Blockchain Papers

Fudong Wu, Qianhong Wu, Jia-Ju Bai, Bo Qin · 6 authors

In Ethereum, DevP2P is the fundamental network-layer protocol set that supports consensus mechanisms, transaction propagation and smart contract execution. Due to the importance of DevP2P, its bugs can be exploited by the attacker to cause security problems like denial of service, leading to property loss on Ethereum. However, existing blockchain testing approaches focus on the bug detection of consensus and application layers, causing many serious DevP2P bugs to be missed. In fact, detecting DevP2P bugs has some key challenges, including how to generate effective inputs and how to detect complex bugs. This paper designs D2PFuzz, the first network-layer differential fuzzing approach of bug detection for Ethereum. It consists of two key techniques: (1) aquery-based fuzzing strategythat dynamically generates valid DevP2P messages according to network, chain and node state changes; and (2) amulti-node differential checking methodthat identifies important differences of DevP2P response messages from multiple nodes in the same blockchain to detect semantic bugs. We have evaluated D2PFuzz on five open-source and popular Ethereum node implementations, including Geth, Erigon, Reth, Besu and Nethermind. D2PFuzz in total finds 15 unique bugs, 12 of which are previously unknown. Compared to two state-of-the-art blockchain testing approaches including LOKI and Hive, D2PFuzz improves testing coverage by 3.7x and 21.6x, respectively, and finds 13 bugs missed by these approaches.

Oliver Alexy, Oliver Baumann, Ying-Ying Hsieh, Giorgia Sampó

Decentralized Autonomous Organizations (DAOs) represent a radical form of socio-technical systems, where rules are enforced by code and governance is conducted by a distributed network of stakeholders. A critical challenge in designing these systems is achieving consensus without centralized authority, yet how consensus ensures effective governance remains underexplored. This study investigates the design of DAO governance systems, utilizing data from 70 DAOs and applying Fuzzy Set Qualitative Comparative Analysis (fsQCA) to explore which consensus configurations lead to positive organizational outcomes. Our analysis challenges the notion of a single consensus model. Instead, we uncover 13 distinct configurations that characterize successful DAOs. Our key finding reveals a fundamental “ideation-legitimation trade-off”: successful DAOs optimize for broad participation in either the proposal (ideation) stage or the voting (legitimation) stage, but rarely both. These insights provide a nuanced framework for understanding and designing effective governance systems for DAOs.

Carlos Melo, José Miqueias, Johnnatan Messias, Glauber Gonçalves · 7 authors

No abstract is available for this record.

Miguel Valido

Smart contracts are high-stakes software: their immutable, publicly accessible code may govern assets worth millions. This means that even minor defects can have severe consequences. Unit tests often miss edge cases. Although formal verification is the only route to full correctness, it demands substantial time and expertise. Property-based testing bridges this gap by exploring large input spaces and shrinking failures to minimal counterexamples. Used early, it filters defects and prioritises verification effort on code already validated by tests. Although Solidity benefits from a mature testing ecosystem, comparable support for other languages such as Daml remains limited. This dissertation addresses this gap by introducing Hypothesis2Daml, an open-source library that brings property-based testing to Damlby connecting Hypothesis withthe Daml JSON API. Hypothesis2Daml enables developers to specify invariants, pre/postconditions, and stateful workflows over realistic ledger interactions. It provides automatic input generation, shrinking to minimal counterexamples, party isolation per example, and request helpers that keep tests focused on properties rather than HTTP/JSON wiring. The approach is validated on a benchmark of eight contracts and twenty-seven properties that range from simple invariants to multi-step, role-sensitive workflows. All properties held under testing, with runs completing within practical time bounds, demonstrating that property-based testing is feasible and effective for Daml applications. A comparative evaluation situates Hypothesis2Daml among established tools and methods, highlighting strengths in usability and expressiveness, and acknowledging a throughput trade-off due to JSON-API transport overhead. Contributions include the Hypothesis2Daml library and a reusable benchmark with representative properties. Limitations concern transport overhead, ecosystem specificity, and dependence on developer-authored properties and generators. Future work targets automation, performance, andscope expansion. Together, these results establish a practical path for making property-based testing a routine part of building Daml applications.

