Bitcoin Selfish Mining Modeling and Dependability Analysis

Blockchain technology has gained prominence over the last decade. Numerous achievements have been made regarding how this technology can be utilized in different aspects of the industry, market, and governmental departments. Due to the safety-critical and security-critical nature of their uses, it is pivotal to model the dependability of blockchain-based systems. In this study, we focus on Bitcoin, a blockchain-based peer-to-peer cryptocurrency system. A continuous-time Markov chain-based analytical method is put forward to model and quantify the dependability of the Bitcoin system under selfish mining attacks. Numerical results are provided to examine the influences of several key parameters related to selfish miners’ computing power, attack triggering, and honest miners’ recovery capability. The conclusion made based on this research may contribute to the design of resilience algorithms to enhance the self-defense and robustness of cryptocurrency systems

Reliability Analysis of Crude Unit Overhead Piping Based on Wall Thickness Degradation Process

Assuring the reliability of crude unit pipelines in the downstream oil and gas industry is highly essential since unexpected failures of these pipelines can result in a number of negative impacts to the business, including safety, environmental, and economic impacts. The objective of this work is to understand the degradation behavior of the piping system so we can know in advance when the degraded pipeline will reach the minimum thickness threshold

Load Redistribution-based Reliability Enhancement for Storage Area Networks

Storage area networks (SANs) are one of the prevalent reliable data storage solutions. However, cascading failures triggered by data overloading have become a major threat to SANs, preventing the desired quality of service from being delivered to users. Based on our preliminary works on studying the impacts of data loading on the reliability performance of SANs, this paper advances the state of the art by implementing node degree-based load redistribution strategies to enhance the SAN reliability, thus mitigating or even preventing the occurrence of cascading failures during the mission time. Load-based and reliability-based node selection rules are considered, which choose nodes with the highest load level and the lowest reliability for load redistribution, respectively. The relationship between data loading and reliability of an individual SAN component is modeled using the accelerated failure-time model with the power law. The SAN reliability is assessed using a combinatorial decision diagram-based approach. The application and effectiveness of the proposed load redistribution strategies are demonstrated and compared through a case study of an SAN with the mesh topology

System-Level Dependability Analysis of Bitcoin under Eclipse and 51% Attacks

Bitcoin is an electronic cryptocurrency developed based on Blockchain technology. With its decentralized feature, it has become incredibly popular since its invention. However, the Bitcoin network suffers from 51% attacks, where if malicious attackers’ control over half of the computing power, they are able to rewrite the network. The attackers are capable of doing so by initiating the Eclipse attack first, which aims to monopolize all communications from and to a controlled Bitcoin node. In this paper, we model and analyze the dependability of the Bitcoin network subject to the Eclipse and 51% attacks. We propose a hierarchical model that encompasses a continuous-time Markov chain method for the node-level dependability analysis and a multi-valued decision diagram method for the system-level dependability analysis. Detailed case studies on Bitcoin systems with homogeneous and heterogeneous nodes are conducted to demonstrate the proposed model and investigate the impacts of several critical parameters on Bitcoin network dependability

Data-driven maintenance priority and resilience evaluation of performance loss in a main coolant system

The main coolant system (MCS) plays a vital role in the stability and reliability of a nuclear power plant. However, human errors and natural disasters may cause some reactor coolant system components to fail, resulting in severe consequences such as nuclear leakage. Therefore, it is crucial to perform a resilience analysis of the MCS, to effectively reduce and prevent losses. In this paper, a resilience importance measure (RIM) for performance loss is proposed to evaluate the performance of the MCS. Specifically, a loss importance measure (LIM) is first proposed to indicate the component maintenance priority of the MCS under different failure conditions. Based on the LIM, RIMs for single component failure and multiple component failures were developed to measure the recovery efficiency of the system performance. Finally, a case study was conducted to demonstrate the proposed resilience measure for system reliability. Results provide a valuable reference for increasing the system security of the MCS and choosing the appropriate total maintenance cost

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data