# Hybrid Quantum-Classical Deep Neural Networks Based Smart Contract Vulnerability Detection
## 2 Literature Review  
In recent years, researchers have proposed diverse strategies to bolster the reliability and security of on-chain agreements. These strategies primarily involve symbolic execution, fuzz testing, formal verification, programmatic analysis, and ML-based approaches [3; 25; 26]. Symbolic execution facilitates precise and comprehensive program analysis by simulating program execution using symbolic inputs rather than concrete values. Oyente is among the first tools created for analyzing the security of Ethereum smart contracts. It leverages symbolic execution to simulate the behavior of contracts and identify vulnerabilities such as reentrancy, mishandled exceptions, and transaction-ordering issues. By modeling the EVM, Oyente explores different execution paths to uncover potential weaknesses. Symbolic inputs are formulated by aggregating constraints that must be satisfied for execution to traverse a specific program path, facilitating the analysis of smart contract behavior. A path is considered infeasible if its condition cannot be met; on the other hand, a path is called feasible if the condition can be met [3; 10; 27; 28]. Mythril is a sophisticated, open-source security analysis tool developed to identify vulnerabilities within the EVM bytecode. Leveraging advanced methodologies such as symbolic execution, Satisfiability Modulo Theories (SMT) solving, and taint analysis, Mythril is highly effective in pinpointing critical security issues in smart contracts, such as reentrancy vulnerabilities, integer underflows/overflows, and access control misconfigurations [29]. MAIAN focuses on identifying weaknesses that may lead to significant security threats. These threats are classified into three primary categories: suicidal (contracts that can be arbitrarily destroyed), prodigal (contracts that may unintentionally transfer assets), and greedy (contracts that indefinitely lock funds). MAIAN integrates symbolic execution with heuristic evaluation to precisely detect these vulnerabilities [30]. Osiris expands on symbolic execution by incorporating taint analysis, enabling it to detect arithmetic issues like integer overflow, underflow, and division by zero. This makes it particularly valuable for analyzing contracts with complex numerical operations [12]. Securify serves as a verification platform, leveraging static analysis to ensure conformity with established security guidelines. It leverages compliance and violation patterns to determine if smart contracts fulfill designated security criteria or demonstrate potentially exploitable behaviors. This structured approach makes Securify scalable and efficient for analyzing large contracts [31]. Slither functions as a specialized analysis tool for Solidity code, enabling rapid and comprehensive identification of numerous security flaws. It makes use of taint tracking and data flow analysis, among other methods, to detect vulnerabilities such as reentrancy, uninitialized variables, and inefficient gas usage. Its speed and flexibility make it suitable for use in continuous development workflows [32]. Fuzzing, also known as fuzzy testing, is a dynamic approach to software security analysis aimed at detecting vulnerabilities by subjecting a program to unexpected or randomly generated inputs. Unlike static analysis, which inspects code without execution, fuzzing actively executes the software under test, systematically injecting malformed or anomalous data to observe its response. This method is particularly effective for uncovering flaws such as buffer overflows, null pointer dereferences, memory leaks, and unhandled exceptions, which may not be apparent through conventional testing techniques [3; 33]. ContractFuzzer is a specialized fuzzing framework developed for identifying security vulnerabilities within Ethereum smart contracts. It operates by generating test inputs derived from the Application Binary Interface (ABI) specifications of the targeted contracts.