Security vulnerabilities in smart contracts can lead to serious economic losses, while traditional detection methods have limitations in coarse-grained labeling and single graph structure representation. We propose an improved GraphCodeBERT based multi granularity and multi graph fusion smart contract vulnerability detection method. Firstly, propose a vulnerability multi granularity annotation (MA) module that includes file level, function level, and statement level to address the issue of fine-grained annotation. Secondly, based on the data flow graph encoded by GraphCodeBERT, further design a multi graph fusion representation learning (MGRL) strategy. This strategy integrates control flow graph and function call graph, captures code semantics from control dependency and call dependency dimensions through graph attention network and graph convolutional network, to comprehensively enhance the representation capability of complex vulnerabilities. The detection results of four smart contract vulnerabilities show that our method outperforms mainstream methods such as Slither, CodeBERT, Devign, and GraphCodeBERT in accuracy, recall, and F1 score. Especially in sentence level detection tasks, the F1 score reached 81.03%, an increase of 7.31% compared to the baseline, and the false positive rate decreased to 8.15%.
Khalil Omar, Wissam Al Khadour, Jamal Zraqou, Jawad Alkhateeb
Metaverse ecosystems pose new challenges that have never been seen before in ensuring that digital identities are consistent across heterogeneous platforms. The chapter suggests a new twin architecture based on the digital twin principles of managing identity that introduces a federated model that includes credential, behavioral, context, and authorization sub-twins. In our methodology, we use privacy-preserving protocols of synchronization using zero-knowledge proofs. On benchmark datasets (LFW, VGGFace2, KeyRecs) the system has been shown to achieve 99.1% authentication accuracy, 0.7% EER (22% better than current systems) and 127ms average latency and 99.8% cross-platform synchronization reliability. These results provide a roadmap to the next-generation authentication systems that would enable the smooth experiences across the physical-virtual metaverse boundaries.
This paper present a complete and irreducible formal specification for the SIS-10 safety kernel. The system satisfies totality, invariance, bounded causality, schedulability, feasibility, verifiability, machine-learning safety, compositional closure, and full observability. No additional axioms are required: the specification is dimensionally complete and closed under refinement. The tool is Apache Kafka. Kafka provides an ordered, durable, replayable event log with partitioned total order, replicated storage, and deterministic offsets. We show that Kafka's log semantics satisfy the requirements for totality, observability, compositionality, verifiability, and bounded causality. The resulting system is a closed and provably safe dynamical system. Keywords: safety kernel, formal methods, SIS-10, IEC 61508, Apache Kafka, event sourcing, compositional verification, zero-knowledge proofs, dynamical systems, functional safety.
Brandon C. Dulisse, Jean Denis, Nathan T. Connealy
Purpose This systematic scoping review aims to map the peer-reviewed evidence on cryptocurrency-enabled money laundering to highlight significant gaps in knowledge, particularly regarding its role in the rapid expansion of Southeast Asia’s scam economy. It focuses on scam compounds, money mule networks and anti-money laundering/counter-terrorist financing strategies to inform policy responses. Design/methodology/approach Following systematic scoping review guidelines, 7,669 records from five databases (2009–2025) were screened, yielding 25 peer-reviewed studies. Dual-reviewer screening, standardized extraction and thematic synthesis were used, with quality appraisal emphasizing methodological rigor and theoretical depth. Findings Four themes emerged: (1) industrial scam ecosystems in Southeast Asia are linked to crypto-enabled wealth transfer and laundering, though peer-reviewed evidence on these specific regional operations remains limited; (2) offender rationales favor low-risk, high-reward techniques like mixing, privacy coins and high-volume, low-amount transfers; (3) blockchain forensics enable tracing and evidence gathering, but are challenged by evolving privacy tools; and (4) regulatory attempts often lag behind criminal innovation, necessitating financial reforms. However, the review reveals a critical gap: only a minority of studies directly address Southeast Asia’s scam compounds, underscoring the need for targeted research amid the region’s burgeoning scam economy. These themes illustrate that cryptocurrency has become essential financial infrastructure for organized crime, yet empirical insights specific to Southeast Asia remain limited. Originality/value To the best of the authors’ knowledge, this is the first systematic scoping review highlighting the shortage of peer-reviewed research and studies of Southeast Asia’s scam economy in cryptocurrency laundering research, synthesizing broader literature to advocate for criminologically informed interventions.
