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Found 5 papers, 2 papers with code
Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning
To address these challenges, we propose TLMG4Eth that combines a transaction language model with graph-based methods to capture semantic, similarity, and structural features of transaction data in Ethereum.
The Role of Transformer Models in Advancing Blockchain Technology: A Systematic Survey
As blockchain technology rapidly evolves, the demand for enhanced efficiency, security, and scalability grows. Transformer models, as powerful deep learning architectures, have shown unprecedented potential in addressing various blockchain challenges.
Edge Computing for IoT: Novel Insights from a Comparative Analysis of Access Control Models
IoT edge computing positions computing resources closer to the data sources to reduce the latency, relieve the bandwidth pressure on the cloud, and enhance data security.
Struggle with Adversarial Defense? Try Diffusion
Unlike data-driven classifiers, TMDC, guided by Bayesian principles, utilizes the conditional likelihood from diffusion models to determine the class probabilities of input images, thereby insulating against the influences of data shift and the limitations of adversarial training.
URLBERT:A Contrastive and Adversarial Pre-trained Model for URL Classification
To address this gap, this paper introduces URLBERT, the first pre-trained representation learning model applied to a variety of URL classification or detection tasks.
