Automating Vulnerability Detection and Prevention in Smart ContractsSpeaker: Latifur Rahman Khan – Plano, TX, United States
Topic(s): Security and Privacy
AbstractWith the increase in the adoption of blockchain technology in providing decentralized solutions to various problems, smart contracts have been becoming more popular to the point that billions of US Dollars are currently exchanged every day through such technology. Meanwhile, various vulnerabilities in smart contracts have been exploited by attackers to steal cryptocurrencies worth millions of dollars. The automatic detection of smart contract vulnerabilities is an essential research problem. Yet, existing solutions to this problem particularly rely on human experts to define features or different rules to detect vulnerabilities; which often lead to missing many vulnerabilities and they are inefficient detecting new vulnerabilities. In this study, we address these challenges and propose a framework to analyze the data and detect some vulnerabilities in Ethereum smart contracts on the blockchain platform. We apply machine learning-based(i.e., deep learning-based) vulnerability detection to relieve human experts from the tedious and subjective task of manually defining features and rules. For prevention, an Ethereum bytecode rewriting and validation method will be presented and evaluated for securing smart contracts in decentralized cryptocurrency systems without access to contract source code.
About this LectureNumber of Slides: 50
Duration: 45 minutes
Languages Available: English
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