Arash Habibi LashkariSepideh HajiHosseinKhani2024-11-072024-11-072024-07-052024-11-07https://hdl.handle.net/10315/42433With the advent of blockchain networks, there has been a transition from traditional contracts to Smart Contracts (SCs), which are crucial for maintaining trust within these networks. Previous methods for analyzing SC vulnerabilities typically lack accuracy and effectiveness, struggling to detect complex vulnerabilities due to limited data availability. This study introduces a novel approach to detecting and profiling SC vulnerabilities, featuring two components: a new analyzer named BCCC-SCsVulLyzer and an advanced Genetic Algorithm (GA) profiling method. The BCCC-SCsVulLyzer extracts 240 features, while the enhanced GA employs techniques such as Penalty Fitness Function and Adaptive Mutation Rate to profile vulnerabilities. Additionally, this work introduces a new dataset, BCCC-SCsVul-2024, with 111,897 Solidity source code samples for practical validation. Three taxonomies are established to enhance the efficiency of profiling techniques. Our approach demonstrated superior precision and accuracy, proving efficient in time and space complexity. The profiling technique also makes the model highly transparent and explainable, highlighting the potential of GA-based profiling to improve SC vulnerability detection and enhance blockchain security.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Computer scienceComputer engineeringElectronic Thesis or Dissertation2024-11-07Smart Contracts (SCs)VulnerabilityVulnerable Smart ContractsVulnerability ProfilingGenetic Algorithm.