The coming 6G wireless network is poised to achieve unprecedented data rates, latency, and integration with newer technologies like AI and IoE. On the other hand, along with this kind of growth in the AI domain and the large-scale connectivity in 6G. It is also going to raise many security concerns at the level of intrusion detection and prevention. For intrusion detection, centralized approaches wonât be able to work effectively, therefore there is an utmost need to design decentralized and privacy-preserving solutions. In this work, we propose a novel secure gradients exchange algorithm for distributed intrusion detection in 6G networks. Our method is designed to take into account the use of Federated Learning with secure multi-party computation and blockchain technology. This way ensures that the collaborating parties are able to conduct the training of intrusion detection models in a secure and collaborative manner by retaining privacy in the data. Gradient compression and adaptive secure aggregation strategies are used to further optimize communication overhead and computational complexity. Therefore, our design works in a robust and efficient manner with the high data rates and huge connectivity that 6G networks will provide. To achieve our goal, experiments using the CICIoT2023 dataset were performed, and results showed that our federated learning-based hybrid model composed of CNN1D and a multi-head attention mechanism outperformed other well-known deep learning models in terms of performance. It achieved the highest average accuracy with 79.92%, the highest average detection rate with 77.41%, and a low false alarm rate with 2.55%.
Support tools that monitor software application processes are required to identify, measure, and assess their performance and resolve any anomalies that impede their full functionality. Similarly, applications that use blockchain technology require analysis and monitoring tools for the resources and functionality provided by their smart contracts. This paper presents a computational tool that monitors hardware resources and evaluates transaction processing requests in blockchain networks. The solution presented here is called MonitorChain, and it has software components that allow the connection with different blockchain networks, the configuration of the workload, the monitoring of consumed hardware resources, and the evaluation of requests processed over blockchain networks. The effectiveness and adaptability of MonitorChain were validated through extensive evaluations across various blockchain environments, including public networks like Ethereum, Avalanche, and Fantom, as well as a private network using Hyperledger Fabric, with a consistent data load of 10,000 transactions. Additionally, through interviews with experienced software developers, we gathered feedback that led to further refinements and identified opportunities for future enhancements. The results demonstrated MonitorChainâs capability to provide critical insights into blockchain applicationsâ performance, scalability, and efficiency. MonitorChain advances the field compared to existing solutions by offering enhanced support for diverse networks, customizable workloads, and real-time data visualization, contributing significantly to the blockchain monitoring landscape.
The primary objective of this project was to improve the cryptocurrency brokerassessment model for a financial services evaluation platform. This initiative focused onreassessing existing cryptocurrency exchanges in the companyâs assessment model bysearching and updating the input data, and, eventually, providing recommendations formodel improvement. Ultimately, the project aimed to improve the accuracy of theplatform's evaluations to assist their users in making informed financial decisions.
The increasing complexity of decentralized financial systems has necessitated advanced orchestration mechanisms capable of managing dynamic, distributed microservices. Conventional orchestration methods tend to be less flexible, scalable and transparent to support real time financial functions. The proposed paper presents a new Hierarchical Agentic Orchestration architecture, which uses neuro-symbolic intelligence to facilitate the dynamism in composing a workflow in decentralized finance (DeFi) systems. The suggested model uses a multi-level hierarchy of intelligent agents such global, domain and local agents which are coordinated by the use of meta-controller mechanisms. The complex workflows can be broken into parts in this structure and also it promotes real time flexibility. The neuro-symbolic layer unites machine learning predictive analytics models with symbolic rule enforcement and compliance with a need to ensure flexibility and interpretability. Moreover, the blockchain and smart contract offer a level of trust, immutability, and auditability, which solve the essential issues of decentralized systems. The implementation is carried out on the microservices-based architecture with an event-driven communication channel, which allows workflow execution to be scaled and resilient. The experimental findings show that there are significant latency, throughput, and accuracy improvements with experimental techniques as opposed to the conventional techniques of orchestration. The system is also highly adaptable to the dynamic load and keeps within the predefined policies. On the whole, the study introduces an all-encompassing and intelligent orchestration paradigm that promotes efficiency, transparency, and reliability of decentralized financial ecosystems, which is the foundation of next-generation autonomous financial systems.
