We demonstrate that the binary payload of the "A Sign In Space" signal (data17square.bin, 8192 bytes) contains a self-referential algebraic structure â a mathematical quine. Through a systematic reverse-engineering and cryptanalytic approach, starting from the raw file as the sole axiom, we derive a chain of algebraic objects over the finite field GF(625): 48 field elements, a 42-amino-acid protein sequence, an elliptic curve, and amino acid coordinate values. The curve parameters recovered from the protein are identical to those derived from the field's primitive element, closing a self-referential loop. The cryptanalysis combines finite field arithmetic, BerlekampâMassey LFSR analysis, elliptic curve theory, and Margolus cellular automaton reverse-engineering to recover the hidden algebraic structure without any prior knowledge of the encoding scheme. The derived protein is validated by Boltz-2 (AlphaFold3 architecture) structure prediction at three levels of assembly (monomer, homodimer, homotrimer), cross-validated with ESMFold (RMSD = 1.10 Ă ), and refined with OpenMM (Amber ff14SB). The monomer forms a single alpha-helix with pLDDT = 92.3 and 100% Ramachandran-favored geometry. The homodimer produces a coiled-coil â the most ancient structural motif in biology. The protein uses exactly the five prebiotic amino acids (A, D, E, L, V) with a perfect 21/21 charged/neutral symmetry. Null hypothesis testing (120 alternative inputs, 0 quines produced) and sensitivity analysis (the quine breaks with any single parameter change: 1/150 polynomials, 1/3 step counts, 98/100 bit flips destroy it) confirm the structure is not an artifact of the analysis pipeline. The conservative probability of chance occurrence is approximately 5 Ă 10â»Âčâč; under uniformity assumptions, approximately 10â»â·â¶. Companion Python scripts (quine_proof.py, verify_123.py) verify all 123 algebraic properties with zero failures. All code and data are provided for full reproducibility. -- Additional notes : This is a preprint resulting from independent reverse-engineering and cryptanalysis of the "A Sign In Space" signal, a simulated extraterrestrial message transmitted by ESA's ExoMars Trace Gas Orbiter in May 2023. The analysis is fully reproducible: running "python3 quine_proof.py data17square.bin" derives every intermediate value from the raw binary file and verifies 47 core assertions with zero failures. The extended script "verify_123.py" checks all 123 algebraic properties. Structure predictions were performed on an NVIDIA RTX 5090 GPU (32 GB VRAM) using Boltz-2 v2.2.1 (AlphaFold3 architecture, maximum precision: 20 recycling cycles, 500 diffusion steps, 20 samples), ESMFold v1 (cross-validation), and OpenMM 8.5 (Amber ff14SB force field, GBn2 implicit solvent, energy minimization + 10 ns molecular dynamics at 300 K). No prior knowledge of the signal's encoding scheme was assumed. The algebraic structure was discovered through systematic cryptanalytic techniques including finite field enumeration, LFSR analysis, elliptic curve point counting, and exhaustive parameter space exploration. If you use any part of this work (data, code, results, figures, or methods), please cite: Lacoche, E. (2026). "A Self-Referential Algebraic Quine in the A Sign In Space Signal." Zenodo. doi:10.5281/zenodo.19218629
Current AI deployment stacks authenticate agents, workloads, and credentials but do not verify which neural network is computing at inference time. Recent incidents â including the undisclosed use of an open-weight foundation model inside a commercial product, industrial-scale distillation campaigns, and emerging agent identity standards that authenticate software without authenticating models â show that this gap has practical consequences. Post-hoc disclosure resolved these incidents; runtime proof would have made the model identity question answerable at inference time. This paper asks whether runtime model identity is technically feasible at frontier scale. We present three results. First, we enrolled and verified five open-weight transformer models spanning 8 billion to 72.7 billion parameters across three families, with zero false acceptances in all pairwise comparisons and self-verification within the acceptance threshold for all models. A thermodynamic observable predicted by extreme value theory remained within two percent of its predicted value across the full range, with no statistically significant scale-dependent correction detected across more than two orders of magnitude in parameter count. Second, we tested structural separability on three declared-lineage distillation pairs spanning 8 billion to 70 billion parameters â each derivative sharing identical architecture with its base â and measured separations ranging from 2,858 to 4,583 times the acceptance threshold, increasing monotonically with model scale across two base-model families. All derivatives self-verified within the acceptance threshold. Third, we demonstrate a frontier-scale software attestation path â including signed JWT issuance and downstream policy consumption â and situate it within a previously formalized attestation architecture that composes with enterprise identity infrastructure, complementing rather than replacing current agent identity frameworks. These results demonstrate that runtime model identity is measurable and separable across the tested range of open-weight instruct-tuned transformers from 8B to 72.7B, with a frontier-validated software attestation path and an inherited route to stronger hardware-backed and proof-backed assurance. The Neural Network Identity Series â Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Newest addition: Technical Note: The Disappearing Window â AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Paper 1: The ÎŽ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks â Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? â Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity â Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) Paper 13: Safety-Alignment Removal as a Model-Identity Failure â Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019) Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883) Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848) Technical Note: Artifact Identity Is Not Runtime Identity â Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).