Vikash Singh, Little, Barrett, Phil Hayes, Fang, Max · 7 authors

Verifying the private liquidity state of Lightning Network (LN) channels is desirable for auditors, service providers, and network participants who need assurance of financial capacity. Current methods often lack robustness against a malicious or compromised node operator. This paper introduces a methodology for the verification of LN channel balances. The core contribution is a framework that combines Trusted Execution Environments (TEEs) with Zero-Knowledge Transport Layer Security (zkTLS) to provide strong, hardware-backed guarantees. In our proposed method, the node's balance-reporting software runs within a TEE, which generates a remote attestation quote proving the software's integrity. This attestation is then served via an Application Programming Interface (API), and zkTLS is used to prove the authenticity of its delivery. We also analyze an alternative variant where the TEE signs the report directly without zkTLS, discussing the trade-offs between transport-layer verification and direct enclave signing. We further refine this by distinguishing between "Hot Proofs" (verifiable claims via TEEs) and "Cold Proofs" (on-chain settlement), and discuss critical security considerations including hardware vulnerabilities, privacy leakage to third-party APIs, and the performance overhead of enclaved operations.

Shibly Sadik, Md Tanjum An Tashrif

Traditional zero trust architectures (ZTA) rely on centralized policy engines and mutable audit logs, creating single points of failure and limiting forensic integrity. We present a novel blockchain-secured zero trust network architecture that integrates distributed ledger technology, machine learning-based threat detection, and zero-knowledge proof authentication to eliminate the se limitations. Our five-layer framework comprises a user access layer, a zero-trust core (policy engine, administrator, and enforcement points), a blockchain security layer (smart con- tracts, consensus engine, and audit trail), a decentralized identity layer (W3CDIDs and zero-knowledge proof authentication), and protected network resources. Access requests are validated via zero-knowledge proofs; trust scores are computed dynamically using Random Forest classifiers achieving 96.3% accuracy; policy decisions are executed through Practical Byzantine Fault Tolerance (PBFT) consensus; and all security events are recorded immutably on Hyperledger Fabric. Smart contracts create automated incident response, which isolates malicious parties in less than 500 ms without human interference. Experiments on a 50-node testbed during simulated attacks show an authentication latency of < 100 ms, a throughput of > 1,000transactions per second (TPS), a threat-detection accuracy of > 95%, and a false-positive rate of < 5%. Our solution reduces the latency by 30% and the operating overhead by 60% and offers 100% integrity of the audit trail compared to traditional zero-trust implementations. This publication represents the first end-to-end integration of blockchain and zero-trust systems, providing a privacy-preserving, scalable, and resilient security architecture for enterprise environments and next-generation networks.

