388 results about "Bytecode" patented technology

The invention discloses a cross-chain smart contract vulnerability detection method and system based on multi-feature fusion learning. The method comprises the following steps: collecting a cross-chain smart contract vulnerability data set for cleaning and labeling; feature extraction is carried out from the source code and the byte code, an abstract syntax tree (AST) is extracted from the cleaned source code, a basic control flow graph (CFG) is extracted from the byte code, and a cross-chain control flow graph (xCFG) is constructed; carrying out feature representation on AST and xCFG, generating a graph vector through a graph neural network (GNN), generating a semantic vector through CodeBert, and fusing the semantic vector into a feature fusion vector; performing model training and detection, taking the generated vectors as training data and test data, obtaining a cross-chain smart contract vulnerability detection model by adopting Transform-FC model training data, and finally evaluating model performance through accuracy, recall rate, precision rate and F1 value. According to the method, the structural features and semantic features of the codes can be effectively fused, potential vulnerability information in the codes can be fully mined, the recognition capability of the model for cross-chain vulnerabilities can be enhanced, and the accuracy and reliability of the cross-chain vulnerability detection model can be improved, so that the security of a block chain system can be more efficiently guaranteed.

The invention relates to the field of risk data visual tracing, and provides a visual track tracing method and system for a risk checking task, and the method comprises the steps: analyzing a heterogeneous data stream, separating a source metadata set, calculating an original structure entropy fingerprint, intercepting an end feature, embedding a global tracking identifier, and generating a packaging data package; generating transformed service data by using dynamic byte code instrumentation, constructing a runtime execution context and a differential snapshot, and transmitting an instantiated track node object according to a shunt delivery strategy; mapping and instantiating the logical topology execution graph based on the Hash fragments, and marking a pollution state and generating a total element execution link graph when the logic topology execution graph is abnormal; the method comprises the steps of generating a topology summary data packet, rendering a real-time state view, responding to physical interaction operation to construct a retrieval request data packet, reconstructing a full-amount service load through topology backtracking and reverse data evolution, and generating an attribution view in combination with static codes and rule description. According to the method, a dynamic trajectory tracing and full-link risk visualization mechanism of massive heterogeneous data is constructed.

The invention provides a server-free dynamic management and control method based on an extended Burkley packet filter, and the method comprises the steps: mounting an eBPF program to a kernel cgroup subsystem, monitoring a function instance creation / destruction event in real time, extracting metadata, dynamically generating a fine-grained network strategy, compiling the fine-grained network strategy into an eBPF byte code, and injecting the eBPF byte code into a Map format strategy table, realizing data packet level allowing / discarding control by using a flow control hook; and when the instance is destroyed, the strategy table rule is automatically cleared through a cgroprease event. Through kernel-level dynamic management and control, precise security protection, zero-trust micro-isolation and kernel layer malicious behavior interception of the function instance in the full life cycle are realized, and the security efficiency and the resource utilization rate of the cloud native environment are remarkably improved.

The invention relates to the technical field of data acquisition, in particular to an AOP-based user behavior data acquisition method. The method comprises the following steps: firstly, identifying user behavior related code features by statically analyzing Java source codes of an application program, and constructing a feature template library; during a compiling period, according to a template library identification target method, embedding a point burying instruction by utilizing a byte code enhancement technology, and constructing a monitoring section framework; and during running, dynamically adjusting an execution strategy according to the running state of the application program by virtue of a dynamic rule engine. Meanwhile, an annular buffer area is adopted to asynchronously process user behavior data streams, cache data are managed in a sub-generation mode, and asynchronous processing is triggered when a threshold value is exceeded. The method also constructs an adaptive thread pool model based on a hierarchical thread pool architecture, distributes tasks according to data value weights, monitors the task backlog rate of a high-priority sub-pool, adjusts the sampling frequency or acquisition dimension when the task backlog rate exceeds a threshold value, forms adaptive closed-loop control, and effectively acquires and processes user behavior data.

Disclosed are systems, apparatuses, methods, and computer-readable media for heterogenous network services using platform agnostic extensions. A method includes: instantiating a first service having a first data plane control point; instantiating a second service configured to access the first control point in a data plane; receiving a first packet at the first service in a network path; providing at least one of the first packet and first metadata associated with the first packet to the second service to analyze or process the first packet and the first metadata in conjunction with the first service; processing at least one of the first packet or the first metadata in the second service based on external bytecode and generating at least second metadata based on the processing; receiving second metadata to the first service; and processing the first packet or a second packet.

