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Paper Type
Complete
Description
We analyze the most common security vulnerabilities in distributed ledger systems. We performed predictive analytics on the REKT database to predict the attack pattern using predictive algorithms, including logistic regression and random forests. The results show that the month of the attack and the cryptocurrency chain affected were significant predictors of the type of scam that occurred. The most important predictors were the Log of funds lost, the chain or platform of the cryptocurrency attacked, and the Log of funds returned after the attack. The study highlights the need for greater scrutiny and improved security measures in DeFi projects to mitigate the risks associated with the DeFi ecosystem.
Paper Number
1333
Recommended Citation
Padalkar, Nakul Ravindra and Ghura, Tegveer Singh Yashwinder, "DEFIYIELD: Exploitation of Open Blockchain Platforms" (2023). AMCIS 2023 Proceedings. 10.
https://aisel.aisnet.org/amcis2023/sig_sec/sig_sec/10
DEFIYIELD: Exploitation of Open Blockchain Platforms
We analyze the most common security vulnerabilities in distributed ledger systems. We performed predictive analytics on the REKT database to predict the attack pattern using predictive algorithms, including logistic regression and random forests. The results show that the month of the attack and the cryptocurrency chain affected were significant predictors of the type of scam that occurred. The most important predictors were the Log of funds lost, the chain or platform of the cryptocurrency attacked, and the Log of funds returned after the attack. The study highlights the need for greater scrutiny and improved security measures in DeFi projects to mitigate the risks associated with the DeFi ecosystem.
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