# AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection
## 3 AI-Driven Blockchain Solutions
### AI-Driven Smart Contract
#### 3.2.1 AI-Driven Smart Contract Security and Vulnerability Detections
[142] developed MSmart, a novel tool that transforms smart contract source code into an intermediate representation and detects vulnerabilities based on XPath rules. They extended SmartCheck's intermediate representation rules to identify a broader range of vulnerabilities and optimized existing rules to accommodate the increasing complexity of smart contracts. [PERSON] et al. [143] introduced DefectChecker, a symbolic execution-based method and tool designed to detect eight types of contract defects that may lead to undesirable behaviors on the Ethereum blockchain. DefectChecker identifies these defects directly from smart contract bytecode. [PERSON] et al. [144] developed CRPWarner (contract-related Rug Pull Risk Warner) to identify malicious functionalities within smart contracts and issue warnings about potential rug pull risks. [PERSON] et al. [145] proposed a Hybrid Attention Mechanism (HAM) model for detecting security vulnerabilities in smart contracts. Their approach extracts code snippets from source code, focusing on critical areas where vulnerabilities are likely to occur. [PERSON] et al. [146] introduced CBGRU, a hybrid deep learning model that strategically integrates various word embeddings (Word2 Vec and FastText) with multiple deep learning architectures (LSTM, GRU, BiLSTM, CNN, and BiGRU). The model extracts features using different deep learning methods and combines them to enhance smart contract vulnerability detection. [PERSON] et al. [147] proposed CodeNet, a code-oriented CNN architecture designed to detect vulnerable smart contracts while preserving semantic and contextual information. To further improve CodeNet's performance, they developed a data preprocessing module that transforms smart contracts into images while maintaining local structural integrity. [PERSON] et al. [148] explored the use of graph neural networks (GNNs) combined with expert knowledge for smart contract vulnerability detection. Their approach transforms the rich control and data flow semantics of source code into a contract graph and introduces a novel temporal message propagation network to extract graph features from a normalized graph. These extracted features are then integrated with expert-designed patterns to develop a comprehensive detection system. [PERSON] et al. [149] proposed a novel smart contract security testing method called Transaction Encapsulation. The core idea involves executing transactions locally on fully synchronized yet isolated Ethereum nodes, allowing for a preview of transaction sequence outcomes in the blockchain's current state. To ensure testing accuracy, their system, TxT, deterministically verifies whether a given transaction sequence follows the same execution path within the blockchain's current state. [PERSON] et al. [150] introduced TxMirror, an innovative data-driven framework that detects bytecode-level smart contract vulnerabilities by symmetrically simulating transactions. Beyond logical analysis, TxMirror customizes the Ethereum Virtual Machine (EVM) for vulnerability detection and stores stack data and logical dependencies in a bi-linked forest structure, enabling efficient indexing of logical relationships. This approach allows TxMirror to directly examine custom EVM stack data within transactions without requiring repeated transaction replays or EVM bytecode-level tracing. [PERSON] et al. [151] presented a multimodal feature fusion-based approach for smart contract vulnerability detection. Their method extracts three types of modal features from the smart contract lifecycle, leveraging both static and dynamic characteristics. By integrating Graph Convolutional Networks(GCN) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks, the system effectively detects various vulnerabilities in smart contracts. [PERSON] et al. [152] proposed a new method based on symbolic execution of the EVM operand stack, enabling precise jump address parsing within EVM bytecode and constructing an accurate Control Flow Graph (CFG) of compiled smart contracts.