The invention provides an intelligent solitary policy processing method based on a neural network and machine learning, and relates to the field of data processing. According to the method, strategy byte codes and execution contexts are generated by extracting business rules, supervision calibers and field dependency relationships of the single insurance policy, discovery, repair and arbitration of field abnormity are achieved in combination with a dynamic data governance model, an account-falling confirmation receipt is output through a voting game and a compensation transaction, and a replayable trace and a rare mode index are generated. And a high-confidence label and an error correction prior increment are obtained in combination with triggering verification, and finally, full-link evidence is solidified through continuous distillation, chain type evidence storage and an auditable snapshot library, so that a traceable record of the single insurance policy is formed. By implementing the technical scheme provided by the invention, full-link data processing can be conveniently carried out on the single insurance policy, so that the processing efficiency is effectively improved.

The invention provides a software monitoring method, system and device based on byte code replacement and risk awareness and a medium, and belongs to the technical field of software security. The method comprises the steps that a special annotation mark in a source code is scanned, a test code entry point is recognized, and metadata information is extracted; performing encryption processing on the metadata and generating a registry file with a digital signature; environment safety data are collected through four monitoring dimensions, and a comprehensive risk score is calculated; generating a grading fusing instruction according to the score; and finally executing the fusing operation of byte code replacement or thread sandbox isolation. According to the invention, accurate identification and real-time protection of the leakage risk of the test code are realized; through a multi-dimensional monitoring and hierarchical fusing mechanism, the system security is ensured, and the influence on the service performance is minimized; and byte code replacement and thread isolation technologies are adopted, so that security protection can be realized without restarting the application, and the availability and the operation and maintenance efficiency of the system are improved.

The invention discloses a link tracking monitoring method, system and device and a computer readable storage medium, and relates to the technical field of communication, the method tracks a link needing to be tracked through a preset link tracking model, unnecessary byte code injection, interception and excessive tracking data are prevented from being generated, and the link tracking monitoring efficiency is improved. The influence on a network, a CPU (Central Processing Unit), a memory and the like caused by transmission, analysis and storage of the tracking data is avoided, the relation among the invasion degree of the existing code, the link tracking granularity and the performance overhead is balanced, and the influence on the existing service performance and the concurrent processing capability is avoided; a byte code enhancement technology is adopted to enhance original service processing logic, enforcement coding and decoding byte codes are injected, execution calling information of a specified link (function / method) is intercepted, enhanced, recorded and sent, zero invasion and zero development are carried out on existing service codes, and tracking of the full-link calling process of an application layer data protocol HTTP / RPC and a transmission layer protocol UDP is achieved.

The invention discloses a real-time anti-fraud transaction intelligent analysis system and method based on multi-chain collaboration, and the system comprises a dynamic federated learning module which is used for dynamically loading and updating a global model during the operation of a JVM (Java Virtual Machine) by adopting a Java Agent bytecode enhancement technology; the multi-chain collaborative verification engine comprises an alliance chain and a private chain, the alliance chain is formed by taking a plurality of banks as organization nodes and is used for distributed evidence storage of transaction characteristics among financial institutions, and the private chain is maintained by a supervision mechanism and is used for auditing and compliance verification; and the real-time stream processing pipeline processes the transaction request in a stream processing mode to obtain feature data, and makes a decision based on the feature data and the global model output by the dynamic federation learning module. The intelligent risk control system which supports cross-mechanism collaboration, privacy protection, high throughput and low delay is constructed, and the problems that in the prior art, model updating is lagged, a verification mechanism is not transparent, and processing delay is high are solved.

The invention relates to the technical field of mobile application continuous integration automation, and discloses a multi-channel APP automatic packaging system which comprises the steps that byte code files in a released mobile terminal APP installation package are acquired, and an occupied symbol space set is generated; generating an available confusion namespace by using set complementary operation; extracting method-level code change by using an abstract syntax tree difference algorithm, and generating a change method set; generating a patch special confusion rule for the change method set by using a namespace allocation algorithm; compiling and obfuscating the change method set by applying a patch special obfuscating rule to generate a safe obfuscating patch byte code; performing packaging and signature operation on the safe obfuscation patch byte code, and outputting a minimized obfuscation safe hot repair patch pack; according to the method, the technical problems that class definition conflicts or method calling errors occur after the hot repair patch is loaded, and the downloading success rate of a user is affected due to the overlarge size of the hot repair patch are solved.