P. C. Pal, Shubhasmita Behera

Keybyte Systems, Intentix Lab , Melbourne, Australiapronab@keybytesystems.com.au Supported by AusIndustry Grant IR2405165 __________________________________AbstractModern cloud-native applications distribute business logic across multiple layers: application code, orchestration frameworks, service meshes, and infrastructure configurations. This distribution creates ”hidden logic”—execution rules embedded in infrastructure that are invisible during design and difficult to trace at runtime. We present Intention Space , a computing model built on the CPUX (Common Path of Understanding and Execution) paradigm that consolidates all business logic into explicit, design-time declarations using plain-language state pulses. In our model, Design Nodes (DNs) contain computation while Gatekeepers declare execution conditions as named pulses (e.g., ”payment validated”: Y). The infrastructure provides only mechanical enforcement through an Intention Loop that matches runtime state to Gatekeepers without adding decision logic. We demonstrate that complex workflows—traditionally requiring nested if-then branching and explicit loops—can be expressed as linear CPUX sequences where execution paths emerge from data state rather than code branching. Our Golang implementation shows complete elimination of orchestration code while maintaining full cognitive traceability. Beyond technical innovation, CPUX addresses a critical social computing crisis: the lack of accountability in distributed social platforms. By creating unique, device-level CPUX footprints for every interaction, our model enables verifiable traceability from device identity through user intention to executed action—restoring accountability to social computing while preserving privacy. We argue this separation of intent (CPUX) from enforcement (infrastructure) is essential for building LLM-integrated, auditable, and socially responsible distributed systems.Keywords: CPUX, Intention Space, Design Nodes, Cognitive Computing, Data-Driven Execution, Microservices Architecture, Cloud Computing, LLM Integration, Social Computing Accountability__________________________________1. Introduction1.1 The Hidden Logic ProblemConsider a typical e-commerce order processing system deployed on Kubernetes with Istio service mesh:// order-service/main.go (Business Logic Layer) func ProcessOrder(order Order) error { if order.Amount > 1000 { if err := premiumValidator.Validate(order); err != nil { return retry(premiumValidator.Validate, 3, order) } } else { standardValidator.Validate(order) } // … more branching logic }# k8s/hpa.yaml (Infrastructure Layer) spec: metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 80 # Hidden rule: Scale when CPU > 80%# istio/retry-policy.yaml (Service Mesh Layer) spec: http: - retries: attempts: 3 perTryTimeout: 2s # Hidden rule: Retry 3 times on failureQuestion: What is the complete execution flow for a $1500 order that fails validation on first attempt?Answer: One must read and correlate:Application code (branching logic)Kubernetes manifests (scaling rules)Istio configurations (retry policies)Service mesh observability logs (runtime behavior)This hidden logic distribution creates fundamental problems:Traceability : No single artifact shows complete flowTestability : Must test infrastructure + code interactionsAuditability : Business stakeholders cannot validate logicMaintainability : Changes require coordinating multiple layersLLM Integration : No structured representation for AI reasoningSocial Accountability : Cannot trace interactions to source devices/users1.2 The Core InsightWe observe that traditional computing conflates two distinct concerns:What should happen(business intent)How to make it happen(mechanical execution)Current architectures intertwine these concerns across code, configuration, and infrastructure, making systems cognitively opaque.Our Contribution: We introduce CPUX (Common Path of Understanding and Execution) , a paradigm that separates business intent from infrastructure enforcement:CPUX Structure : Declares all possible execution paths as sequences of Design Nodes (DNs) with plain-language Gatekeeper conditionsInfrastructure : Provides mechanical execution (Intention Loop) that enforces CPUX declarations without adding decision logicDevice-Level Identity : Each CPUX execution tied to unique device fingerprint + user intention, enabling social computing accountabilityResult : Complete business logic is visible in CPUX; infrastructure remains purely mechanical; every social interaction is traceable1.3 Key ContributionsFormal Model : CPUX as cognitive execution contract with Design Nodes, Intentions, Objects, and Pulses as primitive componentsElimination of Hidden Logic : All business decisions visible in design-time CPUX declarations; infrastructure adds zero decision logicPlain-Language State Declarations : Execution conditions expressed as named pulses (e.g., ”inventory confirmed”: Y) enabling business stakeholder review and LLM integrationData-Driven Execution : Runtime branching eliminated from code; execution paths emerge from pulse state matching via SyncTestSocial Computing Accountability : Device-level CPUX fingerprints create unique, traceable identity for every social interaction, addressing the accountability crisis in platforms like Facebook, Twitter, TikTokImplementation & Evaluation : Golang framework code sample with concrete use case demonstrating zero orchestration code while maintaining full traceability1.4 Paper OrganizationSection 2 examines related work. Section 3 presents the PnR computing model and CPUX formalism. Section 4 details the architecture and implementation. Section 5 evaluates our approach through metrics and case studies. Section 6 discusses LLM integration. Section 7 introduces CPUX for social computing accountability—the urgent global need. Section 8 concludes with future directions.__________________________________2. Related Work2.1 Workflow Orchestration SystemsAWS Step Functions [1] and Azure Logic Apps [2] provide visual workflow definition with explicit state machines. However, they:Use proprietary JSON/XML DSLs (not plain language)Embed conditional logic in workflow definitions (still branching)Remain platform-specific (vendor lock-in)Require reading workflow definitions to understand flowCannot trace to device/user identityApache Airflow [3] and Temporal [4] define workflows as code with DAG structures. They improve on step functions ,recoverability but:Business logic still in code (if-then branches)Workflow orchestration separate from executionNo plain-language condition declarationsNo device-level traceabilityCPUX Advantage : All logic in plain-language pulses, platform-agnostic, no explicit branching in declarations, device-level identity for every execution, recoverability built into platform. 2.2 Service Mesh & OrchestrationIstio [5] and Linkerd [6] provide traffic management, retries, circuit breaking. Kubernetes Operators [7] encode reconciliation logic. These systems:Hide business rules in YAML configurationsDistribute logic across mesh config + operator codeFocus on infrastructure concerns (not business flow)Lack unified view of complete execution pathNo user/device attributionCPUX Advantage : Consolidates all execution logic in CPUX; infrastructure config aligned with business intent; device identity integral.2.3 Event-Driven ArchitecturesApache Kafka [8], AWS EventBridge [9] enable event-driven systems with loose coupling. Reactive systems [10] promote message-passing. However:Event flows implicit (must trace message paths)Conditional logic in event handlers (code-level branching)No design-time declaration of all possible flowsNo provenance tracking to source deviceCPUX Advantage : Explicit declaration of all event-driven paths as DN sequences with visible Gatekeepers; device identity in event provenance.2.4 Intent-Based SystemsIntent-Based Networking [11] translates high-level intents to network configurations. Policy-based management [12] separates policy from mechanism. Closest to our work, but:Focus on infrastructure (not application logic)Policies often domain-specific (not general computing)Limited plain-language expressivenessNo user accountabilityCPUX Advantage : General-purpose computing model with full plain-language pulse declarations applicable to any domain; device-level user accountability.2.5 Formal Methods & Model CheckingTLA+ [13], Alloy [14], and Petri Nets [15] enable formal specification and verification. These are powerful but:Require specialized formal notation (high learning curve)Specification separate from implementation (sync problems)Not designed for runtime executionNo social computing traceabilityCPUX Advantage : Declarations are executable; CPUX structure IS the implementation contract; device identity embedded.2.6 Social Computing & AccountabilityBlockchain-based identity [16] and zero-knowledge proofs [17] address digital identity but:Focus on cryptographic primitives (not execution tracing)Don’t integrate with application logicNo cognitive representation of intentFederated social networks [18] (Mastodon, ActivityPub) improve decentralization but:Still lack device-level traceabilityNo structured intent representationCannot prove user intended specific actionCPUX Advantage : First system to integrate device identity, user intention, and execution trace in single cognitive framework.2.7 PositioningCPUX is the first system to combine:Plain-language execution conditions (like Intent-Based Networking)Executable specifications (unlike formal methods)Complete flow visibility (unlike distributed orchestration)Zero hidden infrastructure logic (unique contribution)Device-level social accountability (unique contribution)__________________________________3. The PnR Computing Model3.1 Core Abstractions3.1.1 Pulse: Atomic State UnitA Pulse is the fundamental data unit representing a named state with optional response and trivalence:Pulse = (Name: String, Response: Value, Trivalence: {Y, N, U})Name : Plain-language identifier (e.g., ”payment validated”)Response : Optional value (e.g., transaction ID)Trivalence : Y (yes/true), N (no/false), U (undecided)