The advent of 5G networks has introduced a paradigm shift in communication infrastructure, facilitating ultra-low latency and high-speed data transmission. Despite this, this progress is accompanied by a spike in diverse and sophisticated cyberattacks, for which there is no comprehensive, foolproof defence strategy. In order to address the Scalability Trilemmaâachieving decentralization, scalability, and trustâand security concerns, this study proposes a robust security framework that combines blockchain technology with Zero Trust Architecture (ZTA). The proposed framework presents an end-to-end coherent workflow in four successive stages: (i) Access Request Initiation with contextual metadata, (ii) Decentralized identity verification via blockchain-based Decentralised Identifiers (DIDs) and Verifiable Credentials (VCs), (iii) Context-aware Dynamic Access Control enforced through smart contracts, risk scoring, and cryptographic mechanisms such as Zero Knowledge Proofs (ZKPs) and Multi-Factor Authentication (MFA), and (iv) Time-bound, least-privilege access provisioning with continuous session monitoring and immutable logging. The model, which is proposed to be strategically implemented at the 5G network's device (access) layer, affirms real-time enforcement while maintaining accountability, privacy, and verifiability. Our research delivers a fully decentralized, tamper-resistant, and scalable architecture capable of dynamically mitigating advanced cyber threats, while ensuring secure delivery of 5G services across diverse use cases.
The increasing penetration of intermittent renewable energy demands innovative solutions to maintain grid stability, resilience, and security in the body of smart cities. This paper presents a novel framework that redefines Bitcoin mining as a form of virtual energy storage, a flexible and controllable load capable of delivering large-scale demand response services, positioning it as a competitive alternative to traditional energy storage systems, including electrical, mechanical, thermal, chemical, and electrochemical storage solutions. By strategically aligning mining activities with grid conditions, Bitcoin mining can absorb excess electricity during periods of oversupply, converting it into digital assets, and reduce operations during times of scarcity, effectively emulating the behavior of conventional energy storage systems without the associated capital expenditures and material requirements. Beyond its operational flexibility, this paper explores the cyberâphysical benefits of integrating Bitcoin mining into the power transmission systems as a defensive mechanism against false data injection (FDI) cyberattacks in smart city infrastructure. To achieve this goal, a decentralized and adaptive control strategy is proposed, in which mining loads dynamically adjust based on authenticated grid-state information, thereby improving system observability and hindering adversarial efforts to disrupt state estimation. In addition, to handle the proposed approach, this paper introduces a high-performance algorithm, a combination of quantum-augmented particle swarm optimization and wavelet-oriented whale optimization (QAPSO-WOWO). Simulation results confirm that strategic deployment of mining loads improves grid sustainability by utilizing curtailed renewables, enhances resilience by mitigating load-generation imbalances, and bolsters cybersecurity by reducing the impacts of FDI attacks. This work lays the foundation for a transdisciplinary paradigm shift, positioning Bitcoin mining not as a passive energy consumer but as an active participant in securing and stabilizing the future power grid in smart cities.