Oleksandr Ulichev, Oleksandr Dorenskyi, Victor Kulahin
The rapid advancement of technology and increasing market competition compel businesses to adapt swiftly by implementing new features and services to meet user demands. Traditional monolithic software architectures often hinder this agility due to challenges in scalability and maintenance. This article aims to analyze microservice architecture to solve these challenges, exploring its historical development, current trends, practical implementation aspects, and comparison with alternative architectural styles such as modulith architecture. The study examines the limitations of monolithic architectures in handling growing complexity and scaling requirements. It explores the emergence of microservice architecture, highlighting core characteristics like independent services, decentralized data management, and autonomous deployment. The evolution influenced by agile methodologies and DevOps practices is discussed. A comparative analysis with other architectural stylesâincluding monolithic, service-oriented, modular monolithic, and serverless architecturesâidentifies contexts where microservices are most beneficial. The research reviews essential tools and technologies for implementing microservices, such as Docker for containerization, Kubernetes for orchestration, and service meshes like Istio and Linkerd. Practical cases from industry leaders like Netflix and Amazon illustrate successful adoption and the challenges faced during implementation. Findings indicate that while microservice architecture offers significant scalability, flexibility, and rapid deployment advantages, it also introduces complexities related to distributed system management and security. The study emphasizes the importance of adopting best practices and standards, such as those promoted by the Cloud Native Computing Foundation and utilizing modern tools to mitigate these challenges. For organizations where full microservices adoption may be impractical, modulith architecture is a viable alternative that combines modularity with deployment simplicity. The article concludes that the choice of architecture should be carefully aligned with the project's specific needs, resources, and long-term strategic goals.
Root cause analysis (RCA) in Micro-services architecture (MSA) with escalating complexity encounters complex challenges in maintaining system stability and efficiency due to fault propagation and circular dependencies among nodes. Diverse root cause analysis faults require multi-agents with diverse expertise. To mitigate the hallucination problem of large language models (LLMs), we design blockchain-inspired voting to ensure the reliability of the analysis by using a decentralized decision-making process. To avoid non-terminating loops led by common circular dependency in MSA, we objectively limit steps and standardize task processing through Agent Workflow. We propose a pioneering framework, multi-Agent Blockchain-inspired Collaboration for root cause analysis in micro-services architecture (mABC), where multiple agents based on the powerful LLMs follow Agent Workflow and collaborate in blockchain-inspired voting. Specifically, seven specialized agents derived from Agent Workflow each provide valuable insights towards root cause analysis based on their expertise and the intrinsic software knowledge of LLMs collaborating within a decentralized chain. Our experiments on the AIOps challenge dataset and a newly created Train-Ticket dataset demonstrate superior performance in identifying root causes and generating effective resolutions. The ablation study further highlights Agent Workflow, multi-agent, and blockchain-inspired voting is crucial for achieving optimal performance. mABC offers a comprehensive automated root cause analysis and resolution in micro-services architecture and significantly improves the IT Operation domain. The code and dataset are in https://github.com/zwpride/mABC.
Capital markets post-trade processes (trade capture, clearing, settlement and reconciliation) are currently limited by excessive data fragmentation, reconciliation lag and high operational expenses. A central architecture creates "data silos" which restricts scalability, transparency and flexibility of integration between disparate financial institutions. This paper introduces a unified, technically advanced framework integrating Hyperledger Fabric (HLF); a permissioned Distributed Ledger Technology (DLT) with cloud native micro services as a means of creating a scalable, fault-tolerant and transparent ecosystem. By implementing Kubernetes based orchestration and Istio service mesh, we have shown how a legacy monolithic system can be replaced with a dynamic, distributed system capable of supporting high frequency transactions. Simulation results on large scale cloud based test beds show that our predictive resource orchestration framework achieves a 5 times greater throughput than a typical standalone DLT deployment and a 26-fold reduction in 95th percentile (p95) latency. The framework offers a scalable way of provisioning AI driven FinTech workloads with significantly increased reliability and decreased Total Cost of Ownership (TCO).