The invention provides a software supply chain risk component calling identification method, and belongs to the technical field of basic safety. The method comprises the following steps: loading components in a Java Web application, and dynamically capturing a loaded component list; storing the component list locally and sending the component list to an external intelligence system, acquiring risk component information, and generating a risk calling interface signature according to the risk component information; a preset instrumentation rule file is loaded based on the risk calling interface signature, and accurate monitoring of the risk interface is realized through dynamic method matching and byte code enhancement; screening risk interfaces triggered during operation according to cross comparison of static data and dynamic calling data, and marking high-risk components which must be repaired and risk components which can be postponed to be repaired; the risk report generation module generates repair suggestions and priority reports. According to the method, the accuracy of risk assessment is fundamentally improved, meanwhile, the unnecessary repair cost is remarkably reduced, and an efficient and innovative technical solution is provided for supply chain risk management.

The invention relates to the technical field of block chains, in particular to a smart contract vulnerability detection method and system based on a multi-modal large language model.The detection method comprises the steps that S1, the system receives source code input of a smart contract; s2, executing semantic branches and graph structure branches in parallel; executing semantic branch processing, calling an annotation agent, and generating an annotation from the source code of the smart contract; calling a vectorization agent, coding the annotation and the source code, and converting the annotation and the source code into a high-dimensional vector; executing graph structure branch processing, compiling a source code into a byte code by the system, and generating a control flow graph; the vectorization agent generates high-dimensional embedded representation of the nodes based on the control flow graph; and S3, integrating features extracted from the semantic branches and the graph structure branches through a multi-modal feature fusion strategy, and inputting the integrated features into a classifier to detect vulnerabilities. The method is based on a multi-modal large language model agent, and potential vulnerabilities in the smart contract are comprehensively analyzed by integrating high-level semantic information and low-level structured data.

The invention discloses an intelligent contract vulnerability detection method based on a multi-level data dependence heterogeneous graph, and belongs to the technical field of intelligent contract security. Intelligent contract byte codes are used as input, contract byte codes are converted into operation codes, and a control-data heterogeneous graph with basic blocks as nodes is constructed; designing an in-block data dependency sub-graph, and forming multi-level data dependency with the inter-block data stream; based on the generated control-data heterogeneous graph, a feature weighted relation graph convolutional network is provided, and control flow and data flow feature weights are dynamically adjusted and optimized by using node static attributes such as dominating tree depth and the like, so that multi-modal semantic efficient aggregation is realized. According to the method, for the contract without a public source code on the block chain, vulnerability detection only for the bytecode of the intelligent contract is realized, and high detection efficiency can be achieved under the condition that the detection accuracy is guaranteed.

Embodiments of the present application provide a method and device for compiling program code. The method for compiling program code may include: obtaining source code; and performing a compilation operation to obtain a first compilation result of the source code, where a first code segment in the source code is compiled into machine code in the first compilation result, a second code segment in the source code is compiled into bytecode in the first compilation result, and the first compilation result is configured to be deployed to a target environment. The proposed technical solutions may be conducive to ensuring performance at low storage resource utilization.

The invention belongs to the technical field of airport business and link tracking, and discloses a business-level link tracking method and system based on airport business. According to the method, a service-level multi-language adaptive probe based on a dynamic bytecode enhancement technology is utilized, semantic analysis is carried out on collected data through a multi-dimensional label identification engine, flight numbers and event coding service labels are extracted, association mapping of the service labels and technology calling links is established, a service link monitoring system is constructed in an airport multi-manufacturer environment, and the service link monitoring system is used for monitoring the airport multi-manufacturer environment. Service-level fault positioning is realized; performing cross-system link integration based on the service label association information, and constructing a service-driven full-link tracking view; and intelligent positioning and visual display of service faults are realized through multi-dimensional service topology analysis and a dynamic early warning mechanism. According to the invention, sampling can be carried out on demand, only key requests and long-time-consuming interfaces are recorded, and the system can be integrated with other monitoring systems so as to quickly respond and solve problems.