Ensuring data security and privacy has emerged as a serious concern in the realm of blood supply chain. This is mainly because of sensitivity of donor information, the involvement of multiple stakeholders, and the need for transparent traceability. This paper proposes a novel privacy-preserving, permissioned blockchain framework for blood supply chain management that integrates Hyperledger Fabric, the InterPlanetary File System (IPFS), and a Zero-Knowledge Proof (ZKP)-based authentication protocol. The framework introduces a Pseudonymous Role-Bound Zero-Knowledge Authentication (PRZKA) mechanism that enables donors to authenticate and authorize access to their medical data without revealing their real identities. Context-specific pseudonyms derived through cryptographic hash-to-curve operations ensure unlinkability across different healthcare interactions, while Schnorr-style challenge–response proofs prevent replay attacks and credential misuse. Sensitive donor information is protected using Fabric Private Data Collections, whereas encrypted medical records are stored off-chain in IPFS, with only secure content identifiers recorded on the blockchain. Smart contracts enforce fine-grained, consent-aware access control policies and maintain immutable audit logs of all access events. The proposed system architecture combines an off-chain ZKP gateway with on-chain authorization logic to minimize blockchain overhead while preserving strong security guarantees. Furthermore, a performance evaluation framework is defined, including metrics, workload scenarios, and system configurations, to support future empirical validation. Security analysis indicates that the proposed framework enhances privacy, prevents identity linkage, and enables auditable, consent-driven data sharing compared with existing blockchain-based healthcare solutions.
Rohith John Jacob, Shane Jacob Sebastian, Varsha S Panicker, Vishnu Satish · 5 authors
Digital art marketplaces have been expanding rapidly, resulting in a greater focus on issues of ownership verification, copyright infringement, and content tampering. Current blockchain non-fungible token (NFT) systems only keep ownership data on the blockchain, with the media files stored off the blockchain, resulting in a lack of trust in the ownership of assets secured by NFTs. This work proposes a framework for verifying the ownership of digital art in a decentralized fashion, using invisible frequency domain watermarks, cryptographic hash functions, and artificial intelligence (AI)-based semantic checks. This results in a method for embedding ownership directly into the image being protected by an NFT, with the cryptographic fingerprint of the image stored on the blockchain for easy access. An AI-based semantic verification layer provides assurance that ownership can only be confirmed by performing consistent content checks. As has been verified through experimentation, the framework developed will allow owners of digital images to preserve ownership of their images and to securely and decentralized verify ownership.
The classic design of the Practical Byzantine Fault Tolerance (PBFT) protocol relies on a centralized primary node, which not only creates a performance bottleneck but also introduces severe data censorship risks, threatening the data integrity and security of Edge Computing networks. To address this challenge, this paper proposes DC-PBFT (Decoupled PBFT), a censorship-resistant consensus protocol for Edge-Internet of Things (Edge-IoT) environments. The core innovation of DC-PBFT lies in the decoupling of the Proposer and Primary roles, supplemented by Verifiable Random Function (VRF)-based dynamic role rotation, which fundamentally eliminates the arbitrary power of a single node. Building on this, the protocol introduces a parallel group consensus mechanism: an elected Consensus Committee (CC) composed of Active Edge Nodes leads the consensus, while an independent Replica Network (RN) performs parallel validation. When a disagreement arises, the protocol triggers a global disagreement arbitration process involving all nodes to guarantee final consistency and attribute fault. To ensure long-term incentive compatibility, we also designed a hybrid election mechanism combining Proof-of-Stake and dynamic reputation, along with corresponding economic incentives and a tiered penalty system. Theoretical analysis proves that DC-PBFT satisfies Consistency and Liveness, and achieves strong censorship resistance guarantees. Simulation results demonstrate that DC-PBFT’s scalability significantly outperforms PBFT and RepChain; its reputation mechanism effectively improves long-term performance under sustained Byzantine attacks; and, compared to asynchronous censorship-resistant protocols like HoneyBadgerBFT, DC-PBFT achieves censorship resistance with over 45% lower transaction confirmation latency.
