## 2 Related Works  
To analyze and classify the recent research progress that offers promising solutions for the integration of AI models and blockchain technologies based smart contracts, this section critically evaluates the recent research papers that propose the integration of AI and blockchain technology for security enhancement in smart systems. As discussed in [32], the authors talk about using AI and blockchain for enhancing cybersecurity. It deals with issues such as intrusion detection, malware monitoring, and data privacy. The outcome offers improved intrusion prevention and network security with emphasis on policy structures. A recent study [33] automates AI, blockchain, and smart contract-based cybersecurity compliance and threat response. It resolves human errors and the lack of real-time monitoring. The results include real-time monitoring, improved audibility, reduced human intervention, and 91% accuracy in threat classification. A method was proposed in [34] related to the use of blockchain and AI for improved cybersecurity in developing healthcare systems. It gives priority to data breaches and unauthorized access. The results depict secure data storage, proactive threat detection, and improved patient data integrity, which cumulatively improve the security of healthcare systems. The findings reported in [35] focus on IoT systems using blockchain and AI for improving security. The study proposes SecureChainAI as a model that enhances detection rates and reduces error percentages, achieving higher detection scores (precision, recall, and F1-score). The \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Ref.** & **Attack** & **Vulnerability** & **Target** & **Method Used** \\ \hline
[22] &
\begin{tabular}{l} Ransomware \\ SC \\ \end{tabular} & via & SC Automation & Data & Criminal SCs \\
[23] & \
\begin{tabular}{l} Cyberattacks \\ \end{tabular} &
\begin{tabular}{l} Insufficient Security \\ \end{tabular} & Smart Home & SC \\
[24] &
\begin{tabular}{l} Transaction \\ attacks \\ \end{tabular} & SC & Transactions & Collaborative learning \\
[25] &
\begin{tabular}{l} Social \\ engineering \\ attacks \\ \end{tabular} & Unsecure code & SC & Test bed \\
[26] & \begin{tabular}{l} Reentrancy \\ attacks \\ \end{tabular} & \
\begin{tabular}{l} Reentrancy \\ vulnerabilities \\ \end{tabular} &
\begin{tabular}{l} Ethereum SC \\ \end{tabular} & Detection \\
[27] &
\begin{tabular}{l} Criminal SCs \\ \end{tabular} & SC Exploitation & SC & Criminal SCs \\
[28] & \begin{tabular}{l} APT \\ \end{tabular} & \
\begin{tabular}{l} Open system \\ \end{tabular} &
\begin{tabular}{l} Blockchain \\ \end{tabular} & Data theft \\
[29] & \
\begin{tabular}{l} SC for energy \\ \end{tabular} &
\begin{tabular}{l} Attack vectors \\ \end{tabular} & SC & Best practices \\
[30] & \
\begin{tabular}{l} DDoS \\ \end{tabular} &
\begin{tabular}{l} Open system \\ \end{tabular} & Resources & DDoS \\
[31] & Exploitation & Security patches & SC & Classification & and analysis \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of the main cyberattacks on smart contracts (SCs). approach presented in [36] improves security in voting systems based on blockchain and AI. It addresses weaknesses and cyberattacks in voting systems. Benefits include better threat detection and transaction volumes, thereby ensuring a secure and more efficient election process. The study in [37] enhances the security and privacy of AI systems through its use of blockchain. It prevents cyberattacks and data governance issues. Decentralized and tamper-proof storage, auditors independent of any authority, and verifications were achieved by the study, thereby ensuring the trust and enhanced security of AI systems. The results discussed in [38] focus on enhancing intrusion detection systems in IoT networks through blockchain and AI. The findings of this research are directed toward a scalable, transparent, immutable, and decentralized system, leading to improved security in IOT systems.