Tsvetelina Ivanova, L Koleva, Idilia Batchkova, G Kolev
Abstract Reliable vacuum control in high-precision installations such as the Electron Beam Melting and Refining (EBMR) plant requires an intelligent architecture that integrates physical subsystems and cyber entities under an adaptive control framework. This paper proposes a multi-agent system (MAS) representation of the EBMR vacuum creation subsystem, developed using the Organizational Multi-Agent Systems Engineering (O-MaSE) methodology. The model unites the IEC 61512 (S88) batch-process standard with the IEC 61499 distributed-control architecture to form a modular and interoperable cyber-physical system (CPS). Each pump, valve, and sensor is modelled as an autonomous agent with defined goals, roles, and communication protocols. The O-MaSE-based design enhances scalability, fault tolerance, and system adaptability, enabling decentralized decision-making and efficient vacuum regulation. The integrated case study demonstrates that MAS-based CPS design substantially improves the responsiveness and resilience of EBMR operations, supporting the principles of Industry 4.0.
The increasing deployment of Agricultural Internet of Things (Ag-IoT) systems is transforming food production and enabling climate-resilient farming practices.However, the growing reliance on interconnected sensing, automation, and cloud platforms significantly expands the attack surface, exposing agricultural operations to cyber threats that can disrupt critical processes, compromise data integrity, and undermine food security.This paper explores adaptive cybersecurity mechanisms designed to enhance the resilience of Ag-IoT ecosystems operating under climate-induced environmental and network constraints.The proposed approach integrates context-aware access control, federated threat learning, zero-trust architectures, and distributed ledger technologies to secure dataflows, device interactions, and supply-chain processes.Experimental evaluations and simulated farm scenarios demonstrate improved attack detection, operational continuity, and system reliability during extreme weather events and adversarial conditions.The results suggest that adaptive cybersecurity strategies are essential for protecting next-generation digital agriculture and ensuring resilient, secure, and sustainable food systems in an era of accelerating climate variability.
Abstract Integrating third-party Machine Learning (ML) models into industrial Operational Technology (OT) creates a procurement deadlock: operators cannot verify vendor performance claims without exposing sensitive operational data, while vendors refuse to reveal proprietary model weights before purchase, rendering traditional safeguards such as Non-Disclosure Agreements technically unenforceable. This paper introduces a framework combining Zero Knowledge Proofs (ZKPs) with smart contracts to enable trust-minimized, privacy-preserving competitive model procurement in Industrial Cyber-Physical Systems (ICPS). Our framework allows vendors to cryptographically prove that their model outperforms a legacy baseline without disclosing proprietary weights, a process we term cryptographic performance attestation . The on-chain workflow combines escrow-backed procurement, automated proof verification, and best-vendor selection with arbiter-based dispute resolution. We analyze three distinct ZKP workflow variations for industrial suitability and evaluate their performance on consumer-grade hardware, achieving proving times of approximately three seconds and sub-dollar on-chain verification costs under Layer-2 fee assumptions for the recommended single-proof variation. Results demonstrate the feasibility of pre-deployment model verification while identifying computational trade-offs of recursive proof aggregation. The entire verification phase operates offline with no impact on real-time OT control paths, bridging the IT/OT pre-transaction trust gap while deferring artifact deployment to existing OT tooling.
This paper introduces a novel decentralized autonomous organization (DAO) framework for microgrid governance, specifically targeting diverse stakeholder ownership. It integrates principles of decentralization, democratization, and digitization to streamline the just energy transition. The multifaceted model synthesizes key DAO mechanisms with microgrid elements by incorporating tokenomics, fund management, actor reputation, decision-making, tender selections and dispute resolutions. Preliminary conceptual validation in Python case studies demonstrates the feasibility of the approach. However, further research and validation are needed to pave the way from centralized structures to a more empowered, equitable and resilient future.
