Full-Stack Hierarchical Fusion of Static Features for Smart Contracts Vulnerability Detection

The security of smart contracts has drawn attention in recent years due to their immutability and ability to hold assets. Existing machine learning and deep learning methods addressing vulnerabilities in smart contracts often partially combine pooled features from first the contract source code, second, the build based approach made of features extracted during source code compilation, and third, the bytecode approach relying on features obtained from the Ethereum virtual machine bytecode analysis. Together those three approaches form the full-stack, and they are usually being conducted under static analysis thanks to its speed of execution. However, to the best of our knowledge, no single work has yet simultaneously undertaken a full-stack intralayer and cross-layer features fusion for smart contracts vulnerability assessment under static analysis, without making use of expert-based patterns nor without manually fusing the various features extracted from shuffled partial combinations of layers in the full-stack. This paper introduces a full-stack hierarchical fusion of static features for smart contracts vulnerability detection. In our construction, we associate each layer of the full-stack to a modality and leverage automatic intramodality and crossmodality pooled features fusion from state-of-the-art artificial neural networks and deep neural networks. Additionally, our models are applied to the hierarchy of power set layers in the full-stack, without any expert-based rule. Furthermore, our work aims to assess the increase in vulnerability detection performance and provide guidance for future research on smart contracts vulnerability detection.