In the current digital landscape, almost everyone is on social media or various social media platforms. People use social media for a plethora of purposes, which include staying connected with friends and family, accessing information and updates about ongoing events, entertainment, networking with professionals, expressing themselves to a wide range of users, promoting businesses, joining online communities and engaging in various activities which has led to an increase in the consumption and usage of online social networks (OSN). One of the reasons for such a growth is their features such as ubiquitous access, on-demand service, friendship networks, user engagement strategies like recommendation engines, etc. However, there are various limitations to the current approach, such as the centralization of control, lack of data ownership, poor access control, fake news, bot accounts, censorship, digital rights management issues, etc. To address these limitations, a paradigm shift is necessary. This paper aims to develop a social media application where every post can be converted to a Non-Fungible Token (NFT) and be sold to earn money. Interplanetary File System (IPFS) is used as the decentralized storage. Algorithms for all the functionalities of the applications are given along with an algorithm for a reputation score for every user and their posts in social media are also proposed.
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Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
As blockchain smart contracts become more widespread and carry more valuable digital assets, they become an increasingly attractive target for attackers. Over the past few years, smart contracts have been subject to a plethora of devastating attacks, resulting in billions of dollars in financial losses. There has been a notable surge of research interest in identifying defects in smart contracts. However, existing smart contract fuzzing tools are still unsatisfactory. They struggle to screen out meaningful transaction sequences and specify critical inputs for each transaction. As a result, they can only trigger a limited range of contract states, making it difficult to unveil complicated vulnerabilities hidden in the deep state space. In this paper, we shed light on smart contract fuzzing by employing a sequence-aware mutation and seed mask guidance strategy. In particular, we first utilize data-flow-based feedback to determine transaction orders in a meaningful way and further introduce a sequence-aware mutation technique to explore deeper states. Thereafter, we design a mask-guided seed mutation strategy that biases the generated transaction inputs to hit target branches. In addition, we develop a dynamic-adaptive energy adjustment paradigm that balances the fuzzing resource allocation during a fuzzing campaign. We implement our designs into a new smart contract fuzzer named MuFuzz, and extensively evaluate it on three benchmarks. Empirical results demonstrate that MuFuzz outperforms existing tools in terms of both branch coverage and bug finding. Overall, MuFuzz achieves higher branch coverage than state-of-the-art fuzzers (up to 25%) and detects 30 % more bugs than existing bug detectors.
In blockchains, mempool controls transaction flow before consensus, denial of whose service hurts the health and security of blockchain networks. This paper presents MPFUZZ, the first mempool fuzzer to find asymmetric DoS bugs by exploring the space of symbolized mempool states and optimistically estimating the promisingness of an intermediate state in reaching bug oracles. Compared to the baseline blockchain fuzzers, MPFUZZ achieves a > 100x speedup in finding known DETER exploits. Running MPFUZZ on major Ethereum clients leads to discovering new mempool vulnerabilities, which exhibit a wide variety of sophisticated patterns, including stealthy mempool eviction and mempool locking. Rule-based mitigation schemes are proposed against all newly discovered vulnerabilities.