N. A. Natraj, J. Midhunchakkaravarthy, Brojo Kishore Mishra

INTRODUCTION: The application of blockchain technology to Internet of Things (IoT) systems offers substantial potential for enhancing security, but traditional consensus mechanisms are ill-suited for resource-constrained environments. While hybrid consensus solutions have emerged as a promising alternative, a systematic framework for their classification and evaluation is notably absent. OBJECTIVES: This study addresses this critical gap by introducing a novel, application-driven framework for analyzing hybrid consensus mechanisms, underpinned by a quantitative synthesis of performance benchmarks. METHODS: We analyze diverse architectures—including combinations of Proof of Work (PoW) and Proof of Stake (PoS), PBFT-enhanced systems, and hierarchical models—through the lens of specific IoT application priorities, such as latency, energy efficiency, and scalability. Case studies of IOTA's Tangle, IoTeX's Roll-DPoS, and Hyperledger Fabric illustrate these practical trade-offs. RESULTS: Our framework reveals not only primary performance trade-offs but also critical "second-order" complexities, such as emergent vulnerabilities at the intersection of different consensus layers. CONCLUSION: Our findings demonstrate that this structured, quantitatively-grounded approach provides an effective methodology for designing and selecting regulatory-compliant hybrid consensus solutions for specific IoT applications.

