## 1 Introduction  
The rapid expansion of decentralized finance (DeFi) applications, especially on public blockchain platforms such as Ethereum, has significantly transformed the financial landscape [1]. Unlike conventional banking systems, DeFi utilizes the transparency and openness of decentralized networks, particularly blockchain technologies, to provide diverse financial services. In this context, a smart contract is a self-executing program deployed on the blockchain that governs the terms and logic of financial transactions without requiring intermediaries. These contracts underpin core functionalities such as token swaps, lending protocols, automated market making, and yield aggregation. Economically, smart contracts reduce reliance on centralized entities, lower transaction costs, and enable programmable financial instruments with deterministic execution. Yet, this automation and immutability also mean that any vulnerability embedded in the code can lead to irreversible losses once deployed. Moreover, the inherent openness of DeFi makes it highly susceptible to external attacks, posing serious risks to the security of participants' funds. According to [PERSON]'s [2] study employing the Fermatean fuzzy AHP approach to rank various risks in DeFi, technical risks are paramount, whereas financial risks are the most significant sub-risk, followed by transaction risks. Solidity, the primary language for developingsmart contracts, has certain design flaws that necessitate a strong focus on code security by developers. In addition, the immutable nature of blockchain technology means that deploying a vulnerable contract can result in irreversible financial losses. Potential dangers arising from security vulnerabilities in DeFi can be enormous, as the ramifications extend beyond financial losses, encompassing broader systemic threats including diminished user trust [3]. A notable example is the 2016 DAO attack [4], where hackers exploited a vulnerability in a smart contract, stealing approximately $60 million from Ether, leading to a 40% drop in its market value. Another significant incident occurred in 2017 when Parity [5], an Ethereum wallet provider, lost around $270 million due to a vulnerability that destroyed a multi-signature wallet contract. More recently, in October 2022, a hacker exploited a cross-chain bridge vulnerability on the BNB Chain, resulting in the theft of 2 million BNB tokens, valued at approximately $566 million [6]. As of 2024, SlowMist [7] reports that cumulative losses from blockchain hacks have surpassed $3.5 billion. These recurring security breaches have severely impeded the development of the DeFi ecosystem and adversely affected stakeholders' interests. Therefore, conducting thorough and precise security analyses of smart contracts prior to their deployment on Ethereum is crucial to minimize vulnerabilities, mitigate further security incidents, and safeguard users' assets and their trust in the DeFi ecosystem. Recent advancements in predictive modeling have shown the importance of integrating both spatial and temporal information to detect complex vulnerabilities in distributed systems. A notable example is the work by [PERSON] et al. [8], which presents a detection model for false data injection attacks in smart grids based on graph spatial features and Temporal Convolutional Networks (TCNs). By encoding the system structure as a graph and using TCNs to capture sequential patterns in the control data, their model effectively identifies sophisticated attack behaviors that evolve over time. Such spatio-temporal approaches have demonstrated strong performance in cyber-physical systems, and their underlying principles, modeling structured data over time, can be extended to smart contract analysis. While powerful, these models typically require labeled data and fixed architectures, which may limit adaptability in fast-evolving DeFi environments.