With Trail to Follow: Measurements of Real-world Non-fungible Token Phishing Attacks on Ethereum

With the popularity of Non-Fungible Tokens (NFTs), NFTs have become a new target of phishing attacks, posing a significant threat to the NFT trading ecosystem. There has been growing anecdotal evidence that new means of NFT phishing attacks have emerged in Ethereum ecosystem. Most of the existing research focus on detecting phishing scam accounts for native cryptocurrency on the blockchain, but there is a lack of research in the area of phishing attacks of emerging NFTs. Although a few studies have recently started to focus on the analysis and detection of NFT phishing attacks, NFT phishing attack means are diverse and little has been done to understand these various types of NFT phishing attacks. To the best of our knowledge, we are the first to conduct case retrospective analysis and measurement study of real-world historical NFT phishing attacks on Ethereum. By manually analyzing the existing scams reported by Chainabuse, we classify NFT phishing attacks into four patterns. For each pattern, we further investigate the tricks and working principles of them. Based on 469 NFT phishing accounts collected up until October 2022 from multiple channels, we perform a measurement study of on-chain transaction data crawled from Etherscan to characterizing NFT phishing scams by analyzing the modus operandi and preferences of NFT phishing scammers, as well as economic impacts and whereabouts of stolen NFTs. We classify NFT phishing transactions into one of the four patterns by log parsing and transaction record parsing. We find these phishing accounts stole 19,514 NFTs for a total profit of 8,858.431 ETH (around 18.57 million dollars). We also observe that scammers remain highly active in the last two years and favor certain categories and series of NFTs, accompanied with signs of gang theft

Lithography-induced hydrophobic surfaces of silicon wafers with excellent anisotropic wetting properties

In recent years, hydrophobic surfaces have attracted more and more attentions from many researchers. In this paper, we comprehensively discussed the effects of specific parameters of microstructures on the wetting properties by using the theoretical models, the effects of microstructures on two-dimensional anisotropic properties and the water droplet impact experiment. Firstly, the relationships between the CAs and variable parameters were explored after the formula derivation for three various patterns. Then three different patterns were fabricated successfully on the silicon wafers by lithography technology and the effects of microstructures (including LWD parameters and interval parameters) on surface wettability were studied based on the theoretical research. After that, the effects of microstructures on two-dimensional anisotropic properties were also studied. Finally, the water droplet impact experiment was carried out and the viscoelastic properties were simply investigated. Our research proposed a potential method for fabricating hydrophobic surfaces with excellent anisotropic properties. This method may be widely used in a variety of academic and industrial applications in the future

Streaming phishing scam detection method on Ethereum

Phishing is a widespread scam activity on Ethereum, causing huge financial losses to victims. Most existing phishing scam detection methods abstract accounts on Ethereum as nodes and transactions as edges, then use manual statistics of static node features to obtain node embedding and finally identify phishing scams through classification models. However, these methods can not dynamically learn new Ethereum transactions. Since the phishing scams finished in a short time, a method that can detect phishing scams in real-time is needed. In this paper, we propose a streaming phishing scam detection method. To achieve streaming detection and capture the dynamic changes of Ethereum transactions, we first abstract transactions into edge features instead of node features, and then design a broadcast mechanism and a storage module, which integrate historical transaction information and neighbor transaction information to strengthen the node embedding. Finally, the node embedding can be learned from the storage module and the previous node embedding. Experimental results show that our method achieves decent performance on the Ethereum phishing scam detection task

Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer

Background and ObjectiveEndometrial cancer (EC) is a common gynecological malignancy worldwide. Despite advances in the development of strategies for treating EC, prognosis of the disease remains unsatisfactory, especially for advanced EC. The aim of this study was to identify novel genes that can be used as potential biomarkers for identifying the prognosis of EC and to construct a novel risk stratification using these genes.Methods and ResultsAn mRNA sequencing dataset, corresponding survival data and expression profiling of an array of EC patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, respectively. Common differentially expressed genes (DEGs) were identified based on sequencing and expression as given in the profiling dataset. Pathway enrichment analysis of the DEGs was performed using the Database for Annotation, Visualization, and Integrated Discovery. The protein–protein interaction network was established using the string online database in order to identify hub genes. Univariate and multivariable Cox regression analyses were used to screen prognostic DEGs and to construct a prognostic signature. Survival analysis based on the prognostic signature was performed on TCGA EC dataset. A total of 255 common DEGs were found and 11 hub genes (TOP2A, CDK1, CCNB1, CCNB2, AURKA, PCNA, CCNA2, BIRC5, NDC80, CDC20, and BUB1BA) that may be closely related to the pathogenesis of EC were identified. A panel of 7 DEG signatures consisting of PHLDA2, GGH, ESPL1, FAM184A, KIAA1644, ESPL1, and TRPM4 were constructed. The signature performed well for prognosis prediction (p < 0.001) and time-dependent receiver–operating characteristic (ROC) analysis displayed an area under the curve (AUC) of 0.797, 0.734, 0.729, and 0.647 for 1, 3, 5, and 10-year overall survival (OS) prediction, respectively.ConclusionThis study identified potential genes that may be involved in the pathophysiology of EC and constructed a novel gene expression signature for EC risk stratification and prognosis prediction

In situ monitoring and universal modelling of sacrificial PSG etching using hydrofluoric acid

A video system has been designed to monitor in situ and accurately the etching of sacrificial phosphosilicate-glass (PSG) microchannels using hydrofluoric acid (HF). An universal model, which predicts accurately the etching length vs. time over a wide range of HF concentration (3-49 wt.%), has been identified. In addition to diffusion, this model is based on a first-and-second order chemical reaction mechanism. It is found that the PSG microchannel etching rate in HF is sensitive to channel thickness but not width. Finally, bubble formation and movement inside the etched microchannels are observed. Most of the generated bubbles are mobile and can enhance the etching rate

Cohort Profile: The Jiaxing Birth Cohort in China.

This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by Oxford University Press.This work was supported by the National Basic Research Program of China (973 Program: 2015CB553604); by National Natural Science Foundation of China (NSFC: 81273054); and by the Ph.D. Programs Foundation of Ministry of Education of China (20120101110107)