Filip Rezabek, Kilian Glas, Richard von Seck, Achraf Aroua · 6 authors
The recent developments and research in distributed ledger technologies and blockchain have contributed to the increasing adoption of distributed systems. To collect relevant insights into systems' behavior, we observe many evaluation frameworks focusing mainly on the system under test throughput. However, these frameworks often need more comprehensiveness and generality, particularly in adopting a distributed applications' cross-layer approach. This work analyses in detail the requirements for distributed systems assessment. We summarize these findings into a structured methodology and experimentation framework called METHODA. Our approach emphasizes setting up and assessing a broader spectrum of distributed systems and addresses a notable research gap. We showcase the effectiveness of the framework by evaluating four distinct systems and their interaction, leveraging a diverse set of eight carefully selected metrics and 12 essential parameters. Through experimentation and analysis we demonstrate the framework's capabilities to provide valuable insights across various use cases. For instance, we identify that a combination of Trusted Execution Environments with threshold signature scheme FROST introduces minimal overhead on the performance with average latency around \SI{40}{\ms}. We showcase an emulation of realistic systems behavior, e.g., Maximal Extractable Value is possible and could be used to further model such dynamics. The METHODA framework enables a deeper understanding of distributed systems and is a powerful tool for researchers and practitioners navigating the complex landscape of modern computing infrastructures.
Dan Zhuo, JiaâWei Tang, Yang Chen, Shaobin Wang · 5 authors
The InterPlanetary File System (IPFS) is a representative decentralized data storage system that has been widely used in recent years. IPFS plays an important role in the emerging Web3-related applications. As a global peer-to-peer system, a good understanding of IPFS nodesâ session time is meaningful. In this work, we introduce a measurement study to uncover the issues that are related to the session time of IPFS nodes. Based on the collected massive data of all online IPFS nodes for over one month, we gain a comprehensive understanding of the relationship between various node attributes and session time. In addition, we build a supervised machine learning-based model to predict the session time with a high prediction performance.
Emilio C. Piesciorovsky, Gary Hahn, Raymond Borges Hink, Aaron Werth · 5 authors
Electrical utilities continue to deploy more intelligent electronic devices (IEDs) inside and outside electrical substation and are associated with distributed energy resources (DERs). The integrity and confidentiality of data from IEDs is crucial, and distributed ledger technology (DLT) could improve the resilience of microgrids by helping to make these data more secure. The most popular applications using blockchain technology for electrical utilities is in the field is based on energy trading. However, the dynamism of the penetration of customer owned DERs and the deployment of sensors with IEDs have led to the identification of new applications using DLT that are focused on other areas, such as monitoring, operation and management of the grid and its assets. In addition, the majority of studies on electrical grid applications with blockchain were validated with software simulations. Although general monitoring of power systems for using DLT could be evaluated in operational electric grids, other DLT research applications such as defense against cyber-attacks and/or electrical fault detection are not likely to be performed in a real infrastructure because of possible risks to the network/equipment security. This report summarizes the application of power system applications using distributed ledger technology (DLT), providing a secure DLT framework for collecting data from IEDs like power meters and protective relays inside and outside of an electrical substation and/or between two different electrical utilities. In this study, the use case scenarios were created and assessed for different power system application by using DLT. The electrical fault detection for faulted phases (1), power quality monitoring of phase voltage magnitudes, frequency levels and load power factor (2), DERs use case monitoring (3), and cyber-event applications (4) were performed in a test bed with a Cyber-Grid Guard (CGG) system using DLT. It had a real-time simulator with power meters and protective relays in-the-loop. The first section of this report presents a literature review of power system applications using blockchain at research level. The second section shows the theory and equations used on this report. The third section shows the description of the test bed, equipment, architecture, and electrical grid diagrams. The fourth section shows the experimental models and use case scenarios that were performed for the electrical fault detection, power quality, DERs use case, and cyber event applications with the CGG system using DLT. The fifth section shows the results collected from the tests based on comparing the time stamped events of the analog signals from the IEDs, DLT computer and real time simulator. The sixth section performed the discussion of the results for the use case scenarios. Finally, section seven presents the conclusions for this report were presented.