The invention relates to a byte code function retrieval method and device of an Ethereum virtual machine and computer equipment, and the method comprises the following steps: processing all byte codes in a byte code library of the Ethereum virtual machine to obtain all byte code functions; calculating a first similarity between the to-be-retrieved natural language query content and all byte code functions based on a pre-trained byte code retriever; inputting the natural language query content into a preset large model to generate a code language function corresponding to the intelligent contract function; calculating a second similarity between the code language function and all byte code functions based on a pre-trained specific retriever; and retrieving a target byte code function corresponding to the natural language query content according to the first similarity and the second similarity. Through the method and the device, the problem of low retrieval accuracy in related technologies is solved, the accurate target byte code function is retrieved on the basis of the first similarity and the second similarity, and then the retrieval recall rate and the retrieval accuracy are enhanced.

The invention discloses a contract vulnerability detection method and system based on cross-granularity feature fusion and meta-learning, and relates to the technical field of block chain security, the method comprises the following steps: obtaining a source code and a compiled byte code of a contract to be detected; source code semantic features are extracted from the source code, operation code execution features are extracted from the byte code, and graph structure features are extracted after a program dependency graph is constructed from the source code; performing cross-granularity attention interaction to generate cross-granularity fusion features; performing type-aware feature modulation on the cross-granularity fusion feature and a vulnerability type identifier of the current detection task, training a classifier by adopting a meta-learning strategy, and outputting a vulnerability detection result; and in response to the vulnerability detection result that the vulnerability exists, positioning the vulnerability code position based on the graph-source code attention weight matrix generated in the cross-granularity attention interaction process, thereby solving the problem of poor generalization ability in a rare vulnerability scene with scarce samples.

The invention discloses an AI service intelligent fault tolerance and degradation method, device and equipment based on a Java bytecode enhancement technology, a medium and a product, and relates to the technical field of AI service integration. According to the method, in a class loading period of Java application starting, a loaded class is scanned to identify target methods marked with declarative fault-tolerant annotations, a bytecode operation framework is utilized to enhance the target methods so as to implant a unified fault-tolerant logic interception entry, and then in a running period, when the target methods are called, the target methods are subjected to fault-tolerant annotation processing. And triggering a fault-tolerant interceptor through the interception entrance to analyze the annotation to obtain a fault-tolerant strategy, allocating an independent fault-tolerant component instance to the fault-tolerant interceptor based on the unique identifier of the method, constructing and executing an enhanced call chain containing a fusing check and retry mechanism, and finally, if the execution of the call chain fails, executing the fault-tolerant interceptor. And if yes, a preset degradation method in the fault-tolerant strategy is automatically executed, so that complete non-intrusive decoupling of AI service fault-tolerant management and service logic can be realized, and the reliability of the system is remarkably improved.

The invention provides a monitoring method and system for a high-frequency transaction scene based on an eBPF. The monitoring method comprises the steps that a pre-compiled eBPF byte code program is loaded into a Linux kernel; an eBPF probe is deployed at an event trigger point of each layer of the network protocol stack; establishing a data storage and processing mechanism in a kernel space by using an eBPF Map, acquiring real-time data through a probe, and performing preliminary analysis; further processing is carried out based on the analysis result, and if abnormity is detected, an alarm is given and information is recorded. The technical problem that microsecond-level continuous monitoring and accurate acquisition of abnormal events cannot be realized in high-frequency transactions is solved.

The invention belongs to the field of end-cloud collaborative computing and lightweight virtualization, and provides an end-cloud collaborative lightweight application distribution and isolated operation method and system, which adopts a WASM byte code as a unique format for application distribution, compiles an application into a WASM module at a cloud and then distributes the WASM module to an end side device; the end side equipment combines read-only configuration data of a host with a temporary file system Tmpfs of a memory by utilizing OverlayFS on a file level, and maps the data into a virtual file system in a WASM sandbox; on the symbol level, a dynamic WASI bridge is introduced, according to the requirement of an import section in a WASM module, the native capacity of a host system is injected into WASM operation in a host function mode in real time, and the heterogeneous hardware characteristics of end-side equipment are adapted. The method is suitable for an end-side environment with an immutable operating system as a base, and is compatible with a cloud containerization deployment scene.

