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.
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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.
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.
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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
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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.
Digital twins are digital representations that enable real-time monitoring, analysis, andprediction of outcomes of physical systems. They depend on continuous communicationto work, which increases the attack surface of the system and introduces security risks,especially regarding unauthorized access to digital twin data and operations. This thesisinvestigates how blockchain-based smart contracts can be used as an authorization mech-anism for a digital twin, by implementing a digital twin for a Crazyflie 2.1 and controllingaccess to it through a smart contract-based authorization layer.A prototype of this system was developed using Python and connected to the physicalUAV using the Crazyradio interface. Flight data was collected and used to identify a sim-plified digital twin representing the vertical subsystem. A blockchain-based authorizationlayer with role-based permissions was then implemented using Solidity smart contracts ina local Hardhat environment.The findings from this thesis show that such a system is feasible to implement. Flighttest runs show that the twin remained numerically stable at all times and estimated thephysical UAV’s state with bounded error. The authorization mechanism enforced the de-fined role-based access-control rules in the tested scenarios, with measured authorizationlatency in the local environment around 14–15 ms. Gas measurements were also used toestimate the relative computational cost of the smart contract operations.
Smart contract security research has historically emphasized exploit-driven threat models developed for open, permissionless blockchain environments. While effective for identifying adversarial attacks and loss-of-funds scenarios, these models are insufficient for institutional blockchain systems supporting Real-World Assets (RWAs). In regulated and asset-backed deployments, threats frequently arise from non-adversarial actors, design assumptions, operational dependencies, and compliance enforcement failures rather than from direct malicious exploitation.This paper presents a structured threat taxonomy tailored to institutional RWA smart contract systems. The taxonomy classifies threats according to origin, intent, capability, and impact, explicitly incorporating adversarial, non-adversarial, and systemic threat sources. By decoupling threat analysis from exploit-centric assumptions, the taxonomy enables correctness-oriented risk assessment, formal specification of threat boundaries, and alignment with institutional audit and compliance requirements. The proposed framework provides a foundation for secure system design, verification, and operational governance in regulated blockchain environments.
Reentrancy remains one of the most persistent and damaging vulnerabilities in Ethereum smart contracts, enabling adversaries to recursively drain funds despite the presence of static and runtime defenses. Existing studies mainly focus on detection or program analysis, but they do not explain why and under what conditions attackers decide to exploit. In this work, we introduce a decision-theoretic framework that models reentrancy as a profit-maximizing problem under gas, risk, and atomicity constraints. Our framework derives the conditions under which reentrancy attacks are economically viable and provides an algorithm for computing optimal exploit strategies. We further extend the analysis to multi-contract attacks, capturing sequential, parallel, and optimized execution strategies. A simulation environment evaluates profitability across varying balances and gas configurations, highlighting thresholds where attacks become infeasible. Finally, we translate attacker decision profiles into practical defense recommendations for developers, auditors, and DeFi system designers. This framework bridges the gap between exploit detection and adversarial economics, offering a rigorous basis for strengthening smart contract security. This framework establishes a theoretical baseline for adversarial economics in smart contract security, forming a foundation for future MEV-aware exploitability models and Layer-2 risk analysis.
<b><i>Bribe-Based Oracle Echoes</i></b> describe a class of incentive-layer attacks in which economically rational adversaries influence oracle participants to repeatedly reinforce distorted data outputs without directly compromising oracle infrastructure. Unlike overt oracle manipulation, these attacks exploit incentive alignment and tolerance-band logic to create feedback loops in which biased signals are echoed, amplified, and normalized across decentralized finance (DeFi) and stablecoin systems. This paper formalizes the threat model, identifies structural enablers, and demonstrates why decentralization alone is insufficient to prevent economically induced oracle drift.
Ambati Satya Sai Vaishnavi, M. Veera kumari, K. Akash Sai, G. Pavan Kiran · 7 authors
Peer-to-peer (P2P) energy trading has emerged as an innovative solution to modern energy challenges by enabling decentralized electricity exchange among users. The Small-scale market allows prosumers to sell excess energy directly to consumers without relying on centralized authorities. Blockchain ensures transparency, security, and immutability of transactions, while smart contracts automate trading operations based on predefined conditions. A MATLAB-based simulation environment is developed to model energy generation, consumption, and transaction processes, along with a digital ledger for recording trades. The results of different case studies demonstrate efficient energy utilization, reduced transaction costs, and improved reliability. The system promotes renewable energy adoption and supports the transition toward decentralized smart grids. This work highlights the feasibility of integrating blockchain technology with energy systems for sustainable and scalable power trading solutions.
The increasing complexity of cyber threats across IoT-cloud infrastructures necessitates the use of innovative, flexible, and confidentiality-preserving prevention techniques. The Blockchain-Assisted Hybrid Attention-Based Intrusion Detection and Access Control System (BHA-IDACS) is presented in this paper. The primary detection module employs an Adaptive Spatio-Temporal Representation Architecture-Self-Attention and Intersample Attention Transformer (Astra-SAINT) to precisely detect evolving intrusion tendencies. A heron optimization algorithm (HOA) is utilized for tuning the model thereby improving accuracy of detection and convergence. Fully Homomorphic Encryption (FHE) maintains the security of data and storage of encrypted data in unsecured cloud and blockchain circumstances. On a Consortium Blockchain, all encrypted transactions and audit trails are maintained by a Proof-of-Stake Authority (PoSA) consensus method. Additionally, based on user behavior and trust level, Smart Contract-Based Dynamic Access Control independently enforces permission and authentication regulations. The suggested model provides better precision, recall, F1-score, F2-score, specificity, and Cohen's Kappa values in addition to a mean accuracy of 99.16%. Furthermore, statistical analysis using confidence intervals and low standard deviation values demonstrates that Astra-SAINT is reliable and consistent across all validation folds. These results demonstrate the efficacy of the suggested Astra-SAINT framework as a scalable and dependable intrusion detection method for protecting IoT environments of the next decade.
Blockchain-based financial systems increasingly intersect with regulated domains, including stablecoins, real-world asset (RWA) tokenization, decentralized finance (DeFi), decentralized autonomous organizations (DAOs), and ESG-linked financial instruments. Existing blockchain insurance and underwriting models rely predominantly on probabilistic risk pricing derived from historical data, oracle-fed inputs, and machine learning inference. While sufficient for limited-scale applications, these approaches exhibit structural limitations when applied to high-volume, regulation-intensive systems. This paper demonstrates that probabilistic risk pricing alone imposes a fundamental scalability ceiling, as residual risk grows unbounded with system volume. We introduce a control-oriented risk mitigation framework based on the Crystal Validator (CV), which enforces execution-level compliance constraints prior to transaction finalization. By reducing compliance entropy through deterministic validation, CV bounds residual risk independently of transaction volume. We formalize this distinction using control theory, information theory, and cyber-physical systems (CPS) principles, and show why improved machine learning alone cannot resolve these limitations. The results establish control-oriented validation as a necessary architectural primitive for sustainable blockchain insurance and regulated on-chain finance.