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
Puwei Wang, Haoran Li, Hang Fu, Zhouxing Sun · 6 authors
In edge computing, applications are usually delivered as services, each of which runs independently and cooperates to construct complicated applications. QoS (Quality of Service) monitoring is an important way to detect and locate faulty services. In a decentralized environment, QoS monitoring will face trust problem because it is difficult to guarantee the trustworthiness of monitoring results. This article builds a blockchain system for QoS monitoring. However, there are two challenges. First, although the blockchain consensus ensures the consistency of on-chain data among nodes, there is no guarantee that the monitoring data collected in the decentralized environment are authentic, because malicious nodes may report falsified data. Second, in order to handle service faults in time, the real-time query is usually required for obtaining monitoring data. But, blockchains suffer from inefficient querying, because the sequential data storage of blockchain is designed for write intensive applications at the expense of some read performance. To address these challenges, this article proposes a clustering-based algorithm for validating the authenticity of monitoring data collected in the decentralized environment, and proposes a probabilistic threshold query over blockchain, which supports efficient querying and guarantees the probability that the query results are correct is not less than a given threshold. This article implements the proposed blockchain system based on the blockchain platformHyperledger Fabricand the edge computing platformKubeEdge. The experiment results demonstrate the proposed blockchain system provides high-throughput and low-latency monitoring ability, and can efficiently obtain monitoring results close to real QoS data.
Smart contracts as one of the most successful applications of blockchain. It holds digital currency with huge economic value. During the rapid development of smart contracts, vulnerabilities in the contracts have caused huge financial losses to the blockchain. This has strengthened researchers’ focus on smart contract security vulnerability detection. In this paper, we explore a vulnerability detection deep neural network-based method on combining features in both contract source code and bytecode forms. We conduct extensive experiments on the Ethereum smart contract datasets for reentrancy vulnerability. The experiment demonstrates that our method achieves 87% accuracy and 78% f1-score. Another experiment shows that our method maintains a good detection performance even when the feature part is missing.
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.
The international railway logistics management system with a centralized system has problems such as information security, difficulty tracking logistics information, complex operating procedures, and information sharing and exchange. However, the decentralized, tamper-proof, and traceable features of blockchain technology can effectively enhance the data security and credibility of the system. Nonetheless, the development of smart contracts based on blockchain lacks a disciplined, standardized, and mature development process. On the other hand, adopting a Model-Driven Architecture (MDA) development approach can reduce human coding errors and improve development efficiency. This study proposed a modeling method based on UML class diagrams and state machine diagrams to analyze the static structure and dynamic behavior of the smart contract in the international railway logistics management system. By developing a conversion algorithm and combining the source and target metamodels, the platform-independent model (PIM) was converted into a platform-specific model (PSM), which was eventually transformed into code using a model-to-code transformation algorithm. The feasibility and correctness of this method were validated through experiments.
Dan Zhuo, Jia‐Wei 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 nodes’ 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.
With the rise of Artificial Intelligence and the autonomous digital ecosystem, the M2M financial systems that can make transactions without human intervention have been developed rapidly. AASI helps intelligent agents, IoT devices, and AI applications to facilitate efficient digital payments exchange in real-time. In the decentralized finance sector, smart commerce, cloud computing, and automated services, these systems are being more and more adopted. But keeping a high transaction efficiency, low transaction execution delay, and ensuring transaction consistency and reliability across different workloads is still difficult. This study analyzes the operational performance of the autonomous payment infrastructure in AI systems by calculating transaction throughput, transaction execution latency, and transaction execution consistency in machine to machine finance systems. The study employs the PaySim financial transaction dataset, in conjunction with automated rule-based financial transaction software agents, to emulate autonomous financial transactions. Fixed periods of machine-to-machine transactions are initiated between the intelligent agents, forming a credible autonomous financial system. Throughput of the research is the number of completed transactions per time unit and execution latency is the time elapsed between the initiation and completion of a transaction. The ratio of successful transaction executions in comparison with failed transaction executions is used to evaluate execution consistency. The experimental analysis is done for low, medium and high transaction loads to study the behavior of the infrastructure and its scalability under different loads. The experimental results confirm the efficiency of the autonomous payment infrastructure with moderate workloads, in particular, high throughput and low latency. If the volume of transactions increases dramatically, however, it impacts on the performance of the system in a negative way, by adding to processing delay and also decreasing execution consistency. As the workload increases, the number of transactions increases, the likelihood of error rises, and transaction congestion and computational bottlenecks occur. The results of the research enable us to practically define the assessment criteria of autonomous financial infrastructures based on measurable criteria. This research helps propel the growth of AI systems to enable the design of scalable, reliable and efficient autonomous payment systems for future machine-to-machine economies.
