Payroll and compensation backends represent some of the most legally sensitive and financially consequential components of enterprise software systems. Traditional implementations often rely on mutable database records that overwrite prior state, complicating auditability, replay safety, and regulatory compliance. In cloud-native, distributed environments, mutable state models further amplify risks related to concurrency, partial failures, and inconsistent recovery. This paper proposes an immutable ledger-based modeling approach for payroll and compensation backends deployed in cloud-native architectures. By treating every compensation-relevant change as an append-only, versioned ledger entry, the system achieves deterministic state reconstruction, strong audit traceability, and resilience under distributed execution. The study examines canonical ledger design, event-sourced architectures, retroactive correction handling, concurrency isolation, and cross-entity coordination within compensation workflows. It also analyzes partitioning strategies, operational resilience, and anti-patterns associated with mutable payroll systems. The resulting framework demonstrates how immutable modeling principles—when combined with identity-scoped partitioning and cloud-native scalability patterns—enable high-integrity financial backend systems that remain deterministic, replay-safe, and regulatorily compliant under high concurrency and infrastructure variability.
Cloud computing is popular in modern businesses because it is scalable, versatile, and cost-effective. Due to the spread and complexity of cloud infrastructures, fault tolerance is crucial. This research focuses on improved cloud failure tolerance using Distributed Ledger Technology (DLT). Decentralised and immutable DLT offers verifiable and transparent transaction history, data consistency, and decentralised recovery, promising to improve cloud infrastructure stability and robustness. This study examines how DLT improves cloud-based detection, recovery, and fault tolerance. Using blockchain and cloud services to create a fault-tolerant system is novel. Comparison of fault tolerance solutions in existing cloud settings with DLT integration is the plan. Data was obtained during failure testing simulations using genuine cloud infrastructure platforms and blockchain networks. Recovery time, defect detection, energy utilisation, and uptime were measured. Graphviz was used to create flowcharts, and Matplotlib was utilised for graphs and plots. Test results show improved fault tolerance due to lower fault impact and faster recovery from DLT. DLT can make cloud systems more fault-tolerant and resilient, setting the pace for distributed computing innovation, the research concludes.
We analyze developer activity across 10 major Ethereum repositories (totaling 129884 commits, 40550 issues) spanning 10 years to examine how events such as technical upgrades, market events, and community decisions impact development. Through statistical, survival, and network analyses, we find that technical events prompt increased activity before the event, followed by reduced commit rates afterwards, whereas market events lead to more reactive development. Core infrastructure repositories like Go-Ethereum exhibit faster issue resolution compared to developer tools, and technical events enhance core team collaboration. Our findings show how different types of events shape development dynamics, offering insights for project managers and developers in maintaining development momentum through major transitions. This work contributes to understanding the resilience of development communities and their adaptation to ecosystem changes.
Testing in the software development process is one of the crucial disciplines. The immutability of deployed smart contracts makes tests particularly important in their design and implementation. The objective is to minimize the costs of smart contract testing by reducing the number of test cases. The article presents the test suite reduction method for smart contracts, which exploits the knowledge of their design manner. In particular, the design of a mechanism for evaluating the list of verification rules controlling the execution of transactions was used. The proposed method defines a minimum set of test cases for smart contracts designed according to a specific design pattern. The test case reduction level was shown using analytical formulas enclosed in the paper. Compared to full coverage, the method reduces over 96% of test cases for a smart contract with at least eight verification rules. The performance tests were conducted and the analysis of the results was enclosed as well. Both the smart contract evaluation time and the test suite execution time are less than 0.01 of a millisecond. Besides, the method reduces the number of test data for smart contracts by over 79%. Therefore, the method can be especially useful in continuous software integration and delivery.