The enactment of the Nigeria Tax Act (NTA) 2025 represents a significant restructuring of the nationâs fiscal framework, aimed at capturing value within the borderless digital economy. This study provides a legal analysis of the updated "Significant Economic Presence" (SEP) rule under Section 17 of the Act, which shifts the taxable nexus from traditional physical presence (Permanent Establishment) to economic participation. By expanding the SEP framework, the NTA 2025 formalises the "digital shadow" of the workforce, explicitly including remote freelancers, digital consultants, and content creators within the tax net while mandating residency-based taxation on worldwide income. Additionally, the Act classifies profits from digital asset transactions, including cryptocurrencies and non-fungible tokens (NFTs), as taxable income. The analysis also examines the institutional transition from the Federal Inland Revenue Service to the Nigeria Revenue Service (NRS), emphasising the deployment of automated technologies for real-time reporting and collection. Despite these advancements, persistent challenges remain, including infrastructure deficits, enforcement complexities in peer-to-peer transactions, and the need to align with global standards such as the OECDâs Two-Pillar Solution. Ultimately, the study concludes that although the NTA 2025 modernises the fiscal social contract, its effectiveness in optimising revenue depends on institutional capacity and clear regulatory guidance.
Matteo Vaccargiu, Sabrina Aufiero, Silvia Bartolucci, Ronnie de Souza Santos · 6 authors
Labels on platforms such as GitHub support triage and coordination, yet little is known about how well they align with code modifications or how such alignment affects collaboration across contributor experience levels. We present a case study of the Kubernetes project, introducing label-diff congruence - the alignment between pull request labels and modified files - and examining its prevalence, stability, behavioral validation, and relationship to collaboration outcomes across contributor tiers. We analyse 18,020 pull requests (2014--2025) with area labels and complete file diffs, validate alignment through analysis of over one million review comments and label corrections, and test associations with time-to-merge and discussion characteristics using quantile regression and negative binomial models stratified by contributor experience. Congruence is prevalent (46.6\% perfect alignment), stable over years, and routinely maintained (9.2\% of PRs corrected during review). It does not predict merge speed but shapes discussion: among core developers (81\% of the sample), higher congruence predicts quieter reviews (18\% fewer participants), whereas among one-time contributors it predicts more engagement (28\% more participants). Label-diff congruence influences how collaboration unfolds during review, supporting efficiency for experienced developers and visibility for newcomers. For projects with similar labeling conventions, monitoring alignment can help detect coordination friction and provide guidance when labels and code diverge.
This paper explores the relationship between fiscal decentralization, green finance, and the digital economy in driving sustainable development, using a balanced cross-country panel dataset spanning 2014â2022, for 29 European countries. Employing dynamic panel estimation techniques, including system generalized method of moments (GMM), the research investigates how fiscal decentralization, green finance, and the digital economy (each of them individually and through interaction mechanisms), dynamically shape sustainable development performance in the presence of endogeneity and temporal persistence. The findings reveal strong inertia in sustainable development, which depends on its previous level. Fiscal decentralization has complex effects: revenue autonomy supports sustainability, whereas expenditure autonomy may undermine it, suggesting differences in how resources are used efficiently at the local versus central levels. Digitalization acts as a catalyst, boosting the effectiveness of environmental taxes and enhancing local spending outcomes. However, if fiscal administrations are not digitally integrated, digitalization may weaken the benefits of decentralized revenues. This study advances the literature by integrating fiscal, financial, and digital views, providing new insights into policy coordination.
