Smart contracts vulnerabilities detection using ensemble architecture of graphical attention model distillation and inference network
Year: 2024
Type: article
Abstract: Smart contracts are automated agreements executed on a blockchain, offering reliability through their immutable and distributed nature. Yet, their unalterable deployment necessitates precise preemptive security checks, as vulnerabilities could lead to substantial financial damages henceforth testing for vulnerabilities is necessary prior to deployment. This paper presents the graphical attention model distillation and inference network (GAMDI-Net), a pioneering methodology that significantly enhances smart contract vulnerability detection. GAMDI-Net introduces a unique graphical learning module that employs attention mechanism networks to transform complex contract code into a smart graphical representation. In addition to this a dual-modality model distillation and mutual modality learning mechanism, GAMDI-Net excels in synthesizing semantic and control flow data to predict absent bytecode embeddings with high accuracy. This methodology not only improves the precision of vulnerability detection but also addresses scalability and efficiency challenges, reinforcing trust in the deployment of secure smart contracts within the blockchain ecosystem.
Authors: Preethi Preethi, Mohammed Mujeer Ulla, Ashwitha Anni, Pavithra Narasimha Murthy, Sapna Renukaradhya
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404853351