Advanced cloud automation architectures establish the foundation for transformative humanitarian aid operations through resilient, scalable infrastructure deployments. This technical transformation utilizes containerized microservices, distributed edge computing nodes, and event-driven patterns that maintain operational integrity despite connectivity challenges characteristic of disaster environments. Distributed ledger integration with standardized API frameworks facilitates secure tracking across heterogeneous systems, while multi-cloud analytics platforms with machine learning capabilities enable proactive disaster management. These systems demonstrate exceptional performance metrics in real-world implementations, including significant reductions in processing latency, dramatic improvements in forecast accuracy, and unprecedented resource tracking transparency. The widespread adoption of these technologies represents a fundamental shift from traditional emergency response toward sophisticated, automated solutions that dynamically adapt to changing conditions. As edge computing, quantum optimization, and adaptive machine learning frameworks continue to mature, the humanitarian sector finds itself at the leading edge of distributed systems innovation, deploying complex cloud architectures that establish new benchmarks for performance and reliability under extreme conditions.
Microservices commonly rely on bearer tokens such as JSON Web Tokens (JWTs). Although fast, these tokens reveal user claims to every service and can be reused if stolen. This thesis designs and evaluates a privacy-preserving alternative based on zero-knowledge proofs (ZKPs) that verifies each request under a zero-trust model without disclosing identity attributes. The authentication prototype service is built with two implementations (TypeScript/NestJS and Rust) and compared with a JWT baseline in privacy, access control correctness, and performance. Across six concurrency levels (1, 10, 15, 20, 25, 30) over 15-second runs, CPU and memory usage were sampled every 250 ms, and means were reported across the concurrency points. The workflow achieves zero attribute disclosure while keeping verification practical: verification adds 1.8 ms per microservice hop with a native Rust verifier and ∼130 ms with a NestJS verifier. Proof generation is the main computational cost (∼0.7s in NestJS; ∼1.4s in Rust), with NestJS demonstrating superior performance due to efficient WASM witness generation and reuse of artefacts. These measurements were taken on the same host with an otherwise idle machine to control variance. Formal statistical significance would require multiple independent repetitions; the present analysis reports central tendencies and observed stability. Contributions are: (i) a working ZKP-based authentication workflow suitable for microservices, (ii) a dual-stack prototype and comparative evaluation against JWT, and (iii) evidence that privacy can be improved without prohibitive verification overhead.
Illicit transaction detection on blockchain networks presents a critical challenge due to the pseudonymous, decentralized, and high-volume nature of decentralized finance (DeFi) ecosystems. Traditional machine learning models struggle to effectively capture the temporal dynamics and irregular patterns of illicit behavior, while graph-based methods often incur high computational costs and rely on static relational structures. In this paper, we propose a novel dual-attention framework—GAM-CoT Transformer—for robust transaction-level anomaly detection. The proposed model integrates two key components: a Global Attention Module (GAM) that adaptively reweights feature channels and temporal steps to emphasize salient patterns, and a Contextual Transformer (CoT) block that efficiently models short-range dependencies using grouped convolutions instead of full self-attention. This design enables the model to simultaneously achieve computational efficiency, temporal expressiveness, and improved detection sensitivity. We evaluate our approach on a real-world blockchain transaction dataset and demonstrate its superiority over conventional classifiers including Random Forest, XGBoost, and LSTM-based models. The GAM-CoT Transformer achieves higher recall and F1 scores, particularly for the minority illicit class, while maintaining fast convergence and deployment scalability. Our method offers a practical and effective solution for enhancing the security of blockchain systems through intelligent transaction behavior modeling.
Zubaida Rehman, Iqbal Gondal, Hai Dong, Mengmeng Ge · 6 authors
Eclipse attacks, which isolate victim nodes by monopolizing their peer connections, remain a critical threat to Ethereum’s consensus mechanism. To address this, we present a principled framework for detecting Eclipse attacks in Ethereum peer-to-peer networks, grounded in a formal adversarial model. Existing defenses are either ad-hoc or lack provable guarantees, leaving open questions about their reliability under adaptive adversaries. Our work aims to bridge this gap by formally defining eclipse attack detection as a security property. We specify soundness, completeness, and robustness theorems under bounded adversarial drift, and derive formal guarantees within false positive and false negative bounds, resilience to adversarial manipulation, and multi-node compositional reliability. We then instantiate a lightweight detection framework that maps packet-level traffic features to predictions using ensemble classifiers (Random Forest, XGBoost). The system was validated using a controlled Ethereum testbed and extended with CTGAN-generated synthetic traces to emulate networks of up to 100 nodes. Empirical evaluation shows that our framework achieves up to 96% F1-score with sub-second inference latency, well within Ethereum’s 12-second Proof-of-Stake validator time slots. These findings demonstrate that lightweight statistical features, when coupled with formal analysis, enable accurate, efficient, and scalable detection of network-level partitioning attacks. Our work establishes a deployable and theoretically grounded defense foundation for securing modern blockchain systems against eclipse adversaries.