Maria Syed

This thesis investigates critical software delivery latency at a large fintech organization, where a modern micro-application architecture was severely bottle-necked by a legacy, manual, ticketing-based approval system. This hybrid environment created an acute organizational bottleneck, imposing high coordination burdens and unpredictable delays on globally distributed feature teams. Using an Action Research (AR) methodology, the study first established a high-friction baseline, measuring the median Lead Time for Changes (LTC) at 20.2 hours. The core intervention involved replacing the mandatory manual approval gate with a fully automated, self-service deployment model integrated directly into the Continuous Integration/Continuous Delivery (CI/CD) pipeline. The intervention successfully drove significant organizational efficiency, yielding a 69% reduction in LTC, dropping the median time from 20.2 hours to 6.2 hours. Concurrently, Deployment Frequency (DF) increased by 213% (from 47 to 100 releases per week). This improvement solidified the organization's position within the DORA elite performance tier. The primary practical guidance derived from this case study is that sustained software acceleration requires prioritizing the decentralization of control over the deployment trigger. This is achieved not merely through technical automation, but by deliberately eliminating all mandatory human coordination steps via external systems (e.g., tickets), relying instead on real-time visibility tooling integrated into the developer workflow. Additionally, and more importantly, this required a complementary organizational culture shift, which involved transfer-ring accountability for production stability directly from administrative roles, such as the Program Manager, to the autonomous development teams.

Rene Casanova (22631729), Fernan A. Villa-Garzon (22631732), John W. Branch-Bedoya (13936272)

Background Health information systems (HIS) are critical for digital health transformation, yet fragmentation and poor interoperability adoption remains a major challenge. Objectives This study systematically reviews architectural patterns used in HIS and evaluates their alignment with ecosystem-level requirements. Methods Following PRISMA 2020 guidelines, a systematic literature review was conducted across Scopus, IEEE Xplore, PubMed, and Web of Science (2020–2025). Eligible studies described, evaluated, or proposed HIS solutions. Results From an initial set of 304 records, 89 met the inclusion criteria. Service-based and decentralized/distributed ledger architectures were predominant, with emerging models integrating edge computing and modular design. FHIR-based contracts are found as stabilizers of interfaces, enabling validation and reducing integration costs. However, gaps persist in cross-border care, sustainability, and artificial intelligence integration. Conclusion While microservices dominate current HIS architectures, achieving resilient, interoperable ecosystems requires greater architectural diversity and intersectoral collaboration.

J.G. An, Z. P. Zhu, Ian Miers, Zaoxing Liu

Verifiable network telemetry is crucial for ensuring transparency and trust in network measurements. However, telemetry logs (e.g., NetFlow records) often contain sensitive data, making public verification challenging. Recent work has attempted to address this problem using Trusted Execution Environments (TEEs), such as Intel SGX, to provide confidentiality and integrity guarantees. However, TEEs are known to suffer from complex deployment requirements and limited scalability. In this paper, we introduce a software-based approach utilizing the latest advances in Zero-knowledge Proofs (ZKPs) to enable verifiable network telemetry without revealing the underlying sensitive logs or relying on special-purpose hardware. Our system employs a general-purpose ZKP virtual machine (RISC Zero) to generate cryptographic proofs over NetFlow data, enabling operators to securely attest to network flow metrics. Our preliminary results indicate that our ZKP-based design offers a viable path toward overcoming deployment and scalability limitations inherent in the solutions that require special-purpose hardware.

Rene Casanova, Fernán A Villa-Garzón, John W. Branch

Background: Health information systems (HIS) are critical for digital health transformation, yet fragmentation and poor interoperability adoption remains a major challenge. Objectives: This study systematically reviews architectural patterns used in HIS and evaluates their alignment with ecosystem-level requirements. Methods: Following PRISMA 2020 guidelines, a systematic literature review was conducted across Scopus, IEEE Xplore, PubMed, and Web of Science (2020-2025). Eligible studies described, evaluated, or proposed HIS solutions. Results: From an initial set of 304 records, 89 met the inclusion criteria. Service-based and decentralized/distributed ledger architectures were predominant, with emerging models integrating edge computing and modular design. FHIR-based contracts are found as stabilizers of interfaces, enabling validation and reducing integration costs. However, gaps persist in cross-border care, sustainability, and artificial intelligence integration. Conclusion: While microservices dominate current HIS architectures, achieving resilient, interoperable ecosystems requires greater architectural diversity and intersectoral collaboration.