The invention provides a kernel bypass-based non-sharing architecture key value cache acceleration method and system, and the method comprises the steps: splitting a complete key value cache processing logic into a plurality of eBPF subprograms, compiling the plurality of eBPF subprograms into bytecode files, loading the bytecode files to a kernel, and mounting the bytecode files to an XDP hook point of a specified network interface; eBPF mapping is created and initialized; the method comprises the following steps: receiving a data packet at a network card driving layer through an XDP hook point, after screening a target data packet conforming to a preset protocol type and a port, analyzing the target data packet to extract a request command type and a key, calculating a hash value of the key, and temporarily storing the request command type and the hash value in a head reserved field of the target data packet; redirecting the target data packet to the target CPU core in combination with eBPF mapping, the request command type and the hash value; and the target CPU core executes data operation on the target data packet to generate a response message, and sends or forwards the response message from the receiving network card to the target network card. According to the method, high-performance cache processing in the kernel is realized.

The invention discloses a Java JAR packet encryption protection method and device, equipment and a medium, and the Java JAR packet encryption protection method comprises the steps: splitting a JAR packet decryption key into two fragments, and statically storing the two fragments in a starter program and a dynamic link library in a nonlinear confusion structure, thereby avoiding the exposure of a key plaintext. During starting, the starter transmits the first secret key fragment to the dynamic link library through secure inter-process communication, and the first secret key fragment is combined with the second fragment in the memory to restore a complete secret key. And during class loading, the Java agent intercepts a request and transmits an encrypted class byte code to the dynamic link library through the JNI, the encrypted class byte code is decrypted by using the restored key, and a plaintext is returned for loading. According to the method, static decompilation and dynamic memory dump attacks are prevented, and the anti-reverse and anti-leakage capabilities of Java software in a privatized deployment environment are remarkably improved.

The invention discloses an FPGA (Field Programmable Gate Array) coprocessor for accelerated execution of an intelligent contract, which belongs to the field of hardware acceleration and comprises a controller module, an operation module, a memory module and a stack module, the controller module is used for receiving a byte code instruction of an external smart contract and generating a control signal to control the operation module to carry out corresponding operation and / or the stack module to carry out corresponding operation; the memory module is used for receiving and storing to-be-operated data output by the smart contract and outputting corresponding operation data to the stack module, and then the stack module inputs the data to the operation module; the operation module comprises a calculator module, a multiplier module, a divider module and a hash operation module; and the stack module is used for storing the data obtained by the calculation of the operation module, and can also execute the operations of in-stack, out-stack, copying and exchange according to the control of the control signal, and outputting the data stored in the stack module to the memory module. The method is suitable for block chains and other high-performance computing applications.

The invention relates to the technical field of full-link monitoring, and discloses a Skwalking-based micro-service system full-link monitoring method, which comprises the following steps of: 1, automatically collecting full-link tracking data without invasion, loading an Agent probe through a bytecode enhancement technology when a micro-service instance is started, and loading the Agent probe through a bytecode enhancement technology; and the Agent probe automatically intercepts service calling under the condition of not modifying a service code. According to the technical scheme, a Kafka cluster is introduced between a data acquisition end and a processing platform to serve as a buffer channel, and an L1 and L2 distributed OAP processing architecture capable of being horizontally expanded is designed, so that the technical effect of guaranteeing the integrity and real-time performance of mass monitoring data in a super-large-scale micro-service environment is achieved; compared with a transmission architecture in which a monitoring probe is directly connected with a central server in the prior art, the transmission architecture solves the problem that the accuracy of a monitoring result is damaged due to the fact that data congestion, delay and even packet loss are easy to occur during data peak flood due to the lack of an effective buffering and peak clipping mechanism.

The invention provides a Python bytecode obfuscation method and system based on dynamic screening and random replacement, and the method comprises the steps: calculating a plurality of safety indexes and a comprehensive safety index SDI of bytecode files, and screening out the bytecode files which have high safety requirements and need to be obfuscated; a linear congruence generator is constructed, a pseudo-random number sequence is provided for obfuscation operation of byte codes, and operation code dynamic replacement obfuscation operation is performed in byte code instructions by generating random seeds, constructing random dynamic operation code mapping tables and applying the independent operation code mapping tables to different byte code files needing to be obfuscated; and performing obfuscation state detection on the obfuscated bytecode file, if the detection result is the obfuscated bytecode file, modifying bytecode analysis and instruction scheduling logic, increasing a runtime de-obfuscation process, embedding an Opcode reflection mechanism, re-compiling to generate a customized Python interpreter supporting the execution of the obfuscated bytecode, and executing the obfuscated bytecode file. According to the invention, byte codes can be confused.

