Sökning: (WFRF:(Gurov Dilian 1964 )) >
Dynamic Vulnerabili...
Dynamic Vulnerability Detection on Smart Contracts Using Machine Learning
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- Eshghie, Mojtaba (författare)
- KTH,Teoretisk datalogi, TCS
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- Artho, Cyrille (författare)
- KTH,Teoretisk datalogi, TCS
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- Gurov, Dilian, 1964- (författare)
- KTH,Teoretisk datalogi, TCS
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(creator_code:org_t)
- 2021-06-21
- 2021
- Engelska.
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Ingår i: Proceedings Of Evaluation And Assessment In Software Engineering (EASE 2021). - New York, NY, USA : Association for Computing Machinery (ACM). ; , s. 305-312
- Relaterad länk:
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http://arxiv.org/pdf...
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visa fler...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- In this work we propose Dynamit, a monitoring framework to detect reentrancy vulnerabilities in Ethereum smart contracts. The novelty of our framework is that it relies only on transaction metadata and balance data from the blockchain system; our approach requires no domain knowledge, code instrumentation, or special execution environment. Dynamit extracts features from transaction data and uses a machine learning model to classify transactions as benign or harmful. Therefore, not only can we find the contracts that are vulnerable to reentrancy attacks, but we also get an execution trace that reproduces the attack. Using a random forest classifier, our model achieved more than 90 percent accuracy on 105 transactions, showing the potential of our technique.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Nyckelord
- Smart Contracts
- Vulnerability Detection
- Machine Learning for Dynamic Software Analysis
- Ethereum
- Blockchain
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)