Ningyu He, Tianyang Chi, Xiaohui Hu, Haoyu Wang

Maximal Extractable Value (MEV) activities pose critical operational challenges for blockchain enterprises, requiring automated detection systems to maintain platform integrity and regulatory compliance. Current industrial practices rely on heuristic rule-based methods with substantial accuracy limitations and inability to adapt to evolving MEV strategies. This paper presents an automated software engineering solution for large-scale MEV detection, introducing a novel graph-based profitability identification algorithm that replaces inflexible heuristic rules with adaptive mechanisms. Our automated system achieves 0.6% false positive rates for arbitrage detection and 2.4% false negative rates, significant improvements over existing methods with much higher error rates. We validate our approach on 21 million Ethereum blocks containing 2.5 billion transactions, covering critical infrastructure transitions including The Merge and Proposer-Builder Separation. Our automated pipeline identifies 12.1 million MEV activities, including 1.2 million previously undetectable advanced variants that pose emerging risks to platform operators. Key findings provide actionable insights for blockchain enterprises: private transaction architectures protect 71.4% of low-yield MEV opportunities rather than harming participants, contradicting previous assumptions. However, we identify concerning builder-searcher collusion involving 2,000+ transactions worth 350 ETH, highlighting compliance risks. Additionally, intensifying centralization trends show a single oligopoly controlling 43.1% of MEV activities in 2024, presenting systemic risks. Our automated detection framework provides blockchain enterprises with production-ready tools for MEV monitoring, risk assessment, and compliance management while offering critical insights for infrastructure design decisions in rapidly evolving DeFi environments.

Kairan Sun, Zhengzi Xu, Kaixuan Li, Lyuye Zhang · 7 authors

Web3 applications, particularly decentralized finance (DeFi) protocols, have grown rapidly with over $100 billion locked in smart contracts, attracting sophisticated attacks causing billions in losses. When attack occur, security analysts need to perform fault localization to identify vulnerable functions and understand attack vectors. This critical process currently requires an average of 16.7 analyst hours per incident due to complex blockchain execution models, rapidly evolving protocol interactions, and multi-contract attack patterns that exceed existing analytical capabilities. Despite its critical importance, blockchain fault localization has received limited attention due to fundamental challenges requiring semantic understanding of economic models and protocol-specific logic. Existing blockchain-specific tools target only single vulnerability types, while the only comprehensive solution, DAppFL, relies on machine learning model that may miss sophisticated exploits and lacks interpretability in results. Recent advances in large language models (LLMs) demonstrate remarkable code comprehension capabilities, but existing applications focus on proactive vulnerability detection with minimal exploration of post-incident fault localization.We present FaultSeeker, an LLM-empowered framework for blockchain transaction fault localization. Our two-stage architecture combines transaction-level forensics for strategic scoping with coordinated specialist agents for sustained reasoning. This design provides long-term memory management via orchestrator agents and specialized attention allocation through coordinated workers, enabling comprehensive analysis across complex multi-contract transactions without context loss. We evaluate Fault-Seeker on a compiled dataset of 115 real-world malicious transactions with expert-validated annotations spanning diverse attack patterns and complexity levels. Results demonstrate that FaultSeeker significantly outperforms existing approaches, including DAppFL and leading native LLMs (GPT-4o, Claude 3.7 Sonnet, DeepSeek R1), while maintaining practical efficiency (4.4- 8.6 minutes) and cost-effectiveness ($1.55-$4.53 per transaction).

Wed Kadhim Oleiwi, Alharith A. Abdullah

Abstract Distributed Software Define Network (DSDN) offers to eliminate the single point of failure present in the centralized SDN controller. The main concept is to have numerous controllers that can share the burden on the network, and one controller can take over another controller when it breaks. In this study, we work on an Opendaylight-based completely distributed system to deal with issues of the controller's central processing unit (CPU) consumption being uneven. Every controller administers its own domain therefore sharing the burden across the network's controllers. To update the flow rules and broadcast new rules to all the controllers at once, maintaining the consistency of the controller's rule set, we create an application ledger that maintains a distributed ledger. Results demonstrate that, in addition to ensuring the uniformity of inventory shards across all controllers in the system, our work presented an increase with throughput reaching 40% and the bandwidth of controller's communication is less with almost 31%.

