Quantifying Malicious Smart Contract Behavior via Automated Abstract Syntax Tree Feature Mapping

International Journal of Computer Science and Technology | Vol. 4, No. 7, July 2023 | pp. 1–8

DOI: 10.46882/2023/IJCST/000222

Article Type: Original Research Paper

Title: Quantifying Malicious Smart Contract Behavior via Automated Abstract Syntax Tree Feature Mapping

Names of Authors: Sanjay Nair¹, Takashi Sato²

Authors’ Affiliations: ¹Cyber Security Research Center, Indian Institute of Information Technology, Bangalore, India; ²School of Engineering, Tokyo Institute of Technology, Tokyo, Japan

Abstract: Decentralized application protocols running on public blockchain ecosystems remain highly vulnerable to smart contract vulnerabilities, exposing digital assets to targeted reentrancy and integer overflow exploits. Manual source-code validation is slow and cannot scale with the rapid deployment rate of smart contracts. This article introduces an automated vulnerability identification pipeline that uses structural feature mapping of Abstract Syntax Trees (ASTs). The pipeline extracts syntactic dependencies and data-flow pathways from smart contract source files, converting them into structured low-dimensional vector representations. A random forest classifier evaluates these feature maps to isolate architectural flaws before compilation or deployment. We tested this validation framework using a verification dataset of 4,500 checked Ethereum smart contracts. The analysis results prove that the tool achieves a true positive classification rate of 97.4%, surpassing standard rule-based pattern matching tools by 14.5%. The average analysis duration recorded was 240 ms per contract file, making it suitable for continuous integration (CI/CD) pipelines.

Keywords: Blockchain, Smart Contracts, Vulnerability Assessment, Abstract Syntax Tree, Automated Detection, Cybersecurity

Manuscript Timeline: Received: February 18, 2023; Revised: April 10, 2023; Accepted: May 20, 2023; Published: July 11, 2023

Citation: Nair, S., & Sato, T. (2023). Quantifying Malicious Smart Contract Behavior via Automated Abstract Syntax Tree Feature Mapping. International Journal of Computer Science and Technology, 4(7), 1–8. DOI: 10.46882/2023/IJCST/000222

Subscribe to read the full article: https://internationalscholarsjournals.org/subscribe-to-read