Cezara DrÄgoi, Constantin Enea, Srinidhi Nagendra, Mandayam Srivas
Large-scale, fault-tolerant, distributed systems are the backbone for many critical software services. Since they must execute correctly in a possibly adversarial environment with arbitrary communication delays and failures, the underlying algorithms are intricate. In particular, achieving consistency and data retention relies on intricate consensus (state machine replication) protocols. Ensuring the reliability of implementations of such protocols remains a significant challenge because of the enormous number of exceptional conditions that may arise in production. We propose a methodology and a tool called Netrix for testing such implementations that aims to exploit programmer's knowledge to improve coverage, enables robust bug reproduction, and can be used in regression testing across different versions of an implementation. As evaluation, we apply our tool to a popular proof of stake blockchain protocol, Tendermint, which relies on a Byzantine consensus algorithm, a benign consensus algorithm, Raft, and BFT-Smart. We were able to identify 4 deviations of the Tendermint implementation from the protocol specification and check their absence on an updated implementation. Additionally, we were able to reproduce 4 previously known bugs in Raft.
Darja Ć mite, Nils Brede Moe, Marcin Floryan, Javier GonzĂĄlezâHuerta · 6 authors
While modern software companies strive to increase team autonomy to enable them to successfully operate the piece of software they develop and deploy, efficient ways to orchestrate the work of multiple autonomous teams working in parallel are still poorly understood. In this paper, we report how team autonomy is maintained at Spotify at scale, based on team retrospectives, interviews with team managers and archival analysis of corporate databases and work procedures. In particular, we describe how managerial authority is decentralized through various workgroups with collective authority, what compromises are made to team autonomy to ensure alignment and which team-related factors can further hinder autonomy. Our findings show that scaled autonomy at Spotify does not mean anarchy, or unlimited permissiveness. Instead, squads are expected to take responsibility for their work and coordinate, communicate and align their actions with others, and comply with a few enabling constraints. Further, squads take many decisions independently without management control or due to collective efforts that bypass formal boundary structures. Mechanisms and strategies that enable self-organization at Spotify are related to effective sharing of the codebase, achieving alignment, networking and knowledge sharing, and are described to guide other companies in their efforts to scale autonomy.
Architectural design contexts contain a set of factors that greatly influence software application development. Among them, organizational design contexts consist of high-level company concerns and how it is structured, for example, stakeholders and development schedules heavily impacting design considerations. The Decentralized Autonomous Organization (DAO), as a vital concept in the Web3 space, represents an organization constructed by automatically executed rules, such as via smart contracts, holding features of the permissionless committee, transparent proposals, and fair contribution by participated stakeholders. In this work, we conduct a systematic literature review of existing DAO literature to summarize its structural features, benefits and challenges, and potential development directions in the context of Web3 applications.
AbstractâAs fully autonomous ride-hailing services continue to scale, occupant-facing microservices have emerged as a linch- pin for multi-rider resource allocation, real-time commerce, and occupant privacy. In previous frameworks, occupant concurrency engines leveraged ephemeral occupant data and aggregator-based telepresence for conflict resolution, yet trust and transparency of occupant identity remain underexplored. This paper proposes a blockchain-powered approach to occupant identity, enabling de- centralized authentication, payment, and data-sharing flows that are impervious to single-point failures. By registering occupant profiles on a distributed ledger, occupant concurrency logic can verify ride privileges, cost splits, and occupant-lingual disclaimers without storing sensitive occupant data in a central aggregator. We describe how occupant concurrency gates, occupant seat usage, ephemeral occupant camera frames, and aggregator route expansions, while deferring occupant identity and micropayment transactions to a blockchain-based ledger. This method unifies occupant-lingual disclaimers with cryptographic wallet checks, ensuring the occupant sees a transparent log of e-commerce or route negotiations. Preliminary simulation results suggest that decentralized occupant identity reduces aggregator overhead by 25%, while occupant-lingual disclaimers adoption remains high due to ephemeral occupant data policies. We further demonstrate how partial offline fallback can cache occupant ledger proofs, re- syncing only hashed occupant usage logs upon coverage reestablishment. By designing occupant concurrency logic around distributed identity, occupant-lingual expansionsâlike seat reassignments or multi-tenant microservicesâachieve global security invariants across multiple brands, fleets, or regional operators. This paper marks a critical step in bridging occupant concur- rency with blockchain and distributed identity, heralding a future of trust-minimized occupant microservices for the driverless era. KeywordsâBlockchain, Distributed Identity, Occupant Con- currency, Ephemeral Data, Occupant-Lingual Disclaimers, Autonomous Ride-Hailing
