Automation and smart materials in detecting smart contracts vulnerabilities in Blockchain using deep learning

Author:

Narayana K. Lakshmi,Sathiyamurthy K.

Publisher

Elsevier BV

Subject

General Medicine

Reference23 articles.

1. SoliAudit: Smart Contract Vulnerability Assessment Based on Machine Learning and Fuzz Testing, 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS);Jian-Wei,2019

2. Wesley Joon-Wie Tann, Xing Jie Han, Sourav Sen Gupta, and Yew-Soon Ong. Towards Safer Smart Contracts: A Sequence Learning Approach to Detecting Security Threats. In Proceedings of ACM (Conference ’19), 2019; ACM, New York, NY, USA, 12 pages.

3. Veloso. (Aug. 08, 2018). smartbugs/dataset/. Accessed on: Nov 2, 2020. [Online]. Available: https:// github.com/smartbugs/smartbugs/tree/master/dataset.

4. B. Mueller (Sep. 17, 2017). ConsenSys/Mythril. Accessed on: Nov 2, 2020; [Online] Available: http://github.com/ConsenSys/ mythril.

5. J Krupp and C.Rossow. teEther: Gnawing at Ethereum to automatically exploit smart contracts. In Proc. 27th USENIX Secur. Symp., Baltimore, MD, USA, 2018; pp. 1317–1333.

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