Blockchain technology, at its core, promises decentralization, transparency, and self-sovereignty. Interacting with a blockchain network involves technical procedures that require specific knowledge, hardware, and ongoing maintenance. This is where providers come in. Providers form the essential infrastructure layer that connects decentralized networks with the users, applications, and developers that rely on them. They are the unsung heroes of the Web3 movement, quietly handling the complex backend operations that enable seamless blockchain interactions.
This study analyzes the impact of validator behavior on investor rewards in proof-of-stake (PoS) based blockchain networks and proposes a visualization system to assist investors in selecting appropriate validators. This system enables personalized evaluations through five adjustable indicators tailored to the investor's preferences. By utilizing similarity-based circular visualizations and radar charts, it facilitates the selection and comparison of validators. Additionally, it provides time-series data-based line graphs and raw data-based table views to support detailed comparative analysis among validators. The introduction of such a multifaceted evaluation methodology in staking investments is expected to contribute to the formation of user-customized portfolios and the establishment of optimized investment strategies.
Web3 describes an idea for the next evolution of the Internet, where blockchain technology enables the Internet of Value. As Web3 software, decentralized applications (DApps) have emerged in recent years. There exists a natural link between DApps and cryptocurrencies, where faults in DApps could directly lead to monetary losses associated with cryptocurrencies. Hence, efficient fault localization technology is of paramount importance for urgent DApp rescue operations and the mitigation of financial losses. However, fault localization methods applied in traditional applications are not well-suited for this specific field, due to their inability to identify DApp-specific fault features, e.g., a substantial amount of cryptocurrency is transferred from DApps to hackers. In order to explore the root cause of DApp faults, some researchers try to identify suspicious code snippets through mutation testing. Nonetheless, applying mutation testing for DApp fault localization is time-consuming and thus limited in practice. This paper conducts the first comprehensive study of DApp fault localization. We introduce DAppFL, a learning-based DApp fault localization tool that performs reverse engineering to gather executed source code and then trace cryptocurrency flow to assist in locating faulty functions. We also present the inaugural dataset for DApp fault localization, providing a new benchmark for this domain.Our experimental results demonstrate that DAppFL locates 63% of faults within the Top-5, 23% more than the state-of-the-art method. To facilitate further research, our code and dataset are freely available online: https://github.com/xplanet-sysu/awesome-works#dappfl.
Runtime Verification (RV) of smart contracts ensures the absence of exploitations within a transaction during execution. It is a crucial security aspect that is often omitted due to its high onchain overhead. The lack of RV in public blockchains allowed attackers to compromise vulnerable contracts and cause significant monetary losses. Existing runtime protection solutions fail to address the onchain overhead limitation, which may hinder their deployment and undermine their effectiveness. To address this problem, we propose SRP (Smart contract Runtime Protection), an efficient framework that integrates an off-chain mechanism with on-chain contract execution to minimize the burden of RV. Our hybrid architecture aims to protect deployed smart contracts from real-time attacks while maintaining blockchain scalability. We first present SRP from a design perspective proposing a protocol customized for off-chain RV interoperability. Then, we evaluate our approach empirically and demonstrate the applicability of SRP using a proof-of-concept implementation on a local Ethereum network. We further generalize our evaluation results using a queue-theoretic approach. We present a discrete-time queuing network model of SRP and the block creation process. The model is then used to conduct an extensive data-driven parametric evaluation of SRP compared to onchain-only RV. Our empirical, experimental, and quantitative results indicate the feasibility and efficiency of our approach, where SRP outperforms the onchain-only mechanism in terms of service time and throughput, for increasing workloads. Lastly, we introduce a game-theoretic incentive mechanism enhancing the robustness and security of SRP. We analyze the security of SRP and demonstrate that the presence of at least one honest validator serves as a deterrent to RV forging attacks.