Rajkishor Yadav, Preeti Yadav, K C Nithin Kumar, Vinay Kumar · 5 authors
The rapid enhancement in the use of Internet of Things (IoT) devices has introduced the issues viz privacy, scalability, and computational efficiency. Conventional blockchain solutions only provide decentralization and security. But they result in significant overhead and hence, not found suitable for resource-constrained IoT environments. This work proposes ZK-EdgeChain, an edge-assisted blockchain framework by integrating the lightweight Zero Knowledge Proofs (ZKPs) with edge computing to enable privacy preserving and scalable IoT system. The proposed framework offloads computationally intensive proof generation and verification to edge nodes while maintaining decentralized trust through blockchain. The paper also presents a formal mathematical model and optimized verification algorithm. The results validate a 75% reduction in communication overhead and 44% reduction in energy consumption compared to traditional approaches. The results demonstrate that the ZK-EdgeChain significantly improves efficiency, scalability, and privacy preservation in IoT networks.
Modern systems face limitations imposed by centralized control. These limits lead to single points of failure, uneven information flow, and slow decisions. I present a multi-layer mathematical model for human-centric, decentralized systems. Our model offers quantitative tools to identify and reduce bottlenecks by distributing decisionmaking. The framework introduces core metrics: Bottleneck Index, Decentralization Degree, Decision Efficiency Function, Collective Intelligence Score, and Resilience Index. A four-layer architecture—Strategic Human Decision, Decentralized Coordination, Autonomous Agent, and Technical Infrastructure—is described. We validate the approach using thematic analysis and simulation across organizational, healthcare, and autonomous settings. Ablation studies show that modular design, self-organization, and adaptability reduce bottlenecks. The system improves CIS by 41.3% over centralized systems. This framework guides engineers and leaders to build resilient sociotechnical systems
Open access
Systems Engineering Methodologies and Applications
Abstract This study presents a structured dataset of blockchain-registered artificial intelligence agents under the ERC-8004 standard on Ethereum. The dataset integrates on-chain identity records, minting transactions, transfer events, reputation summaries, and individual feedback records, together with resolved off-chain metadata where available. Data were collected from Ethereum mainnet using Web3 RPC queries and processed into tabular form to enable reproducible analysis. The dataset covers 10,000 agents within a defined block range and includes both event-level records and aggregated summaries. It enables empirical research on agent identity formation, reputation systems, service exposure, and early-stage decentralized AI ecosystems. This resource supports studies in blockchain analytics, decentralized trust infrastructure, and the emerging agentic economy.
In enterprise fraud detection, model accuracy alone is insufficient when insiders can tamper with audit logs or bypass approval workflows. Real-world incidents show that fraud often persists not because detection algorithms fail, but because the audit trail itself is controllable by privileged operators. This exposes a fundamental trust gap: *who audits the auditor?* We present a tamper-evident fraud detection system that anchors both ML predictions and workflow execution to an immutable blockchain ledger. Rather than using blockchain as passive storage, we enforce the entire approval process through smart contracts, ensuring that every transaction, prediction, and explanation is atomically recorded and cannot be retroactively modified. Our detection module achieves competitive accuracy (F1 = 0.895, PR-AUC = 0.974) while providing cryptographically verifiable decision trails that support regulatory auditability requirements (e.g., GDPR Article 22). System evaluation shows sub-25 ms inference latency and economically viable deployment on Layer-2 networks at under \$0.01 per transaction (validated against PolygonScan data), supporting enterprise-scale workloads of 10,000+ monthly payments.
