REVIEW 2 cited by
Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
As more and more attacks have been detected on Ethereum smart contracts, it has seriously affected finance and credibility. Current anti-fraud detection techniques, including code parsing or manual feature extraction, still have some shortcomings, although some generalization or adaptability can be obtained. In the face of this situation, this paper proposes to use graphical representation learning technology to find transaction patterns and distinguish malicious transaction contracts, that is, to represent Ethereum transaction data as graphs, and then use advanced ML technology to obtain reliable and accurate results. Taking into account the sample imbalance, we treated with SMOTE-ENN and tested several models, in which MLP performed better than GCN, but the exact effect depends on its field trials. Our research opens up more possibilities for trust and security in the Ethereum ecosystem.
Forward citations
Cited by 2 Pith papers
-
Curriculum Guided Reinforcement Learning for Efficient Multi Hop Retrieval Augmented Generation
EVO-RAG applies curriculum-guided reinforcement learning with time-varying reward weights to multi-hop RAG, reporting improved EM on HotpotQA, 2WikiMultiHopQA, and MuSiQue.
-
DeepRAG: Integrating Hierarchical Reasoning and Process Supervision for Biomedical Multi-Hop QA
DeepRAG, a combination of DeepSeek R1 hierarchical decomposition and RAG-Gym process supervision with UMLS concept rewards, reports EM 62.4 and concept accuracy 71.8 on the MedHopQA dev set.
Discussion (0). Sign in to comment.