# 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
However, if these mechanisms are misconfigured--such as assigning administrative rights to regular users or failing to enforce restrictions--the contract becomes vulnerable. Such flaws may allow unauthorized users to perform critical actions like modifying contract parameters or transferring funds, thereby compromising the integrity and security of the entire contract system. [PERSON] et al. [136] first introduced the Contract Dependency Graph (CDG) to analyze vulnerabilities in Ethereum smart contracts. The CDG characterizes inter-contract dependencies formed by DELEGATECALL-type internal transactions in Ethereum. They then proposed three general definitions of security violations related to the CDG to identify potential victim contracts affected by different types of vulnerable contracts. To validate these security violations, they constructed a CDG using 195,247 active smart contracts from the latest Ethereum blocks and successfully detected three representative known vulnerabilities. [PERSON] et al. [137] proposed CrossFuzz, a fuzzing-based approach for cross-contract vulnerability detection. Their method leverages data flow information to optimize mutation strategies, significantly improving the efficiency of fuzz testing in identifying cross-contract vulnerabilities. [PERSON] et al. [138] introduced an automated approach to learning the characteristics of Solidity-based smart contracts, which is particularly beneficial for clone detection, error detection, and contract verification. [PERSON] et al. [139] conducted a comprehensive survey on smart contract quality assurance, covering vulnerability identification, the evaluation of detection tools, and dataset development. They developed an annotated dataset as a benchmark, including ten common vulnerability types, serving as a critical resource for future research. Additionally, they conducted extensive experiments \begin{table}
\begin{tabular}{p{71.1 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Type** & **Definition** & **Causes** & **Typical Example** & **Potential Impact** \\ \hline Arithmetic Vulnerabilities & Errors in mathematical operations such as overflow and underflow. & Lack of boundary checks and absence of safe math libraries. & Overflow in ERC20 tokens. & Incorrect calculations, unauthorized fund transfers. \\ \hline Re-entrancy Attacks & Exploiting recursive calls to re-enter the contract before state updates. & State changes occur after external calls; absence of function locks. & The DAO attack. & Repeated fund withdrawals, total asset drain. \\ \hline Unchecked Calls & Failure to verify the return value of low-level function calls. & Ignoring success/failure checks for call(), delegatecall(), delegatecall(), etc. & Parity Wallet bug. & Logic errors, silent failures, potential loss of funds. \\ \hline Inconsistent Access Control & Improper or missing role-based restrictions for function access. & Misconfigured or absent access control mechanisms. & Governance function override. & Unauthorized users gain control of sensitive operations. \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Comparison of key categories of smart contract vulnerabilities. evaluating 14 vulnerability detection tools, providing comparative analyses that highlight their strengths and limitations. [PERSON] et al. [140] proposed OpenSCV, a novel open and hierarchical classification system for smart contract vulnerabilities. OpenSCV is designed to be community-driven, aligning with current industry practices while remaining adaptable to future modifications and advancements. [PERSON] et al. [141] introduced HomoDec, a timestamp vulnerability detection technique for smart contracts based on code homogeneity analysis. The core idea is to compare the tested smart contract's code against a database of known vulnerability patterns, assessing similarities to determine whether the tested contract exhibits timestamp-related vulnerabilities. [PERSON] et al.