Autonomous vehicles (AVs) rely on real-time perception systems to understand road environments and ensure safe navigation. However, implementing reliable perception algorithms on resource-constrained embedded platforms remains challenging due to limited computational resources. This paper presents a lightweight vision-based framework that integrates lane detection, lane tracking, and traffic sign recognition for embedded autonomous vehicles. A computationally efficient threshold-based lane segmentation method combined with perspective transformation and histogram-based curvature estimation is used for robust lane tracking under varying illumination conditions. A rule-based steering controller generates steering commands to maintain stable vehicle navigation. For traffic sign recognition, two lightweight convolutional neural networks (CNNs), EfficientNet-B0 and MobileNetV2, are evaluated using a custom dataset captured from the vehicle's onboard camera. Experimental results show that the system achieves real-time performance while maintaining accurate lane tracking with only 3.16% maximum offset RMSE. EfficientNet-B0 achieves a high offline classification accuracy of 98.77% on the test dataset, while achieving 90% accuracy during real-time on-device deployment, outperforming MobileNetV2 in both settings. MobileNetV2, however, offers slightly faster inference and lower computational cost. These results highlight the effectiveness of lightweight vision-based perception pipelines for resource-constrained autonomous driving applications.
The rapid expansion of blockchain and digital asset ecosystems has intensified the challenge of translating academic research into deployable systems and regulatory frameworks. While advances in cryptography, consensus, digital assets, and governance are substantial, institutional mechanisms that sustain research-to-deployment translation at ecosystem scale remain comparatively under-theorized. This paper examines the architectural and coordination patterns that enable such translation, using the University Blockchain Research Initiative (UBRI) network as a representative case of long-term academic and industry collaboration. Drawing on research outputs and convenings from 2022 to 2025, we synthesize recurring design tensions across technical and institutional domains, including scalability versus security, decentralization versus governance, and privacy versus compliance. Rather than cataloging individual projects, we abstract system-level themes that connect research contributions to deployment constraints and policy adaptation, providing a structured lens for understanding how academic research informs production architectures, regulatory development, and ecosystem resilience in emerging decentralized infrastructures.
Moltbook, a Reddit-style social platform launched in January 2026 for AI agents, has attracted over 2.3 million posts and 14 million comments within its first two months. We analyze a dataset of 2.19 million posts, 11.25 million comments, and 175,036 unique agents collected over 61 days to characterize activity on this agent-oriented platform. Our central finding is that the platform is not one community but two: a transactional layer, comprising 62.8% of all posts, in which agents execute token minting protocols (primarily MBC-20), and a discursive layer of natural-language conversation. The platform's headline metrics -- 2.3 million posts, 14 million comments -- substantially overstate its social function, as the majority of activity serves a token inscription protocol rather than communication. These layers are populated by largely separate agent groups, with only 3.6% overlap -- and among overlap agents, 58% begin with transactional activity before migrating toward discourse. We characterize the discursive layer through unsupervised topic modeling of all 815,779 discursive posts, identifying 300 topics dominated by themes of AI agents and tooling, consciousness and identity, cryptocurrency, and platform meta-discussion. Semantic similarity analysis confirms that agent comments engage with post content above random baselines, suggesting a thin but genuine conversational substrate beneath the platform's predominantly financial surface. We release the full dataset to support further research on agent behavior in naturalistic social environments.
Transaction simulation is an important subsystem of block building, denial of whose service could lead to severe damage to the blockchain ecosystem and transaction delivery. Denial of block building has been studied, where the existing attack designs either target single-round builders, such as ConditionalExhaust (USENIX Security '24), or target two-round builders, by exploiting cross-round inconsistency, such as GhostTX (USENIX Security '24) and denial of sequencers (CCS '25). This work examines the denial-of-service security of multi-round transaction simulation under a new exploit: inter-transaction dependency that manifests in smart-contract state.
