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 With the rise in prominence of crowdsourced datasets in machine learning, data poisoning attacks pose a considerable threat. Many current defenses fall short because they are overly specialized for certain attacks, lack contribution incentives, and are difficult to integrate into current platforms. This paper explores the underaddressed system security problem posed by data poisoning through SeBRUS, a comprehensive data contribution application that leverages Ethereum smart contracts to secure crowdsourced datasets. SeBRUS introduces a voting network and poisoned data detection model, allowing for easy implementation with current platforms to defend against label-flipping, clean-label, and backdoor attacks.
 
-This is SeBRUS (SEH-BRUS), or Secure Blockchain Regularization Upload System. This repository consists of the code for the SeBRUS application associated with our group's research. SeBRUS is built to allow for simple and secure machine learning dataset contribution by using the Ethereum blockchain to ensure the security of crowdsourced contributions to machine learning datasets.
+This is [SeBRUS (SEH-BRUS)](https://en.wikipedia.org/wiki/Cerberus), or Secure Blockchain Regularization Upload System. This repository consists of the code for the SeBRUS application associated with our group's research. SeBRUS is built to allow for simple and secure machine learning dataset contribution by using the Ethereum blockchain to ensure the security of crowdsourced contributions to machine learning datasets.
 
 This research project was conducted as part of the [Governor's School of Engineering and Technology](https://soe.rutgers.edu/academics/pre-college-engineering-programs/new-jersey-governors-school-engineering-and-technology) 2023 program. To read more about our research, please read our [paper](./.github/paper.pdf) which was accepted to the 2023 [MIT IEEE Undergraduate Research Technology Conference](https://urtc.mit.edu/). For a demonstration of how to use our project, please watch this [video](https://drive.google.com/file/d/1mqfoFTWgBrgPmEPCjlzGkFySi_8S5huI/view?usp=sharing).