This podcast introduces a novel profiling technique for identifying vulnerabilities in smart contracts (SCs), specifically proposing an enhanced Genetic Algorithm (EGA). It details the architecture of SCsVulLyzer (V2.0), a tool designed for feature extraction and optimization in SC analysis. The paper further categorizes and analyzes existing vulnerability detection methods, contrasting non-learning-based approaches like rule-based systems and finite state machines with learning-based techniques such as machine learning and deep learning. Ultimately, the research demonstrates the superior performance of the EGA model in accurately detecting various SC vulnerabilities compared to traditional and neural network-based methods, while also visualising and defining common profiles for both vulnerable and secure SCs.

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