Arman Petrosyan

The paradigm of enterprise analytics is undergoing a fundamental shift from centralized, reactive reporting to distributed, proactive intelligence. This review article evaluates the integration of machine learning models within SAP business intelligence frameworks operating across multi-cloud and hybrid environments. We analyze how the transition toward a federated data architecture, facilitated by SAP Datasphere, enables the deployment of high-performance neural networks without the traditional constraints of data replication. The study specifically examines the efficacy of Long Short-Term Memory units for temporal forecasting in SAP Integrated Business Planning and the role of unsupervised learning models in real-time financial anomaly detection. Furthermore, we explore the rise of augmented analytics and natural language processing in democratizing data access, alongside the operational necessity of MLOps to mitigate model drift in volatile global markets. The review also addresses critical technical and strategic barriers, including data latency across distributed cloud nodes, the harmonization of structured and unstructured data, and the evolving landscape of global data sovereignty. By synthesizing current performance benchmarks with future directions such as agentic intelligence and the integration of carbon accounting through the green ledger, this research provides a roadmap for architecting autonomous analytical ecosystems. We conclude that the convergence of machine learning and distributed cloud infrastructure is the primary catalyst for transforming raw enterprise data into a strategic, self-optimizing asset.

Eranga Bandara, Safdar Hussain Bouk, Sachin Shetty, Ravi Mukkamala · 10 authors

Software services are crucial for reliable communication and networking; therefore, Site Reliability Engineering (SRE) is important to ensure these systems stay reliable and perform well in cloud-native environments. SRE leverages tools like Prometheus and Grafana to monitor system metrics, defining critical Service Level Indicators (SLIs) and Service Level Objectives (SLOs) for maintaining high service standards. However, a significant challenge arises as many developers often lack in-depth understanding of these tools and the intricacies involved in defining appropriate SLIs and SLOs. To bridge this gap, we propose a novel SRE platform, called SRE-Llama, enhanced by Generative-AI, Federated Learning, Blockchain, and Non-Fungible Tokens (NFTs). This platform aims to automate and simplify the process of monitoring, SLI/SLO generation, and alert management, offering ease in accessibility and efficy for developers. The system operates by capturing metrics from cloud-native services and storing them in a time-series database, like Prometheus and Mimir. Utilizing this stored data, our platform employs Federated Learning models to identify the most relevant and impactful SLI metrics for different services and SLOs, addressing concerns around data privacy. Subsequently, fine-tuned Meta's Llama-3 LLM is adopted to intelligently generate SLIs, SLOs, error budgets, and associated alerting mechanisms based on these identified SLI metrics. A unique aspect of our platform is the encoding of generated SLIs and SLOs as NFT objects, which are then stored on a Blockchain. This feature provides immutable record-keeping and facilitates easy verification and auditing of the SRE metrics and objectives. The automation of the proposed platform is governed by the blockchain smart contracts. The proposed SRE-Llama platform prototype has been implemented with a use case featuring a customized Open5GS 5G Core.

Keyhan Mohammadi, Reza Ebrahimi Atani

This paper presents TEOM (The Evolutionary Open Machine), a decentralized operating protocol designed for smart enterprise architecture. TEOM enables secure, scalable, and flexible integration across diverse organizational functions, from AI and IoT to finance and governance. By applying a combination of Hashgraph and blockchain technologies, TEOM provides a robust framework for decentralized service delivery, real time data sharing, and multi organization collaboration. It supports the creation of autonomous, fault tolerant ecosystems that scale effortlessly, ensuring high availability and fault resistance across distributed networks. TEOM’s extensibility allows seamless integration of legacy systems and third-party platforms, promoting interoperability within smart enterprise environments. The protocol also integrates advanced AI capabilities for intelligent decision making, predictive analytics, and federated learning. Through automated service provisioning and decentralized transaction management, TEOM fosters an agile, transparent, and adaptive framework for modern enterprise architecture, empowering organizations to operate efficiently in a zero-trust environment.

Sudhakar Tiwari

This study presents a blockchain-backed Role-Based Access Control (RBAC) service for distributed clouds, addressing auditable authorization under multi-region latency and consistency constraints. Although end-to-end costs and tail impacts of permissioned ledgers in RBAC are under-quantified, the present study implements a multi-region deployment with Istanbul Byzantine Fault Tolerance (IBFT), batched commits, and an off-chain cache, with evaluation against centralized and event-sourced baselines, throughput reported in requests per second (RPS), and calibration via Expected Calibration Error (ECE) and Brier Score using multi-seed runs and moving-block bootstrap. At the target load, the candidate achieved 145.0 +/- 4.6 ms 95th percentile (p95) latency and 1008 +/- 13 RPS while meeting the 150.0 ms Service Level Objective at 1000 rps; the 99th percentile (p99) policy-update consistency lag measured 1750.0 +/- 150.0 ms. Connectivity remains the bottleneck. The parts are familiar; the sequencing is not, combining ledger finality with cache-assisted reads and bounded block intervals to narrow the gap to centralized designs under controlled failures. These findings indicate that tamper-evident auditing can be added with modest overhead for enterprise security operators planning multi-region cloud authorization.