With the increase in the rate of cyber threats, such as ransomware, social engineering, and zero-day exploits, it is urgent to adopt new security mechanisms like Security Orchestration, Automation, and Response (SOAR) systems. The increase in cyber threats has not only amplified in frequency but also in sophistication. This escalation has forced organizations to rethink traditional defense strategies. SOAR has shown itself to be an important solution by automating repetitive tasks and helping security teams in focusing on strategic threat hunting as well as mitigation. The integration of AI and ML in SOAR frameworks helps in predictive analytics, in which systems can anticipate potential breaches based on pattern recognition from vast datasets. The role of blockchain is to enhance data integrity and help enable secure and decentralized threat intelligence sharing between stakeholders. This paper presents a systematic literature review (SLR) on recent advancements in SOAR technologies, especially the incorporation of artificial intelligence (AI), machine learning (ML), and blockchain; it also reviews case studies across various industry sectors, such as healthcare, finance, industrial control systems, and critical infrastructures, as well as the challenges facing SOAR adoption. By examining 29 studies from academic research, industry case studies, and technical reports, the review synthesizes methodologies, architectures, and performance outcomes to summarize the current state of SOAR systems. The research found that SOAR can significantly reduce incident response times and improve threat detection accuracy, with findings indicating that SOAR can lower response times by up to 80% compared to legacy systems, although implementation costs may reach as high as $5 million. Additionally, specialized personnel are still needed to operate these systems. The skills gap increases barriers to adoption, as few professionals possess expertise in cybersecurity as well as in automation tools. Future directions emphasize developing hybrid models that blend human intuition with machine efficiency for more robust defenses. Finally, the review discusses future research directions to help SOAR further scale, interoperate across platforms, and enable autonomous decision-making
Slow Liquidity Drain (SLID) scams have recently emerged as a subtle and persistent threat within the decentralized finance (DeFi) environment. While prior studies have introduced heuristic and machine learning techniques for identifying SLID behaviors, deploying these methods in real-world industrial systems reveals substantial challenges. In particular, updated large-scale datasets collected from operational DeFi platforms show that SLID behaviors and their effective detection time-range evolve over time, rendering previously reported fixed thresholds unreliable for production use. This work presents a data-driven reassessment of SLID detection under contemporary DeFi conditions and demonstrates that the observation window required for reliable detection shifts as new data and new scam behaviors emerge. Building on these findings, we introduce an industry-oriented detection framework that decouples machine learning models from time-range selection and supports adaptive operation without retraining or feature redesign. Rather than proposing a single deployment strategy, we outline two practical operating modes: a slow-adaptive mode that prioritizes stability and auditability through periodic window updates, and a fast-adaptive mode that enables flexible sensitivity and tiered alerts for security-driven environments. Together, these designs translate empirical insights into concrete system architectures suitable for large-scale DeFi monitoring, bridging the gap between academic SLID detection research and production deployment requirements.
As artificial intelligence (AI) systems grow more powerful, autonomous, and embedded in critical infrastructure, their identification and traceability become foundational to regulatory oversight and sustainable digital governance. In digitally transformed enterprises, long-term sustainability depends on transparent, accountable, and lifecycle-governed AI systems, all of which require verifiable identity. This study proposes a conceptual and architectural framework for AI identification, combining technical and governance mechanisms to support lifecycle accountability. The framework integrates five components: model fingerprinting, cryptographic hashing, blockchain-based registration, zero-knowledge proof (ZKP)-based proof of possession, and post-deployment structural change screening. We introduce a dual-layer identifier, consisting of a machine-verifiable primary hash and a human-readable secondary identifier, anchored in a tamper-resistant registry. Identity validation is supported by selective ZKP-based verification at governance-defined checkpoints, while post-deployment changes are monitored using Lempel--Ziv Jaccard Distance (LZJD) as a governance-oriented screening signal rather than a semantic performance metric. The framework establishes an enforceable and transparent identity infrastructure that enables continuity, auditability, and policy-aligned oversight across AI system lifecycles. By embedding AI identification within enterprise architecture and governance processes, the proposed approach supports sustainable innovation, strengthens institutional accountability, and provides a foundation for selective, policy-defined verification during digital transformation.
v2: Corrected affiliation domain to pastoral.tech. This paper presents a unified framework for anticipatory cyber defense integrating eight convergent dimensions: adversarial machine learning countermeasures, supply chain and hardware implant analysis, quantum threat transition analysis, attribution resistance with deepfake forensics, autonomous defense game theory, zero-knowledge proof systems for operational security, temporal correlation at scale, and biological-physical security integration. We formalize the Mantis autonomous defense environment as a Gymnasium-compatible reinforcement learning system with self-play training, introduce Chameleon, a five-channel defensive steganography framework using dynamic key rotation and Shamir Secret Sharing, and develop a ZK-Evidence Ledger for cryptographic evidence chains with Merkle tree notarization and Circom-based inclusion proofs. The convergence of these systems produces an anticipatory architecture where offensive research (Helix synthetic organization detection), defensive operations (Mantis game-theoretic simulation), and attribution resistance (zero-knowledge Merkle proofs) form a closed operational loop.
