The paper presents a method for early detection of fraudulent memecoins (rug pulls) on the Solana blockchain, using a dataset of 6.4 million tokens collected over seven months. It shows that most rug pulls occur within an hour of launch and that classic machine learning models, especially Gradient Boosting (XGBoost), can reliably predict them using only the first five minutes of trading data. Cross‑platform data fusion between PumpFun and Raydium further improves detection by reducing domain shift.
arXiv:2608. 20271v1 Announce Type: new Abstract: The rapid proliferation of memecoins on blockchain platforms has increased the risk of fraudulent activities, particularly rug pulls.
By Jianghai Li, Pavel Kuznetsov, Yury Yanovich, Konstantin Nott-Whaley, Igor Vodolazov
arXiv:2607. 27370v1 Announce Type: new Abstract: Sybil attackers are Blockchain actors that adopt the characteristics of regular users to exploit airdrops or influence governance.
By Micha{\l} Bartnicki, Jaros{\l}aw A. Chudziak
arXiv:2607. 27350v1 Announce Type: new Abstract: Sybil bots are Ethereum actors that imitate legitimate users to extract airdrop rewards or influence governance.
By Micha{\l} Bartnicki, Jaros{\l}aw A. Chudziak
arXiv:2605. 29526v2 Announce Type: replace-cross Abstract: Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addresses and diverse anomalous behaviors.
By Runang He, Tongya Zheng, Huiling Peng, Yuanyu Wan, Bingde Hu, Jiawei Chen, Canghong Jin, Mingli Song, Can Wang
arXiv:2508.20517v2 Announce Type: replace-cross
Abstract: Cross-chain bridges enable asset and state transfers across heterogeneous blockchains, but their complex cross-domain interactions introduce...
By Dan Lin, Shunfeng Lu, Ziyan Liu, Jiajing Wu, Junyuan Fang, Jianzhong Su, Bowen Song, Qing Xia, Zibin Zheng
The paper presents a semi‑supervised learning framework for detecting illicit Bitcoin flows in Shared Send Mixers, using a large historical dataset of 163 million transactions. It demonstrates that the success of SSL depends on data quality rather than sheer volume, with high‑fidelity features such as KeyLinker address clustering and Shared Send Untangling complexity metrics achieving an F1 score of 0.84 on unlabeled data. The study also shows that common heuristics like One‑Time Change introduce noise, underscoring the importance of smarter feature engineering in blockchain forensics.
By Yekaterina Smolenkova, Nickolay Larionov, Nikolay Ivanov, Yury Yanovich
arXiv:2604. 17420v2 Announce Type: replace-cross Abstract: Money laundering poses severe risks to global financial systems, driving the widespread adoption of machine learning for transaction monitoring.
By Keyang Chen, Mingxuan Jiang, Yongsheng Zhao, Zeping Li, Zaiyuan Chen, Weiqi Luo, Zhixin Li, Sen Liu, Yinan Jing, Guangnan Ye, Xihong Wu, Hongfeng Chai
DeFiFusion is a dual‑modal framework that detects price manipulation attacks in decentralized finance by jointly analyzing transaction events and smart contract semantics. It encodes fine‑grained temporal and economic features from transactions and extracts contract logic using large language models, then fuses these signals with a Dual‑Modal Projection‑Fusion Transformer. The approach achieves state‑of‑the‑art performance, recalling 222 of 225 known attacks with 96.10% precision.
By Rui Cao, Shaojing Fan, Liming Fang, Yuchan Liu, Yingying Jiao, Zhenguang Liu
TxSum introduces a user-centered approach to understanding Ethereum transactions by providing structured, risk-aware explanations grounded at the token‑flow level. The authors built a dataset of 187 complex transactions with 2,375 token‑flow annotations and transaction‑level summaries, and developed MATEX, a multi‑agent framework that retrieves external knowledge and audits explanations for factual consistency. MATEX outperforms existing baselines, improving user comprehension from 52.9% to 76.5% and increasing malicious‑transaction rejection from 36.0% to 88.0% while keeping false‑rejection rates low.
By Zifan Peng, Jingyi Zheng, Yule Liu, Huaiyu Jia, Qiming Ye, Jingyu Liu, Xufeng Yang, Mingchen Li, Qingyuan Gong, Xuechao Wang, Xinlei He
arXiv:2602. 02838v2 Announce Type: replace-cross Abstract: The detection of online influence operations -- coordinated campaigns by malicious actors to spread narratives -- has traditionally depended on content analysis or network features.
By Philipp J. Schneider, Lanqin Yuan, Marian-Andrei Rizoiu
arXiv:2607. 06963v1 Announce Type: cross Abstract: Large Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion by OpenAI, Anthropic, Google, Meta, Microsoft, Stability AI, respectively, are revolutionizing cybersecurity, enabling both automated defense and sophisticated attacks.
By Kiarash Ahi, Saeed Valizadeh