# Hybrid Quantum-Classical Deep Neural Networks Based Smart Contract Vulnerability Detection
## 2 Literature Review
The framework uses predefined test oracles to identify possible security issues and integrates an instrumented EVM to track and record runtime behaviors during execution. These recorded logs are subsequently analyzed to identify and report security vulnerabilities [34]. ILF leverages imitation learning to develop a fuzzing policy modeled as an ensemble of neural networks. This policy is trained using a dataset of high-quality transaction sequences generated through symbolic execution. By utilizing this approach, ILF is capable of generating effective test cases that maximize code coverage and enhance the detection of vulnerabilities [35]. In addition, fuzzing tools such as EtherFuzz [36], which uses a mutation fuzzy test method, and sFuzz [37], which is based on AFL and adopts a feedback adaptive fuzzification strategy, have been developed. Formal verification is the process of using mathematical methods to prove or disprove the correctness of a system with respect to a certain formal specification. Unlike testing, which examines software behavior under specific inputs, formal verification ensures correctness across all possible inputs and states. Because of this characteristic, it is well aligned with the stringent reliability standards required by on-chain agreements [38]. A variety of solutions and frameworks now support the formal validation of on-chain contracts. For instance, KEVM provides a detailed representation of the EVM, enabling comprehensive analysis of EVM bytecode [39]. FSolidM is a solution that conceptualizes contracts using Finite-State Machines [40]. Isabelle/HOL: A general-purpose theorem prover that can model smart contract behaviors [41]. Program analysis and taint analysis have become fundamental techniques for detecting vulnerabilities in smart contracts, particularly in blockchain ecosystems. These approaches facilitate the systematic examination of code behavior and the tracking of data flows to identify potential security risks, such as unauthorized data access or unintended state changes [3; 10]. Slither integrates techniques like data flow analysis, taint tracking, and custom rule detection to identify issues such as reentrancy, uninitialized variables, and inefficient gas usage [32]. SmartCheck translates the contract's code into an intermediate representation to apply pattern-matching rules, identifying issues like reentrancy, integer overflows, and insecure function visibility. SmartCheck provides detailed reports with categorized findings for ease of remediation [42]. Researchers have investigated ML techniques for vulnerability detection to address the shortcomings of traditional methods. ML algorithms can analyze large datasets to learn patterns associated with insecure code, enhancing detection capabilities more efficiently than manual methods. Recent studies have introduced novel approaches to smart contract vulnerability detection, leveraging advancements in neural networks and graph-based models. [PERSON] et al. [43] proposed a method combining pure neural networks with interpretable graph features and expert pattern fusion, providing a more robust mechanism for identifying security weaknesses in smart contracts. [PERSON] et al. [44] introduced G-Scan, a graph neural network designed for line-level vulnerability identification. This model focuses on precise detection, making it a valuable tool for developers to pinpoint vulnerabilities within specific lines of code. [PERSON] [45] developed Vulnerability-Hunter, which employs an adaptive feature perception attention network to enhance vulnerability detection. By focusing on dynamic feature extraction and attention mechanisms, the model improves accuracy for complex vulnerability patterns. This approach highlights the increasing sophistication of ML techniques in addressing smart contract security. [PERSON] et al. [46] conducted an extensive review of methods for identifying vulnerabilities in smart contracts, highlighting developments in both conventional and ML-driven approaches.