Abstract Business Processes (BPs) are commonly used by organizations to describe their goals. However, the existent decentralization found in many organizations forces them to build such BPs by coordinating distributed and fragmented BPs. Within this context, microservices arise as a very interesting and convenient way to address the implementation of such processes due to their low coupling characteristic. In this case, the coordination of such fragmented BPs is usually achieved by means of event-based choreographies. One of the main challenges to be faced by choreographies is their evolution due to the complexity that introduces the need of integrating changes among autonomous and independent partners. We face the challenge of evolving a microservice composition that is globally defined in a BPMN model but executed through a choreography of BPMN fragments. We introduce a protocol to manage the propagation of a change done by one microservice to be integrated into both the BPMN fragments of the rest of the microservices and the global BPMN model. This protocol also supports the negotiation among participants and the automatic suggestion of model adaptations to maintain the functional integrity of the composition. These suggestions are supported by a catalogue of adaptation rules that precisely characterize every possible change and propose actions to be considered by the affected microservices. All the evolution process is done at the modelling level, without managing hard-coded implementations. We have developed specific tools to facilitate the practical adoption of this protocol, and we have validated our work in an experiment with users. We can conclude that the proposed approach is effective to evolve microservice compositions implemented as event-based choreography of BPMN fragments from the local perspective of one partner.
With the deepening application of blockchain technology, exaggerating its empowering effects has become common. In recent years, the rational assessment of the maturity of blockchain technology applications in digital projects in different fields has been the focus of attention and identified as the key to improving the implementation effect of various digital projects. Although some studies have obtained substantial research results on technology maturity and its derivative applications, which can be used to predict the overall trend of a technology or guide the implementation of the technology on the ground, few studies have evaluated the maturity of blockchain technology in combination with different application scenarios. Our study combines application scenarios and the technical characteristics of blockchain technology and proposes an evaluation system for blockchain technology application maturity consisting of five primary indicators, that is, key application requirements, data security, process complexity, application ecological completeness, and technical performance requirements, and their corresponding secondary indicators. In addition, we take digital government public service projects as application scenarios and use the analytic hierarchy process (AHP) entropy method and expert scoring method to determine the weights corresponding to each index in the assessment system and construct a blockchain technology application maturity assessment model. Moreover, we apply the model to ten typical digital government public service projects to conduct a comprehensive assessment and analysis. By comparing the indicator scores of the different projects, we analyze the project characteristics influencing blockchain technology application maturity and provide suggestions for applying âblockchain + digital government public servicesâ.
Abstract: Software testing is part of a set of activities that ensure high-quality software. It primarily aims at revealing defects that have been inserted into software at various stages of its development. In functional testing, test requirements are derived from software specifications. This paper proposes a functional testing/ evaluation that was performed using the functional specification provided during a Model for Creating a Stable Cryptocurrency Using Fiat Currency for Global Electronic Commerce design and verifies the model against the functional requirements. The functional approach of model effectiveness was used to establish model functions first, and then to build criterion measures to assess how well the objectives were met. The functional requirements of the evaluation model were then transformed into test cases. Furthermore, the test case reports were presented as final findings.
Learning heterogeneous graphs consisting of different types of nodes and edges enhances the results of homogeneous graph techniques. An interesting example of such graphs is control-flow graphs representing possible software code execution flows. As such graphs represent more semantic information of code, developing techniques and tools for such graphs can be highly beneficial for detecting vulnerabilities in software for its reliability. However, existing heterogeneous graph techniques are still insufficient in handling complex graphs where the number of different types of nodes and edges is large and variable. This paper concentrates on the Ethereum smart contracts as a sample of software codes represented by heterogeneous contract graphs built upon both control-flow graphs and call graphs containing different types of nodes and links. We propose MANDO, a new heterogeneous graph representation to learn such heterogeneous contract graphsâ structures. MANDO extracts customized meta-paths, which compose relational connections between different types of nodes and their neighbors. Moreover, it develops a multi-metapath heterogeneous graph attention network to learn multi-level embeddings of different types of nodes and their metapaths in the heterogeneous contract graphs, which can capture the code semantics of smart contracts more accurately and facilitate both fine-grained line-level and coarse-grained contract-level vulnerability detection. Our extensive evaluation of large smart contract datasets shows that MANDO improves the vulnerability detection results of other techniques at the coarse-grained contract level. More importantly, it is the first learning-based approach capable of identifying vulnerabilities at the fine-grained line-level, and significantly improves the traditional code analysis-based vulnerability detection approaches by 11.35% to 70.81% in terms of F1-score.