The invention provides a multi-source heterogeneous data unified processing method and system based on a dynamic script engine, and relates to the technical field of game engines and software development.The multi-source heterogeneous data unified processing method comprises the steps that multiple game engine protocol signals are converted into a unified intermediate representation format, and meanwhile asset specification parameters and engine compatibility identifiers are extracted and injected into the unified intermediate representation format; forming enhanced asset data; analyzing and verifying the dynamic script codes, converting byte codes to load a memory, and establishing a dynamic script hot update mechanism; concurrently loading enhanced asset data according to a mechanism state, recording an error when the enhanced asset data fails, triggering an exception, and switching a standby path for reloading; finally, the asset format is converted into a target engine native format, algorithm parameters are updated by means of a hot updating mechanism to optimize quality, it is ensured that the target engine requirements are met, asset data enhancement, dynamic script hot updating, concurrent loading fault tolerance and asset format conversion and quality optimization can be achieved, and it is ensured that assets meet the target game engine requirements.

The embodiment of the present specification provides a method for processing code, a method for processing exceptions and respective devices, wherein the method for processing code comprises: reading a bytecode file generated after a program source code is compiled; locating a code region in a target method contained in the bytecode file which needs to capture an exception; and performing an exception capture block adding process on the code region based on a bytecode operation mode to obtain a processed bytecode file, wherein the exception capture block adding process comprises: adding a start label of a monitored block at the beginning of the code region, adding an end label of the monitored block at the end of the code region, and adding a corresponding exception processing block for the monitored block, wherein the exception processing block is used for processing an exception captured by the monitored block.

The invention provides a code detection and protection method, system and device and a medium, and belongs to the technical field of malicious code detection.The method specifically comprises the steps that WASM byte codes are obtained; converting the byte code into a WAT text and an intermediate representation, and analyzing meta-information; respectively constructing control flow diagrams and program dependency diagrams of the JS and the WASM based on the AST and the intermediate representation, and fusing the control flow diagrams and the program dependency diagrams into a unified dependency diagram through cross-language interface nodes; matching the dependency graph by using a preset malicious rule base, and marking a suspicious level; if not, WASM is injected into a Unikernel mirror image to be executed in an isolation environment, and behavior fingerprints during operation are collected; and outputting a malicious and non-malicious conclusion by combining the static analysis result with dynamic feature rating such as resource consumption and abnormal events. Through cross-language dependency graph fusion and dynamic and static combination analysis, malicious behaviors in WASM and JS interaction are captured, execution and behavior collection are isolated, the misjudgment risk is reduced, the detection accuracy is improved, and efficient and accurate WebAssembly module protection is achieved.

The invention discloses a data calculation method and device, a storage medium and an electronic device, and relates to the field of data processing.The method comprises the steps that a data calculation request initiated by a client side is received, and the data calculation request at least comprises a service identifier and input parameters; byte codes and service definition information corresponding to the service identification are inquired from a database, the service definition information comprises an input parameter definition, a processing unit definition, execution logic of a processing unit and an output parameter definition, and the processing unit comprises an inquiry unit and a calculation unit; constructing a data processing sequence according to the byte code and the service definition information, the data processing sequence being used for determining an execution sequence of the processing unit; executing a processing task generated according to the data processing sequence, calling a query unit based on the input parameter to obtain to-be-calculated data from the data source, calling a calculation unit to calculate the to-be-calculated data, and obtaining a calculation result output according to the output parameter definition; and sending a calculation result to the client.

The invention relates to a dynamic SQL (Structured Query Language) detection method and system, a program product and a storage medium and device thereof. The method comprises the following steps: deploying a Jar packet for SQL detection to a target server; configuring a javaagent parameter in a starting parameter of a to-be-detected application service of the target server, wherein the javaagent parameter specifies a path of the Jar packet on the target server; when the to-be-detected application service is started, the Java virtual machine loads the Jar packet according to the Java proxy parameters, and byte code enhancement is carried out on an SQL executor so as to insert detection logic in the SQL execution stage; and when the to-be-detected application service processes a request, executing SQL detection through the detection logic inserted by the byte code enhanced code.