the nth hop node.
3. The method for proving program control flow based on multi-objective particle swarm algorithm according to claim 1, wherein: the authentication request comprises a random number, a target program identifier and an input value of the target program.
4. The method for proving program control flow based on multi-objective particle swarm algorithm according to claim 1, wherein: the signature algorithm uses a digital signature algorithm in an SM2 elliptic curve public key cryptographic algorithm in China commercial cryptographic algorithm standard.
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