Open access
4 source records
Adversarial Robustness in Machine Learning
Physical Unclonable Functions (PUFs) and Hardware Security
This research presents the design and implementation of the Decentralized Smart City of Things (DSCoT), a novel framework leveraging Web3 architecture to enhance the security and authentication of assets in cyber-physical systems (CPSs) for smart cities on a private blockchain. Unlike traditional non-fungible tokens that primarily identify and distinguish financial assets, existing approaches lack robust mechanisms for attributing and authenticating CPS assets such as owners, users, and IoT-enabled smart devices. DSCoT addresses this gap by introducing an extended ERC721 protocol, enabling IoT-enabled devices to have unique blockchain identities similar to user accounts, which enhances device management and tracking. Novel smart contract modules facilitate secure identification and authentication of CPS assets. Evaluated results on a private Hyperledger Besu blockchain show that DSCoT achieves significant performance improvements, including sub-second latency (∼0.1–0.5 s for application programming interface (API) calls), minimal transaction costs (∼0.01–0.05 USD), and a high processing capacity (∼1000 transactions per second (TPS)). These results, along with improved security through immutable authentication records, demonstrate DSCoT’s effectiveness as a scalable and secure solution for smart city CPSs.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
The transition from centralized power systems to decentralized infrastructures with a high share of renewable energy sources calls for reliable settlement in P2P electricity trading across “smart” regions. Blockchain platforms can enhance transparency and facilitate automated settlement; however, double-spend attacks still pose a threat to transaction finality and, consequently, undermine trust in the payment layer. This paper quantifies this risk through a probabilistic analysis of classical double-spend scenarios for Proof-of-Work (PoW) and Proof-of-Stake (PoS) blockchains augmented with periodic checkpoints, which render the chain history prior to the latest checkpoint effectively irreversible. We develop attack models for both consensus mechanisms and derive explicit formulas for the attacker’s success probability as a function of the adversarial share, the spacing between checkpoints, and the number of confirmation blocks. On this basis, we compute the minimum confirmation depth needed to satisfy a predefined risk threshold. Numerical evaluation using the derived expressions shows that checkpoints consistently reduce double-spend probability relative to checkpoint-free baselines; in the evaluated settings, the reduction reaches up to 44% and becomes more pronounced as the adversarial share increases. Finally, the analysis yields practical guidance for energy trading applications: accept a payment after the computed number of confirmations when it fits within a single checkpoint interval; otherwise, treat finality as reaching the next checkpoint.
Yuan Chang, Tom H. Luan, Jinkai Zheng, Yinuo Li · 5 authors
The rapid adoption of electric vehicles (EVs) has created new opportunities for decentralized energy trading, where EVs can act as mobile energy providers in peer-to-peer markets. Blockchain provides a secure foundation for such systems, ensuring trust and accountability. However, its inherent transparency creates privacy risks, as it enables the tracking of trading activities. Existing privacy-preserving mechanisms typically focus on concealing payment transactions but often expose other critical interactions, such as matching coordination. To address these challenges, we proposePriVET, a privacy-preserving framework for vehicular energy trading. PriVET leverages smart contracts for trade matching and uses an enhanced Paillier encryption scheme to support encrypted comparisons, ensuring secure coordination without revealing sensitive data. Additionally, a Bloom-filter– based Geohash encoding is used to protect location privacy during spatial matching. We evaluate PriVET through both theoretical analysis and practical experiments. In a simulation environment, the transaction computation time for 100 vehicles is shown to be under 30ms, with communication overhead kept below 20KB. These results demonstrate that PriVET provides robust privacy protection while maintaining minimal overhead, making it a practical solution for real-world blockchain-based energy trading scenarios.
The rapid proliferation of smart home IoT devices has introduced unprecedented cybersecurity vulnerabilities, necessitating scalable and privacy-preserving intrusion detection systems (IDS). Federated Learning (FL) offers a promising decentralized approach by training models locally without sharing raw data, but it remains susceptible to poisoning attacks and relies on a vulnerable central aggregator. This paper presents a novel blockchain-enhanced FL framework tailored for smart home IDS, integrating multiple consensus mechanisms—Proof-of-Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and Proof-of-Authority (PoA)—for the first time in this context. Our approach uniquely combines differential privacy (DP) and secure aggregation (SA) within a blockchain-managed workflow to mitigate gradient inversion and membership inference attacks while ensuring tamper-resistant, decentralized trust. Experimental evaluation using the N-BaIoT dataset demonstrates that the proposed system achieves up to 88.3% detection accuracy with manageable latency (~200 ms/round) and formal privacy guarantees ($\varepsilon$=1.0 DP). The framework introduces 52.8% system overhead compared to vanilla FL—a reasonable trade-off for enhanced security and privacy. This work establishes a robust, transparent, and scalable security infrastructure for smart homes, effectively addressing the limitations of both centralized and conventional FL-based IDS.