Decentralized Finance (DeFi) operating in Benin are essential for financing the agricultural sector and for achieving the Sustainable Development Goals. This research contributes to the debate on the effectiveness of agricultural financing models proposed by DeFi in Northern Benin. Two theoretical approaches are mobilized to assess farmersâ perceptions : Triandisâ interpersonal behaviour model (1979) and the balanced incomplete block design method for analyzing farmersâ choices. A total of 585 farmers were surveyed, including 385 financing beneficiaries, using purposive sampling. Data were analyzed with R version 4.3.0 and RStudio version 2022.02.0. The results highlight a preference for individual financing models (61.26%) over group-based models (38,74%), as they better meet the immediate needs of farmers. Regarding the impact of financing models on agricultural factors of production, farmers acknowledge the positive effect of individual financing on the purchase of inputs and equipment, but criticize the inability of group financing models to stimulate overall productivity and land expansion. The overall perception of support systems implemented after financing is negative, as they remain disconnected from farmerâs real needs. It therefore appears that while financing models satisfy beneficiaries in terms of immediate operational aspects (inputs, equipment, financial needs), and they fail to address structural expectations such as productivity growth and farmland expansion
Carpooling System (CS) is traveling in a carpool where many people travel in one car. It provides users with a number of vehicles spread across the city. Sensitive information, such as the Ride Sharing (RS) userâs identity, zone, access code, etc., is transmitted through public CS. Due to data breaches, it is necessary to maintain data confidentiality and adequately secure ridesharing usersâ data. Criminals use this information to steal information from the CS. Therefore, since attackers can access RS usersâ details for illegitimate purposes, developing secure authentication protocols is critical. Traditional car-sharing systems also follow a centralized structure for data sharing. Yet, car-sharing systems also have security challenges. To address this problem, this paper suggests using the Blockchain-based Decentralized Secret Ride Communication System (BDSRCS) for secure data transmission in CS. First, the Proxy Attribute-based Encryption (PAE) approach is utilized to encrypt the RS userâs details in an unreadable format. Subsequently, the BDSRCS approach is used for secure chain link-based communication in the network. This method prevents unauthorized access to data and decentralized data sharing in the network. Thereafter, the Delegated Proof of Stake Key Validation (DPSKV) technique is used to check the primary keys for peer-end verification to hand over the encrypted data. Numerical outcomes establish that the proposed BDSRCS-PAE model enhances the detection rate of suspicious manners and increases the security performance compared with other approaches.
Blockchain technology is often discussed as if it emerged from nowhere, yet its architectural DNA traces directly to the decentralized computing principles James~N. Gray articulated in 1986. This paper maps the conceptual lineage from Gray's requestor/server model to modern blockchain architectures, showing how his emphasis on modularity, autonomy, data integrity, and standardized communication anticipated the design of systems like Bitcoin and Ethereum, and, more recently, the Web3 movement and Layer-2 scaling architectures. We examine consensus mechanisms, cryptographic foundations, rollup-based Layer-2 protocols, and cross-chain interoperability through this historical lens, identify persistent challenges in scalability and modularity, and outline future directions toward Web4: an intelligent, decentralized internet integrating blockchain, artificial intelligence, and the Internet of Things.
Web3 authentication stacks largely inherit ECDSA centric single-signature workflows that limit security and efficiency, while custody of identity data often remains application controlled rather than self-sovereign. We present S-Auth, an authentication layer that combines BIP340 Schnorr signatures with Decentralized Identifiers (DID), Verifiable Credentials (VC), and Content addressing (CID). The proposed solution utilizes Schnorr digital signatures, which have demonstrated improved security and efficiency over traditional schemes. The contributions of this work are as follows. Firstly, we apply the BIP340 standard to Schnorr digital signatures, bolstering security against various attacks including strong unforgeability under chosen message attack (SUF-CMA), non-malleability, linearity, related-key at tacks, hash collision, fault injection, nonce exfiltration, resource exhaustion, and domain separation. Secondly, we leverage the linearity property of Schnorr signatures to enable multi-signature aggregation and batch verification, addressing the inefficiency of existing schemes that rely on single signatures and thereby also enhancing privacy. Third, we combine the blockchain with DID, VC, and IPFS to provide a secure and self-sovereign identity that can be authenticated. Experiments comparing ECDSA, Ed25519, Schnorr, and BIP340 show that S-Auth reduces signature artifacts via aggregation, improves verifier throughput with batching, and decreases anchoring overhead while preserving user-controlled identity. S-Auth provides a self-sovereign, efficient, and secure authentication mechanism suitable for Web3 environments.
Autonomous AI agents increasingly execute consequential actions against operational infrastructure. This paper presents AEGIS, a constitutional governance architecture that enforces deterministic policy at the agent action boundary â post-reasoning, pre-execution. AEGIS satisfies Anderson's reference monitor properties, aligns with all four functions of the NIST AI Risk Management Framework, and introduces a decentralized federation model for cross-organizational governance intelligence sharing. Submitted to IEEE Computer, Special Issue on AI Governance and Compliance.