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.
Block chain technology, which evolved as a distributed system preserving a public ledger through consensus protocol, is showing great promise in many different contexts, including the supply chain, the financial sector, the internet of things (IoT), and so on. Important in the development of the block chain infrastructure is the use of consensus mechanisms, and one such mechanism is Byzantine Fault Tolerance (BFT) protocols. However, the vast majority of BFT protocols are inherently unable to be used in realistic settings due to their lack of dynamic features (i.e., nodes can't join or leave a functioning system) and the inability to penalize unsuccessful nodes. In this paper, we provide the Dynamic Enhanced BFT (DEBFT) protocol for handling dynamic properties and faulty node penalties. DEBFT, which is based on Honey Badger BFT, uses Dynamic Threshold Identity-based Encryption and Distributed Key Generation to facilitate consensus group changes without requiring a complete system reconfiguration. Additionally, assessment metrics are provided to evaluate consensus nodes and remove problematic nodes.
Cezara Drăgoi, 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.
A smart contract is a self-executing program that is stored on the blockchain and runs when predetermined conditions are satisfied. Many frequent transactions involving asset transfers rely on smart contracts deployed on the blockchain, making them highly vulnerable to attack, thus it is essential to ensure the security of smart contracts. Since the smart contract is immutable once deployed, developers must try their best to fix existing vulnerabilities in advance to ensure security. Current approaches for automatic program repair on the smart contracts have mainly adopted the heuristic search algorithms or defined patterns to fix several well-defined types of vulnerabilities. They can only provide security code recommendations for developers in specific scenarios. We explore more general automated program repair of smart contracts in software history.To pave the way for studying code changes related to bug fix of smart contracts in software history, we present a labeled public dataset for method-level program repair task, containing over 12 typical insecure code patterns. Unlike bugs in traditional software, the vulnerabilities of smart contracts are more associated with access control and conditional statements as smart contracts pertain to financial assets. For this problem, we devise a novel double-encoder network and use a code representation designed for the smart contract based on syntax information to repair program. By implementing and evaluating our approach on new dataset comprised of over 10,000 program pairs, we demonstrate the superiority of our approach in both qualitative and quantitative aspects.
Darja Šmite, Nils Brede Moe, Marcin Floryan, Javier González‐Huerta · 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.
The 5th generation mobile network (5G) is designed with a new core architecture that makes it quite extensible. The components of the 5G core architecture are no longer physical standalone devices, but rather software processes run on commercial off-the-shelf (COTS) servers. The backbone of 5G is software-defined networking (SDN) and network function virtualization (NFV), and they both bring unprecedented flexibility to network and resource management. In this context, 5G logical networks can be created by partitioning a shared physical infrastructure, and each network can be customized and optimized for specific entity. This concept is known as 5G network slicing. Despite the tremendous benefits of network slicing, it also brings many unprecedented security challenges because of the dynamism and diversity of slice's structure. Therefore, establishing trust in the 5G ecosystem is a cornerstone for global adaptation and tackling security and privacy risks. In this paper, we focus on the trust aspect between the network slice stakeholders (i.e slice owners, users, slice resource providers, and service providers), and we propose a blockchain-based zero trust model that addresses threat models that are based on the lack of trust between the entities in a network slice. Our approach for zero trust modeling and quantification is based on direct evidence and indirect evidence and the use of smart contracts with blockchain to maintain the required trust values at runtime. We provide details on how to model and quantify the trust of all the stakeholders of a given network slice and how the blockchain smart contract can enforce the zero-trust requirements for all network slice stakeholders.
Abstract—As 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 expansions—like seat reassignments or multi-tenant microservices—achieve 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. Keywords—Blockchain, 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 “blockchain + digital government public services”.
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.