The integration of IoT technology in smart grids has revolutionized the energy sector by enabling decentralized energy production, real-time monitoring, and peer-to-peer energy trading. However, these advancements introduce challenges such as ensuring security, scalability, and data privacy, which are critical for the reliable operation of IoT-enabled smart grids. Blockchain technology has emerged as a promising solution to address these challenges by providing decentralized, secure, and transparent frameworks for managing energy transactions. This study aims to explore the application of blockchain in enhancing the security and scalability of IoT-enabled smart grids while addressing challenges related to resource limitations and privacy concerns. Simulation and experimental analyses were employed to evaluate blockchain performance in a decentralized energy network. The study focused on key metrics: latency, transaction throughput, energy consumption, and data integrity. The study shows Proof of Authority (PoA) excels in IoT smart grids with < 200 ms latency, 190 Tx/s throughput, and 0.5–0.9 J/Tx energy use—outperforming PoW (450-780 ms, 5.2–10.3 J/Tx). While Proof of Stake (PoS) offers competitive 0.3–0.7 J/Tx efficiency and higher 210 Tx/s scalability, its latency (150–300 ms) remains slightly higher than PoA. These results position PoA as ideal for resource-constrained IoT nodes, while PoS better suits more extensive networks needing higher throughput. The findings highlight how consensus mechanisms can be tailored to different smart grid requirements, with PoA providing the best balance for most decentralized energy applications. Additionally, blockchain's immutable ledger ensured zero unauthorized data modifications, enhancing data security and transparency. The practical implementation of these results highlights blockchain's potential to transform IoT-enabled smart grids. By reducing security vulnerabilities and operational inefficiencies, blockchain enables secure and efficient peer-to-peer energy trading and enhances the resilience of decentralized energy systems. Future work should optimize scalability beyond 500 nodes and integrate advanced privacy-preserving mechanisms to ensure the widespread adoption of blockchain in innovative grid applications.
Niomi Langaliya, Vinay Thakor, Purna Tanna, Disha Shah
This research preprint presents Aegis, a zero-knowledge-proof-based security paradigm designed to mitigate validator-compromise attacks in cross-chain bridges. The work empirically evaluates a ZKP-based withdrawal verification mechanism against an optimized multi-signature validator model under controlled conditions, demonstrating complete resistance to unauthorized fund transfers at the cost of increased Layer 1 gas consumption. The study introduces the concept of the cost of trustlessness as an empirically derived techno-economic metric and provides quantitative justification for migrating cryptographic verification to Layer 2 environments. This work was previously presented at FINCON’25, National Forensic Sciences University (NFSU), Gandhinagar, India. This version is released as a non-peer-reviewed research preprint for open dissemination and citation. Journal submission is in progress.
Open access
2 source records
Physical Unclonable Functions (PUFs) and Hardware Security
AbstractContemporary blockchain architectures face a critical impasse defined herein as the "Tetra-Lemma"—a four-dimensional optimization problem comprising decentralization, security, scalability, and thermodynamic sustainability. Proof-of-Work networks confront diminishing security budgets, while Proof-of-Stake systems risk validator centralization. This paper presents a Unified Monetary-Supply Framework designed to resolve these structural conflicts. By deriving a closed-form solution for supply dynamics that integrates a deterministic "Customized Halving Mechanism" with probabilistic asset attrition models, we demonstrate a mathematical convergence that maintains thermodynamic security over a secular horizon. Key Quantitative Findings: Asymptotic Convergence: While effective circulating supply may experience a temporary peak (approx. 27 million RIN), all evaluated models are engineered to stabilize below the 21 million threshold (specifically converging to 20.88 million RIN). Secular Stability: The framework secures a deflationary emission schedule mirroring Bitcoin’s scarcity model over a multi-century horizon of 443–703 years. Publication Status & RoadmapThis manuscript (v1.5.0) is maintained as a Living Research Document. It serves as the foundational theoretical framework for the Rincoin protocol. Future iterations will formalize the consensus mechanisms required to govern these algorithmic parameters. Integrity & Provenance ArchitectureThe scientific integrity and existence of this document are secured by a Triple-Verification Layer: 1. Academic Provenance: Indexed via Zenodo (DOI: 10.5281/zenodo.17141922). 2. Thermodynamic Timestamping: Anchored to the Bitcoin blockchain via OpenTimestamps. 3. Identity Assurance: Digitally signed by the author via a third-party certification authority (GMO Sign). Note: Verification data and the "Certificate of Authenticity" are available in the supplementary files. CorrespondencePrimary Author: Michiru Tokino (also known as Aevust in the decentralized infrastructure community). Academic Inquiries: edu@aevust.org Community Governance: @aevustus (Discord) / @aevust (X/Telegram)