The amount of international capital invested in sustainability-focused investments and decentralized financial technologies has been growing fast. Thus, this research focuses on the transmission of volatility and optimal portfolio composition among decentralized finance (DeFi) assets, S&P renewable energy and technology market indices, and conventional energy commodities for the period from March 15, 2018, to August 30, 2024. The sample period was divided into three sub-periods to examine the impact of COVID-19, which increased in parallel with the adoption of DeFi and a focus on sustainability: pre-COVID, during-COVID, and post-COVID. This research utilizes the Diebold-Yilmaz and BarunĂk-KĆehlĂk techniques for time-and frequency-domain analyses, and the Dynamic Conditional Correlation model for portfolio optimization. First, the findings reveal that DeFi tokens (sustainable markets) (brown investments) display moderate (high) (very low) internal connectedness. Second, DeFi tokens demonstrate very low volatility connectedness with both sustainable and brown markets, which suggests strong diversification effects. Third, volatility connectedness among sustainable markets and conventional energy commodities is equally low. Fourth, sustainable markets (conventional energy commodities) make the highest (lowest) contribution to total volatility connectedness, and they operate as net transmitters (receivers) of volatility. Moreover, the total volatility connectedness is 33.7%, which is relatively low, suggesting significant opportunities for diversification of investment portfolios. Furthermore, the outcomes for optimal portfolio weights present greater allocations to green markets compared to conventional energy commodities and DeFi assets, revealing an escalating global transition toward sustainability. Additionally, COVID-19 significantly influenced volatility transmissions and portfolio allocations.
Multi-chain ecosystems suffer from fragmented identity, siloed liquidity, and bridge-dependent token transfers. We present n-VM, a Layer-1 architecture that hosts n heterogeneous virtual machines as co-equal execution environments over shared consensus and shared state. The design combines three components: a dispatcher that routes transactions by opcode prefix, a unified identity layer in which one 32-byte commitment anchors VM-specifific addresses, and a unified token ledger that exposes VM-native interfaces such as ERC-20 and SPL over a common balance store. We formalize routing, identity derivation, and token transfer semantics, and prove cross-VM transfer atomicity and identity isolation under standard cryptographic assumptions. We describe a concrete instantiation with five VMs: a native runtime, EVM, SVM, Bitcoin Script, and TVM. We also present context-based sharding and a write-set scheduler for parallel execution. Under an analytical throughput model, the architecture admits a projected range of about 16,000 to 66,000 transactions per second on commodity hardware.
Stablecoins serve as the fundamental infrastructure for Decentralised Finance (DeFi), acting as the primary bridge between fiat currencies and the digital asset ecosystem. While peg stability is well-documented, the structural role stablecoins play in transmitting systemic risk to the broader market remains under-explored. This study uses copula-based approaches to quantify the transmission of volatility and activity from stablecoin to cryptocurrency markets. We demonstrate in-sample causality across daily, weekly, and monthly horizons. Furthermore, we show that incorporating stablecoin factors significantly reduces Mean Squared Error in cryptocurrency forecasting. Specifically, we link stablecoin volume and upside volatility to broader market volatility, indicating its role as dry powder. Finally, we establish economic value by demonstrating reduced risk in a cryptocurrency volatility targeting model when stablecoin factors are employed.
Umar Majeed Sada, Abrar Ahmad, Najam Saqib, Md. Sarwar
Introduction: With the increasing adoption of solar photovoltaic, bioenergy, and wind energy in distributed networks, consumers are transforming into prosumers, utilizing renewable energy resources within distributed microgrid systems. In these environments, peer-to-peer (P2P) energy trading emerges as a highly promising energy management solution. Methods: This paper introduces a Polygon-based blockchain smart contract designed to execute trading and payment rules autonomously, significantly enhancing the security and fairness of energy trading compared to traditional database technologies. The smart contract consists of two core components: a bidding and settlement module and a payment module, both deployed on a Polygon-based test network, Cardona, to assess real-time interactions. Furthermore, to validate this model, simulations were conducted using realistic data on an Ethereum Virtual Machine (EVM) in a microgrid setting with six participants. Results: The results illustrate the model's potential to facilitate the integration of renewable energy within microgrids, thereby encouraging the adoption of distributed energy resources and supporting localized P2P energy trading. The proposed methodology shows that Polygon-based smart contracts with auction mechanisms can enable efficient, transparent, and automated P2P energy trading, enhancing grid stability in non-autonomous microgrids. However, the approach requires validation at larger scales to assess performance under real-world conditions . Conclusion: This approach offers a robust framework for designers aiming to build sustainable, decentralized energy markets.