A smart contract is a pivotal notion in blockchain technology. Distributed applications contain smart contracts verifying the fulfillment of the conditions, which determine the execution of transactions between the blockchain network nodes. Those software-controlled logical conditions are called verification rules. As the number of conditions increases, the complexity of smart contract testing rapidly grows. This paper aims to propose a smart contract testing pattern that significantly limits the needed number of test cases. For evaluation expression with four verification rules, the pattern usage reduces the number of test cases by 68.75% in relation to the full coverage of logical value combinations. With the increase in the number of logical conditions, not only the number of test cases but also their percentage decreases. Starting from seven verification rules in the evaluation expression, the percentage reduction of test cases exceeds 90%. As a result, the cost of preparing and maintaining test case suites may be substantially cut. It should be emphasized that test execution time can be reduced even by 3 orders of magnitude (from seconds to milliseconds). Such an approach is highly important for regression testing, especially when used in continuous software integration, delivery, and deployment approaches.
Alex Groce, Kush Jain, Rijnard van Tonder, Goutamkumar Tulajappa Kalburgi · 5 authors
Bitcoin is one of the most prominent distributed software systems in the world. This paper describes an effort to investigate and enhance the effectiveness of the Bitcoin Core fuzzing effort. The effort initially began as a query about how to escape saturation in the fuzzing effort, but developed into a more general exploration. This paper summarizes the outcomes of a two-week focused effort. While the effort found no smoking guns indicating major test/fuzz weaknesses, it produced a large number of additional fuzz corpus entries, increased the set of fuzzers used for Bitcoin Core, and ran mutation analysis of Bitcoin Core fuzz targets, with a comparison to Bitcoin functional tests and other cryptocurrenciesâ tests. Our conclusion is that for high quality fuzzing efforts, improvements to the oracle may be the best way to get more out of fuzzing.
Seyed Hossein Haeri, Peter Thompson, Neil Davies, Peter Van Roy · 6 authors
This paper directly addresses a long-standing issue that affects the development of many complex distributed software systems: how to establish quickly, cheaply, and reliably whether they can deliver their intended performance before expending significant time, effort, and money on detailed design and implementation. We describe ÎQSD, a novel metrics-based and quality-centric paradigm that uses formalised outcome diagrams to explore the performance consequences of design decisions, as a performance blueprint of the system. The distinctive feature of outcome diagrams is that they capture the essential observational properties of the system, independent of the details of system structure and behaviour. The ÎQSD paradigm derives bounds on performance expressed as probability distributions encompassing all possible executions of the system. The ÎQSD paradigm is both effective and generic: it allows values from various sources to be combined in a rigorous way so that approximate results can be obtained quickly and subsequently refined. ÎQSD has been successfully used by a small team in Predictable Network Solutions for consultancy on large-scale applications in a number of industries, including telecommunications, avionics, and space and defence, resulting in cumulative savings worth billions of US dollars. The paper outlines the ÎQSD paradigm, describes its formal underpinnings, and illustrates its use via a topical real-world example taken from the blockchain/cryptocurrency domain. ÎQSD has supported the development of an industry-leading proof-of-stake blockchain implementation that reliably and consistently delivers blocks of up to 80 kB every 20 s on average across a globally distributed network of collaborating block-producing nodes operating on the public internet.