As the United States Department of Defense (DoD) transitions toward Zero-Trust Architecture, the hardware and software supply chain remains a critical vulnerability. Current provenance models rely on centralized, siloed databases that lack the transparency required to counter sophisticated state-sponsored interdiction. This paper proposes a novel framework: AI-Enhanced Trust Graph Analytics over Distributed Ledgers. The architecture utilizes a permissioned Distributed Ledger Technology (DLT) substrate to host an immutable record of component lifecycles, anchored by Hardware Roots of Trust (RoT) through Physically Unclonable Functions (PUFs). By mapping silicon fingerprints to Software Bill of Materials (SBOM), the system constructs a multi-dimensional Trust Graph. We employ Graph Neural Networks (GNNs) to detect structural anomalies indicative of subversion, while Federated Learning enables inter-agency intelligence sharing without compromising operational security. Our findings demonstrate that this integrated approach significantly reduces the time to detect compromised assets in air-gapped and tactical environments, providing a strategic roadmap for an autonomous, self-healing supply chain.
Open access
3 source records
Physical Unclonable Functions (PUFs) and Hardware Security
We present the first application of algebraic topology to smart contract vulnerability detection, demonstrating that reentrancy vulnerabilities correspond to non-trivial first cohomology classes ($H^1 \neq 0$) in the contract call graph. Using the Eden Scanner's Hensel obstruction test, we identified a critical vulnerability in the Inverse Finance FiRM Convex sDola-scrvUSD Market contract (address \texttt{0x63D27fC9d463Ed727676367D3F818999962737E8}) within 48 hours of its addition to the Immunefi bug bounty scope. The vulnerability affects approximately \$605,500 in total value locked and enables direct theft of user collateral through a reentrancy attack via the \texttt{liquidate()} $\to$ \texttt{escrow.pay()} $\to$ callback path. We provide mathematical proof of exploitability through the Regularization Theorem and validate with a fork test against Ethereum mainnet. \end{abstract}
The rapid digitalization of the energy sector and the growth of distributed energy resources have exposed the limitations of traditional centralized energy management and trading models. This shift has created a need for more flexible, transparent, and user-focused solutions. Blockchain technology addresses these needs by enabling secure, traceable, and direct transactions through a decentralized and immutable record system. Peer-to-peer energy trading platforms on the public Ethereum network, for example, allow producers and consumers to exchange energy securely without intermediaries. This study presents a blockchain-based system architecture for peer-to-peer energy distribution and trading, known as the Decentralized Energy Management System (DEMS). The system is built on a permissioned Ethereum blockchain (PEDNET) using the Istanbul Byzantine Fault Tolerance (IBFT 2.0) consensus mechanism, and automates energy exchanges and payments using smart contracts, which enable secure, auditable, and traceable transactions through the use of energy tokens. An artificial intelligence-powered decision support module comprising three specialized neural network models has also been integrated to optimize users' energy purchasing preferences, achieving approximately 90% recommendation quality. The system has been validated through comprehensive testing with 500 simulated users over a 3-month period, demonstrating a 32% reduction in average transaction time and an 18% increase in user satisfaction compared to non-AI baselines. Performance benchmarking shows sub-2-second transaction finality and throughput exceeding 500 TPS on the PEDNET network. The study also addresses security considerations, regulatory compliance requirements, and provides a detailed cost analysis of smart contract operations. The study demonstrates the practical impact of combining blockchain and artificial intelligence technologies in P2P energy systems.
We prove that first homology of the control flow graph provides a complete characterization of reentrancy vulnerability in smart contracts. Specifically, we establish the Homological Reentrancy Theorem: a contract admits a reentrant execution path if and only if H₁(G) ≠ 0, where G is the extended control flow graph incorporating external call returns. We prove soundness (no false negatives) and completeness (no false positives) for contracts satisfying a non-degeneracy condition. For multi-contract systems, we apply the Mayer-Vietoris exact sequence to compute H₁ of the composed system from individual components, enabling detection of cross-contract reentrancy. We validate empirically against 17 known exploits including The DAO (2016), Parity Wallet (2017), and Cream Finance (2021), achieving 100% detection with zero false positives.