Krishna Dusad

Today's distributed payment systems must function correctly despite the inherent presence of asynchrony, partial failures, and third-party integrations. Unlike typical RPC-based workflows used in software development, payment flows are heavily influenced by external delays, retries, timeouts, and nondeterministic state changes across multiple systems of record. A fault-tolerant ledger abstraction that decouples payment intent from execution enables safe retries and supports service events that may arrive out of order. Correctness and safety depend on distributed transaction constructs such as outbox/inbox patterns, compensation workflows, and time-bounded state machines to contain the effects of race conditions, double submissions, and ambiguous or indeterminate outcomes. A declarative reconciliation framework continuously verifies consistency between internal and external systems, enabling real-time anomaly detection and facilitating orchestration and recovery. These pragmatic engineering approaches, validated through simulations and production-level benchmarks, offer guidance for building resilient payment infrastructures in naturally asynchronous and failure-prone environments.

Liang, Hanzhong, Yue Duan, Xing Su, Xiao Li · 8 authors

As the Web3 ecosystem evolves toward a multi-chain architecture, cross-chain bridges have become critical infrastructure for enabling interoperability between diverse blockchain networks. However, while connecting isolated blockchains, the lack of cross-chain transaction pairing records introduces significant challenges for security analysis like cross-chain fund tracing, advanced vulnerability detection, and transaction graph-based analysis. To address this gap, we introduce ConneX, an automated and general-purpose system designed to accurately identify corresponding transaction pairs across both ends of cross-chain bridges. Our system leverages Large Language Models (LLMs) to efficiently prune the semantic search space by identifying semantically plausible key information candidates within complex transaction records. Further, it deploys a novel examiner module that refines these candidates by validating them against transaction values, effectively addressing semantic ambiguities and identifying the correct semantics. Extensive evaluations on a dataset of about 500,000 transactions from five major bridge platforms demonstrate that ConneX achieves an average F1 score of 0.9746, surpassing baselines by at least 20.05\%, with good efficiency that reduces the semantic search space by several orders of magnitude (1e10 to less than 100). Moreover, its successful application in tracing illicit funds (including a cross-chain transfer worth $1 million) in real-world hacking incidents underscores its practical utility for enhancing cross-chain security and transparency.

Authors unavailable

In the evolving landscape of cybercrime and digital investigations, the integrity and traceability of digital evidence are paramount.Traditional chain-of-custody (CoC) mechanisms in digital forensics rely heavily on centralized systems, manual logging, and institutional trust, all of which are prone to human error, tampering, and data loss.This study introduces a blockchain-based chain-of-custody model aimed at ensuring immutable, transparent, and verifiable tracking of digital evidence across its entire lifecycle-from acquisition and analysis to presentation in court.Leveraging blockchain's decentralized architecture and cryptographic immutability, the proposed framework records every interaction with digital evidence, including transfers, access logs, and analysis events, in a tamper-proof ledger distributed across trusted nodes in a forensic network.Smart contracts automate procedural compliance, access permissions, and time-stamping, thereby reducing reliance on third-party oversight and enhancing procedural integrity.The system was designed and simulated using Hyperledger Fabric, integrating role-based access control and hash-based evidence fingerprinting.Benchmark testing demonstrated the model's robustness in preserving forensic timelines under various adversarial scenarios, including internal breaches and unauthorized access attempts.In addition to enhancing evidentiary credibility, this blockchain-enhanced CoC model offers interoperability with existing digital forensic tools and forensic readiness systems.By aligning with legal admissibility standards and ensuring end-to-end accountability, the framework strengthens the evidentiary chain, particularly in multi-jurisdictional and cloudbased investigations.As digital forensics faces increasing scrutiny regarding evidentiary handling, this research presents a scalable and future-proof alternative to conventional CoC practices-crucial for maintaining the rule of law in cybercrime prosecutions.

Jing Shi, Xiaoying Bai, Wenzheng Zhang, Pei-Lun Li · 7 authors

No abstract is available for this record.

Lvbin Xu, Lei Wu, Xingyu Wu, Zhaoyu Su

No abstract is available for this record.