<div> Large Language Models (LLMs) are transforming 1 blockchain security and analytics, yet a system-2 atic evaluation of their capabilities remains limited. 3 This survey provides a comprehensive, AI-centric 4 assessment of LLM-based methods across over 70 5 recent studies spanning 11 application domains, 6 such as security auditing, transaction fraud de-7 tection, and cryptocurrency portfolio management. 8 Our unified taxonomy standardizes task formula-9 tions and evaluation practices to enable a com-10 parison of six LLM roles across domains. For 11 each domain, we review input representations tai-12 lored to blockchain data; LLM architectures, learn-13 ing and inference paradigms, e.g., fine-tuning, 14 retrieval-augmented generation, and agentic strate-15 gies. Our review analyzes the strengths, limita-16 tions, and emerging patterns of LLM roles observed 17 in current systems. Finally, we provide practi-18 cal guidance for selecting LLMs for specific roles 19 and outline promising research directions. The on-20 line resources of this survey are available on https: 21 //llmblockchain.github.io/LLMBlockchain/. 22 1 Introduction 23 Large Language Models are increasingly incorporated into 24 blockchain systems for both security and financial analyt-25 ics, including smart contract auditing, transaction monitoring, 26 fraud detection, market analysis, and decentralized finance 27 </div>
Svetlana V. KRIVORUCHKO, Viktor L. DOSTOV, Irina A. RIZVANOVA
Subject. The decentralized finance (DeFi) ecosystem. Objectives. To identify the key features of its functioning based on an analysis of the DeFi ecosystem. Methods. The study applied general scientific methods, as well as generalized, object-subject, systems, process, and functional approaches, and conducted a structural analysis. Results. A decomposition of the DeFi ecosystem by levels has been carried out. Key features have been identified, including the absence of mandatory institutional separation of products, high flexibility of ecosystem interactions, a specific mechanism for liquidity generation, and hybridization with TradFi into a new type of high-level unified ecosystem. A mapping of levels, elements, and products has been developed. The analysis has also distinguished four analytical approaches: technological architecture, product environment, institutional composition, and hybrid solutions with TradFi. Conclusions. The DeFi ecosystem is characterized by high algorithmic connectivity and a weak institutional structure. Its operational features include technological neutrality, modularity, de-institutionalized interaction, and expansion towards TradFi. At the technological level, the DeFi ecosystem is vertical. At the product level, it is largely localized on the lower layers of the technology stack and is rather horizontal: various products interact without a pronounced hierarchy. The external ecosystem enables interaction with the traditional financial system. There is a clear trend towards increasing connectivity between DeFi and TradFi.
