Smart Contract Vulnerability Detection via Semantic and Interface Fusion
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Solution Overview
Problem
Existing deep learning-based vulnerability detection methods for smart contracts on the Ethereum blockchain primarily focus on opcode sequences, neglecting semantic features and function interface data, which limits their effectiveness in detecting vulnerabilities.
Innovation Solution
The method constructs a control flowchart, collects opcodes and operands in SSA form, crawls application binary interfaces, and uses sequence-to-sequence models to infer function parameters, combining semantic and function interface features to detect vulnerabilities through a fusion of hidden-layer features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based vulnerability detection methods focus only on opcode sequences, then the detection method is simple to implement, but the detection accuracy is limited due to neglecting semantic features and function interface data
Solution Approach 1:
The patent segments the smart contract analysis into multiple independent feature extraction modules: opcode sequence extraction, semantic feature extraction from operands, and function interface data extraction. Each module processes a specific aspect independently, then the features are fused together. This segmentation allows comprehensive feature analysis while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent transitions from one-dimensional opcode sequence analysis to multi-dimensional feature analysis by incorporating semantic features from operands and function interface data as additional dimensions. This dimensional expansion enriches the feature space, enabling more accurate vulnerability detection through comprehensive multi-perspective analysis.
2Measurement precision
If comprehensive multi-dimensional data including operands and function interfaces is used, then vulnerability classification accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent merges multiple feature sources (opcode sequences, semantic features from operands, function interface data) into a unified feature representation. The encoder-fuser-decoder architecture combines these diverse features through learned fusion mechanisms, achieving comprehensive vulnerability classification while managing complexity through integrated processing.
Solution Approach 2:
The patent introduces an encoder-fuser-decoder architecture as an intermediary mechanism that bridges the gap between raw multi-dimensional data and vulnerability classification results. This intermediary structure processes and integrates complex features systematically, reducing the burden of direct complex data processing while maintaining high classification accuracy.
3Reliability
If function parameters are extracted and analyzed, then the vulnerability detection comprehensiveness improves, but the computational resources required increase
Solution Approach 1:
The patent performs preliminary extraction and processing of function parameters and interface data before the main vulnerability detection process. By pre-processing this data and organizing it into structured representations, the system reduces the computational burden during the actual detection phase, achieving comprehensive analysis with optimized resource consumption.
Data Source
AI summary
Disclosed are a vulnerability detection method and device for a smart contract, and a storage medium. The method includes the following steps: constructing a control flowchart for the smart contract and collecting opcodes, operands, and opcodes in a static single assignment (SSA) form based on a call flow thereof; crawling application binary interfaces in a blockchain browser based on an address of the smart contract; using the opcodes and the operands as an input, and outputting function parameters; monitoring whether there are specified actions in functions to determine function attributes; and fusing the opcodes in the SSA form and the application binary interfaces or a concatenation form of the function parameters and the function attributes by an encoder, and obtaining existent vulnerability types by a decoder. With the technical solution of the present disclosure, semantic and function interface information in bytecodes is effectively used, and multi-label vulnerability detection is implemented.