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.
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.
The paper introduces a continuously synchronized dataset suite covering the entire lifecycle of decentralized prediction markets, from market creation to final settlement. It integrates market metadata, fill-level trading records, and oracle-resolution events into a unified relational system, providing over 3.29 million market records, 1.90 billion order executions, and 21 million oracle events from October 2020 to the present. The authors detail the data model, collection pipeline, and consistency mechanisms, and demonstrate the dataset’s usefulness for sports betting, economic forecasting, and blockchain research, with public access via a website and interactive tools.
arXiv:2606. 25007v1 Announce Type: new Abstract: Financial fraud detection in digital banking requires reasoning over multiple heterogeneous event streams -- transactions, login sessions, risk signals -- that individually appear benign but collectively reveal fraudulent patterns.
arXiv:2610.01168v1 Announce Type: cross Abstract: Time Series Anomaly Detection has received increasing attention, driven by the growing availability of complex time series data. This surge has led t...
arXiv:2609.39257v1 Announce Type: cross Abstract: Accurate forecasting of electricity production is essential for maintaining the operational efficiency and strategic planning of energy utilities. In...
arXiv:2607. 27350v1 Announce Type: new Abstract: Sybil bots are Ethereum actors that imitate legitimate users to extract airdrop rewards or influence governance.
arXiv:2510. 22397v2 Announce Type: replace-cross Abstract: Network operators monitor their infrastructure by collecting telemetry data such as packet counts, byte rates, or flow volumes, yet answering the questions that effective operations demand -- forecasting future load, diagnosing and characterizing anomalies, and searching for and retrieving historical precedents -- requires more than raw measurements.
arXiv:2607. 29383v1 Announce Type: new Abstract: In recent years, with the development of big data technology, increasingly more companies use HDFS for data processing and storage.
TxnQA is a new analytical QA benchmark that tests reasoning and evidence grounding over transaction event sequences. It contains 27 task families with 81 templates and a 5‑scenario fraud‑risk reasoning suite built on synthetic, privacy‑mitigated transaction data. The benchmark evaluates models using format‑following rate, conditional content accuracy, and end‑to‑end correctness, revealing distinct bottlenecks in schema compliance, semantic calculation, and evidence attribution.
arXiv:2607. 17555v1 Announce Type: new Abstract: Predicting extreme price movements in high-frequency financial markets is a challenging task due to non-stationarity, heavy-tailed return distributions, and severe class imbalance.
arXiv:2606. 01741v1 Announce Type: cross Abstract: Distributed event-based systems have become a common substrate for Internet-scale publish/subscribe services, IoT telemetry, cloud-native microservices, and security operations pipelines.