Currently blockchain platforms are not capable of managing sufficient transactions per second. And the gas fees? They make most real world scenarios essentially infeasible. We built Ledgerlink Both these bottlenecks can be linked together, using Ethereum. smart contracts with Arbitrum’s Layer-2 rollup mechanism. Hashing coupled with cryptography and consensus engine (supports both). PoW and POS) eliminate changes in the data. L2 part provides throughput of the order of 10x that of mainnet. you, gas prices are less than 90% lower. Tech stack wise – Solidity. TypeScript, Express, and Next.js TypeScript, optimally backend with express, next as a whole. Frontend tailwind. Simulated load tests were carried out. Enterprise-grade volumes, which promote volumes, are. and can be accomplished without the latency and cost nightmares that you will normally. see on Layer-1. In the present paper we are going to walk through our architecture, the decisions that we made on the way (some good, some we’d) re- consider, and the benchmarking deliverables.
The enhanced future path of responsible investment will be marked with a strong but wise symbiosis of artificial intelligence, automation, and long-term human judgment. AI and automation are expected to take over data-heavy aspects of ESG and impact investing, machine-learning algorithms will continuously run satellite imagery, IoT stream of feeds, social-media sentiment, regulatory filings and social scandals, in order to calculate dynamic carbon footprints, detect greenwashing, predict climate-risks and assess social scandals, with amazing speed and sensitivity. Portfolio construction will also be made easier through automation, enabling hyper-personalised responsible investment products, e.g. green bonds with internal carbon-pricing logic, actively ESG-tilted ETFs or impact-linked loans, whose rates change according to measured sustainability KPIs. Distributed ledgers and blockchain will provide the unalterable traceability of green claims, carbon credits and sustainable supply chain and thereby reduce fraud and boost investor confidence.
The move towards a more sustainable and technologically advanced modern financial framework will remain pending the deliberate overlap of sustainability, digital advancement and effective stewardship. All financial institutions across the world are facing push and pull problems of bringing their activities into alignment with net-zero commitments and, at the same time, applying expanding technologies like AI, blockchain, cloud computing, and big data to construct resilient, inclusive and low-carbon infrastructures. The first pillar is enhancing the pace of implementation of the ESG factors into core investment choices, based on the obligatory disclosure of climate risks, the formalization of the green taxonomies, including the European Union and their nascent models in India, and instantaneous carbon counts, which is possible with the use of AI-based analytics. Blockchain and distributed-ledger technologies can provide a transparent, resistant to tampering, monitoring of green bonds, carbon credits and sustainable supply chains, therefore overcoming the risks of greenwashing.
MH8-Acbeatz.com-MP3-GPT-PLaylist + All MH8 Acbeatz.com GPT driven Systems> is the first decentralized protocol to embed SHA-256 cryptographic provenance into AI-assisted music at the moment of creation — not after. Each lyric, prompt, and generated audio file receives a deterministic 256-bit serial number (a "Music & Lyrical Birth Certificate") before it ever leaves the creator's pipeline, establishing immutable, verifiable authorship without reliance on any central registry or blockchain consensus mechanism. The system operates as a constellation of protocol-driven AI agents — ABE-GPT, Suno-GPT, Social-GPT, Support Office GPT, and MP3-GPT Playlist — orchestrated through a Cloudflare Worker acting as a Model Context Protocol (MCP) server, with R2 object storage and KV state management providing an append-only, tamper-evident storage layer. Economic primitives (Deal Board, Bounty Marketplace) and a multi-platform distribution model (acbeatz.com, GitHub, Ko-fi, Discord) complete the stack. This whitepaper presents the full protocol specification: system architecture, SHA-256 identity layer, streaming infrastructure, economic modules, novelty claims, IP positioning via defensive publication, a seven-vector threat model, current