In the realm of Decentralized Finance (DeFi), this manuscript introduces a Hybrid Cross-Chain Model. As DeFi architectures grapple with the complexities of monolithic single-chain platforms, our proposed model orchestrates a symphony of multiple chains to facilitate seamless cross-chain communication, offering a poised solution to scalability and transaction speed challenges. Incorporating modeling effects and simulations, our rigorous performance evaluation underscores the model's excellence and includes an in-depth analysis of its performance, particularly focusing on robust security measures. The model is positioned as a cornerstone in an interconnected DeFi landscape by emphasizing stringent measures to ensure data integrity and uphold consensus mechanisms. User-centric enhancements promise swift transaction confirmations and reduced fees, improving the overall experience. The abstract culminates with a comparative analysis, positioning the Hybrid Cross-Chain Model as an innovative solution with profound implications for the future of DeFi. This manuscript advocates for ongoing research and development, heralding a new era of sophistication and resilience in decentralized finance.
Abstract: In the ever-evolving landscape of distributed systems, ensuring safety, efficiency, and scalability remains a paramount challenge. BlockEdge emerges as a pioneering framework designed to address these critical issues by leveraging the principles of blockchain technology and advanced consensus mechanisms. This abstract outlines the key features, innovations, and potential impacts of BlockEdge on the realm of distributed computing. BlockEdge integrates blockchain's immutable ledger properties with a novel consensus algorithm tailored for distributed systems. Unlike traditional blockchain applications that prioritize decentralization for financial transactions, BlockEdge focuses on enhancing the performance and reliability of distributed applications. The framework employs a hybrid consensus model that combines Byzantine Fault Tolerance (BFT) with Proof-of-Stake (PoS), optimizing both security and energy efficiency. A standout feature of BlockEdge is its modular architecture, which allows seamless interoperability between different types of distributed networks. This modularity facilitates the integration of various consensus protocols, catering to the specific needs of diverse applications, from IoT networks to large-scale data processing systems. By enabling secure and efficient cross-chain communication, BlockEdge effectively mitigates the silo effect prevalent in current distributed system designs.
U ovom radu su istraženi mehanizmi koji osiguravaju i decentraliziraju blok-lanac mreže čiji se konsenzus postiže mehanizmom dokaza o zalogu (engl. Proof of Stake). Dan je pregled različitih načina na koji korisnici mogu sudjelovati validaciji mreže. Predstav- ljene su prednosti za korisnike koji sudjeluju, no i mane koje se pojavljuju prisutnošću većeg broja transakcija na mreži u jedinici vremena. Objašnjen je predložen pristup koji povezuje tehnologije skaliranja blok-lanac mreža i validacije uz analizu rezultata i pri- jedloge poboljšanja te decentralizirane aplikacije.
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 won’t 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%.
Обсяг роботи 64 сторінки, 11 ілюстрацій, 3 таблиці, 1 додаток, 24 джерел літератури. Об’єктом дослідження є безпека смарт-контрактів у мережі Ethereum від зламу та Предметом дослідження є модифікація смарт-контрактів в мережі Ethereum та підвищення безпеки від шахрайства при роботі з ними. Методи дослідження – поєдання наявних, та минулих реалізацій та стандартів токенів у мережі Ethereum. Метою роботи є розгляд та підвищення рівня захищеності смарт-контрактів від шахрайських дій шляхом безпечного пакетування викликів смарт-контрактів. Результати роботи можуть бути використані для майбутніх оновлень мережі Ethereum, або для побудови різноманітних децентралізованих застосунків на основі смарт-контрактів.
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 MonitorChain’s capability to provide critical insights into blockchain applications’ 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 company’s 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 styles—including monolithic, service-oriented, modular monolithic, and serverless architectures—identifies 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.