Towards a First Step to Understand the Cryptocurrency Stealing Attack on Ethereum
April 03, 2019 ยท Declared Dead ยท ๐ International Symposium on Recent Advances in Intrusion Detection
Authors
Zhen Cheng, Xinrui Hou, Runhuai Li, Yajin Zhou, Xiapu Luo, Jinku Li, Kui Ren
arXiv ID
1904.01981
Category
cs.CR: Cryptography & Security
Citations
36
Venue
International Symposium on Recent Advances in Intrusion Detection
Repository
https://github.com/zjuicsr/eth-honey
Last Checked
1 month ago
Abstract
We performed the first systematic study of a new attack on Ethereum that steals cryptocurrencies. The attack is due to the unprotected JSON-RPC endpoints existed in Ethereum nodes that could be exploited by attackers to transfer the Ether and ERC20 tokens to attackers-controlled accounts. This study aims to shed light on the attack, including malicious behaviors and profits of attackers. Specifically, we first designed and implemented a honeypot that could capture real attacks in the wild. We then deployed the honeypot and reported results of the collected data in a period of six months. In total, our system captured more than 308 million requests from 1,072 distinct IP addresses. We further grouped attackers into 36 groups with 59 distinct Ethereum accounts. Among them, attackers of 34 groups were stealing the Ether, while other 2 groups were targeting ERC20 tokens. The further behavior analysis showed that attackers were following a three-steps pattern to steal the Ether. Moreover, we observed an interesting type of transaction called zero gas transaction, which has been leveraged by attackers to steal ERC20 tokens. At last, we estimated the overall profits of attackers. To engage the whole community, the dataset of captured attacks is released on https://github.com/zjuicsr/eth-honey.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Cryptography & Security
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Membership Inference Attacks against Machine Learning Models
R.I.P.
๐ป
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
๐ป
Ghosted
Practical Black-Box Attacks against Machine Learning
R.I.P.
๐ป
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
๐ป
Ghosted
Extracting Training Data from Large Language Models
Died the same way โ ๐ 404 Not Found
R.I.P.
๐
404 Not Found
Deep High-Resolution Representation Learning for Visual Recognition
R.I.P.
๐
404 Not Found
HuggingFace's Transformers: State-of-the-art Natural Language Processing
R.I.P.
๐
404 Not Found
CCNet: Criss-Cross Attention for Semantic Segmentation
R.I.P.
๐
404 Not Found