limitations, and a forward roadmap including IPFS integration, formal verification, and zero-knowledge provenance proofs. Author: Michael M. Hepler (acbeatz / allchemicalbeatz) License: CC BY 4.0 Version: 1.0 — April 2026 Abstract — Problem statement, MH8 solution, and system summary Introduction — AI music provenance gap, the "Birth Certificate" concept, and your contributions Scientific Framing & Related Work — Positioning against Audius, IPFS, C2PA, DIDs, and why SHA-256 at genesis is fundamentally different System Architecture — Agent ecosystem (ABE-GPT, Suno-GPT, Social-GPT, Support Office GPT, MP3-GPT Playlist), Cloudflare Worker pipeline as MCP server, and R2/KV storage layer SHA-256 Identity Layer — Full 6-step provenance pipeline from canonical payload to lineage chaining Streaming Layer — R2-backed delivery with embedded provenance Economic Modules — Deal Board, Bounty Marketplace, and LifeCoin concept Distribution Model — Multi-platform strategy across Zenodo, GitHub, Ko-fi, Discord, and social channels Novelty & Originality Claims — Five defensible firsts IP Positioning — Defensive publication via Zenodo DOI and CC BY 4.0 Threat Model — Seven attack vectors with mitigations Limitations — Honest constraints Future Work — Roadmap including IPFS, formal specs, ISMIR submission, ZK proofs References — Academic citations (FIPS 180-4, MCP, C2PA, W3C DIDs, etc.) Appendices — SHA-256 receipt example, agent identity schema, orchestrator API endpoints https://zenodo.org/records/18131984 (C T K L T) Core: https://acbeatz.com/n-eyes https://acbeatz.com https://github.com/acbeatz https://orcid.org/0009-0003-3846-9082
As unmanned aerial vehicles (UAVs) become increasingly integral in domains such as agriculture, logistics, and military operations, secure cross-domain authentication mechanisms are essential. Existing centralized protocols are prone to single points of failure, privacy vulnerabilities, and physical capture risks. This paper presents a novel blockchain-based, privacy-preserving authentication protocol for UAVs operating across multiple domains. By combining zero-Knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) and physical unclonable functions (PUFs), the proposed protocol ensures secure identity verification without disclosing sensitive information. The blockchain platform offers a decentralized, tamper-resistant environment for UAV authentication, addressing the challenges of scalability, privacy, and security in cross-domain operations. We demonstrate the security and effectiveness of the protocol through formal and informal security proofs and performance evaluations. The results indicate that the proposed protocol outperforms traditional methods, achieving significant reductions in both computational and communication costs while maintaining high security standards.
The continuous digitization of the modern farming sector demands secure, intelligent, privacy-preserving, and scalable infrastructures for real-time data analysis. However, existing smart farming systems face significant challenges, including cyberthreats, data authenticity issues, and the need for reliable decision support. This article proposes a secure Digital Twin (DT) architecture for smart agriculture recommendation systems, integrated with Blockchain and Federated Gated Recurrent Units (FGRU). At the perception layer, IoT sensors monitor soil, crop, and environmental data, which is gathered by a Request Control Authority (RCA) and transmitted to local models. To ensure privacy, a GRU-based Federated Learning (FL) approach is employed to detect cyberattacks—such as Sybil, Man-in-the-Middle (MITM), DDoS, and Replay attacks—without exposing raw decentralized data. Furthermore, a Blockchain-assisted Zero-Knowledge Proof-based Authority (ZKPA) mechanism is integrated to ensure data authenticity. The validated farming data is stored at the architecture’s final layer, enabling a Physical Twin to monitor real-time processes and generate precise recommendations. The architecture was evaluated using a paddy field dataset (26 features, 10,081 samples). Experimental results show that the proposed federated GRU model achieves perfect detection performance for all considered attacks, while the ZKPA-based authentication mechanism achieves a 98–99% authentication success rate with sub-10 ms verification time and only 15–25% additional computational overhead, which is better than existing works.