Abstract - In the fast-growing digital age, systems for communicating through cyber means allow people, organisations and governments to share information in real-time. Unfortunately, the majority of the current communications networks use a centralised architecture, which are susceptible to cyber threats like data breaches, identity theft, man in the middle attack, and distributed denial of service attacks. These vulnerabilities degrade the confidentiality, integrity, availability and trust in the digital communication systems. This research proposes a secure cyber communication framework using blockchain technology to bypass the limitations of the current systems. The framework uses decentralised distributed ledger technology, public/private key cryptography, digital signatures, cryptographic hash functions and smart contracts to facilitate the secure transmission of messages, authenticate user identity and maintain a tamper-proof record of messages. All messages will be encrypted, digitally signed for authenticity and validated using consensus before being permanently recorded onto the blockchain. This architecture will reduce reliance on third-party intermediaries while providing greater resilience and transparency against cyber-attacks, as well as eliminating single points of failure. Performance, scalability and energy consumption will also be discussed in this study. According to the results, use of blockchain for cyber-communication is associated with high levels of security; therefore, the use of blockchain in future decentralised communication networks should result in more secure, trustworthy, and reliable systems within all key industries. Key Words: Blockchain Technology, Cyber Communication, Secure Messaging, Distributed Ledger, Cryptography, Digital Signatures, Smart Contracts, Decentralization, Data Integrity, Network Security
Password-based authentication systems remain the most widely used method for user verification despite being highly susceptible to offline dictionary attacks. To mitigate such attacks, server-aided password-based authentication schemes utilize an independent server, which helps to harden the credentials to be stored on the website database. Existing server-aided password-based authentication schemes rely on number-theoretic assumptions that are vulnerable to quantum-enabled adversaries and incorporate complex computations such as bilinear pairings, exponentiation, and Zero-Knowledge Proofs. In this work, we introduce a novel post-quantum secure server-aided password-based authentication scheme based on the Module Learning With Errors (M-LWE) problem. A defining feature of our protocol is its complete operational transparency as it integrates with existing web interfaces without requiring users to modify their login behaviour or perform additional computation. To ensure long-term resilience, our scheme includes a transparent key rotation mechanism that allows service providers to update the entire credential database with a fresh secret key without user intervention. We provide a formal security analysis in the Real-or-Random (RoR) framework. This analysis demonstrates that our protocol's resistance to offline dictionary attacks reduces to the underlying hardness of the M-LWE problem, and the system achieves forward secrecy through a key rotation mechanism. Through an optimized Number Theoretic Transformation (NTT)-based implementation for faster polynomial multiplications, our empirical analysis demonstrates high computational efficiency, with average registration and authentication latencies of 0.88 ms and 0.96 ms, respectively.
Cybersecurity regulatory and compliance frameworks such as NIST SP 800-53 Rev. 5, the HIPAA Security Rule, and the GDPR define essential security and privacy obligations for healthcare information systems and their supporting infrastructures. Despite their critical role, compliance assurance in healthcare is predominantly manual and centered on periodic, point-in-time assessments, relying on human interpretation of regulatory requirements and fragmented evidence collection. As healthcare ecosystems evolve toward decentralized systems and extensive third-party participation, there is a growing need for compliance mechanisms that enable continuous assurance, verifiable accountability, and privacy-preserving enforcement across organizational boundaries. This paper proposes SSAP-CPCF, a Secure, Smart, Automated, and Privacy-Preserving Cybersecurity Policy Compliance Framework that integrates permissioned blockchain orchestration, LLM-assisted regulatory interpretation with human validation, and zero-knowledge proof-based verification. The framework explicitly encodes regulatory authority, assessor oversight, and multi-party approval into its protocol design, ensuring that automation enforces, rather than replaces, governance structures. SSAP-CPCF treats compliance as an enforceable system property by binding assessments to vendor-declared obligations and enforcing approval and oversight requirements at the ledger level, ensuring integrity, auditability, and separation of authority. A prototype implemented on a multi-organization Hyperledger Fabric network demonstrates the feasibility of privacy-preserving, ledger-enforced compliance automation in realistic multi-stakeholder settings.
Current mobile System-on-Chip (SoC) architectures suffer from a fundamental âReactive Polling Tax,â where high-level software must frequently interrupt low-power hardware states to query rawsensor telemetry. This paper introduces the Contextual Neural Bus (CNB), a dedicated, asynchronous hardware-level interconnect designed to shift context awareness from volatile software cycles to deterministic silicon logic. By utilizing a decentralized multi-modal fusion layer, the CNB generates Universal Intent Tokens (UITs)â64-bit cryptographic primitives that represent verified user states at the physical layer. Unlike legacy co-processors that merely buffer data, the CNB integrates a Hardware-Resident Zero-Knowledge Proof (ZKP) Generator to provide mathematical certainty of user intentwhile physically isolating raw biometric and environmental telemetry within a secure silicon enclave. Preliminary simulations using a digital-twin SoC model indicate a 90.8% reduction in interrupt driven power consumption, effectively achieving âEnergy-Neutral Privacyâ by utilizing the resulting power surplus to offset cryptographic overhead. Furthermore, the architecture introduces Predictive L3 Cache Pre-warming, which anticipates user interactions to virtually eliminate âcold-startâ application latencies. By anchoring proactive computing in the deterministic reliability of silicon, this work establishes a scalable, privacy-first path toward zero-latency, energy-autonomous mobile ecosystems.