Search Results (3)

Smart contracts refer to small programs that run in a decentralized blockchain infrastructure. The blockchain system is trustless, and the determination of common variables is done by consensus between peers. Developing applications that require generating random variables becomes significantly challenging—for instance, lotteries, games, and random assignments. Many random number generators (RNGs) for smart contracts have been developed for the decentralized environment. The methods can be classified into three categories: on-chain RNG, Verifiable Random Function (VRF), and the Commit–reveal scheme. Although the existing methods offer different strengths and weaknesses, none achieves the three important requirements for an ideal RNG solution: security, applicability, and cost efficiency. This paper proposes a novel RNG approach called Native VRF, which offers application development simplicity and cost efficiency while maintaining strong RNG security properties. Experimental results show that Native VRF has the same security properties as the widely used RNG methods, i.e., Randao and Chainlink VRF. On top of that, our work offers a much simpler setup process and lower hardware resources and developer expertise requirements. Most importantly, the proposed Native VRF is compatible with all Ethereum virtual machine (EVM) blockchains, contributing to the overall growth of the blockchain ecosystem. Full article

Understanding the technical properties of the subsurface soil is essential for carrying out any building project correctly. In sandy soils in Phuket, Thailand, a correlation between electrical resistivity and the SPT N-value should be established in design to cut down on the time and expense of engineering field work for site investigation operations. Using the least squares method, the data of electrical resistivity and SPT N-values were fitted for regression analysis, and the behavior of the fitting coefficients was examined under various soil conditions found in the field. The factors affecting the link between electrical resistivity and the SPT N-value were found using data classifications of geology and climate. Phuket is located in a humid tropical region with frequent heavy rainstorms and extended periods of high temperatures and is composed of two main distinct geological areas: (1) igneous rock and (2) sedimentary and metamorphic rocks. According to the geological classification, the regression parameter (R²) of the relationship between the electrical resistivity and SPT N-values somewhat increased from a straightforward plot (R² = 0.0171) to 0.1721, and the geology and climate data categorization revealed the best fit which is an exponential model (R² = 0.6175). By re-examining the two VES lines under the identical bedrock and climate characteristics as the original model, the relative R² of 0.5519 validates the association between electrical resistivity and SPT N-values. It is important to emphasize that the geology and climate of the collected data have an impact on the correlation model. Furthermore, a geotechnical investigation should be used to confirm the findings of an electrical resistivity survey as a preliminary tool to assess any problematic subsurface zones. However, more field test data from diverse places must be acquired in order to establish the relationship between the SPT N-value and electrical resistivity of sandy soils. Full article

Decentralized finance (DeFi) has exploded in popularity with a billion-dollar market cap. While uncollateralized lending, known as a flash loan, emerged from DeFi, it has become a primary tool used by attackers to drain investment tokens from DeFi networks. The existing countermeasures seem practical, but no comprehensive quantitative analysis framework was available to test them. This paper proposes the Flash loan Attack Analysis (FAA) framework, which aids security practitioners in understanding the DeFi system’s effects on preventative methods when various factors change. The quantitative predictions can help security professionals in identifying hidden dangers and more efficiently adopting countermeasure strategies. The simulation predicts that the existing strategy, fair reserves, can fully protect the platform in a typical market environment; however, in a highly volatile market where the token price drops by 60% in a single hour, it will be broken, causing more than $8 million in damage. Full article