Electric vehicles have garnered substantial attention as an environmentally sustainable transportation alternative amid escalating global concerns regarding ecological preservation and energy resource management. While the proliferation of electric vehicles necessitates the development of efficient and secure charging infrastructure, the inherent communication-intensive nature of the charging processes has raised concerns regarding potential privacy vulnerabilities. Our paper introduces a privacy protection scheme specifically designed for electric vehicle charging reservations to address this issue. The primary goal of this scheme is to protect user privacy while maintaining operational efficiency and economic viability for charging providers. Our proposed solution ensures a secure and private environment for charging reservation transactions and subsequent deviation settlements by incorporating advanced technologies, including zero-knowledge proof, a consortium blockchain, and homomorphic encryption. The scheme encrypts charging reservation information and securely transmits it via a consortium blockchain, effectively shielding the sensitive data of all participating parties. Notably, the experimental findings establish the robustness of our scheme in terms of its security and privacy protection, aligning with the stringent demands of electric vehicle charging operations.
Muhammad Muzammil, Abisheka Pitumpe, Xigao Li, Amir Rahmati · 5 authors
Governments and regulatory bodies have recognized investment scams as a prevalent form of cryptocurrency fraud. These scams typically use professional-looking websites to lure unsuspecting victims with promises of unrealistically high returns. In this paper, we introduce Crimson, a distributed system designed to continuously detect cryptocurrency investment scam websites as they are created in the wild. During the first 8 months of 2024, Crimson processed approximately 6 billion domain names and classified 43,572 unique cryptocurrency investment scam websites in real-time. Beyond detection, we provide insights into the design and infrastructure of these websites that can help users recognize scam patterns and assist hosting providers in detecting and blocking such sites. Furthermore, we investigate the inclusion of our detected scam websites in block-lists used by popular web browsers and applications, finding that the vast majority of these websites were absent. On the financial side, by analyzing the transactions incoming to scammer wallets on 6.7% of the sites detected by Crimson, we observe an estimated lower bound of 2.04M USD in losses due to cryptocurrency investment scams.
Background: The rise of blockchain technology has led to the development of smart contracts, which are self-executing contracts with the terms of the agreement directly written into code. While they promise enhanced efficiency, security, and automation, the legal implications of smart contracts on conventional contract law remain largely underexplored. The advent of these digital contracts challenges traditional legal frameworks and calls for a reassessment of existing contract law principles. Objective: This study aims to analyze the implications of smart contracts for conventional contract law, focusing on the legal, ethical, and practical challenges they present. The research seeks to evaluate how smart contracts align with or diverge from traditional contract principles such as offer, acceptance, and enforceability. Method: A qualitative research approach was employed, utilizing a comprehensive literature review and case law analysis to explore the intersection of smart contracts and conventional contract law. Interviews with legal experts and blockchain developers were also conducted to gather insights on real-world applications. Results: The findings reveal that while smart contracts offer substantial benefits in terms of automation and security, they also raise issues related to ambiguity, legal recognition, and the need for updated regulations. The study identifies a gap in existing legal frameworks regarding the enforceability of smart contracts. Conclusion: Smart contracts have significant potential to revolutionize contract law, but their integration into conventional legal systems requires substantial legal reform and adaptation. Further research is needed to establish clear regulatory standards.
In recent years, the Ethereum Name Service (ENS) has garnered significant attention within the community for enabling the use of Unicode in domain names, thereby facilitating the inclusion of a wide array of character sets such as Greek, Cyrillic, Arabic, and Chinese. While this feature enhances the versatility and global accessibility of domain names, it concurrently introduces a substantial security vulnerability due to the presence of homoglyphs-characters that are visually similar to others across Unicode and ASCII sets. These similarities can be exploited in homoglyph attacks, posing a distinct threat to domain name integrity. Despite community efforts to counteract this issue through a normalization process prior to domain resolution, our analysis uncovers significant discrepancies in how the normalization processes are applied across various applications. This inconsistency could result in the same domain name being resolved to different addresses in different applications, underscoring a critical vulnerability. We also discovered the new attack scenario in ENS which may cause legitimate domains resolved into malicious addresses even when they are verified by authorities. To systematically evaluate this inconsistency, we designed a tool for detecting application-level discrepancies in domain normalization process without requiring access to the application's source code. Our evaluation on hundreds of real-world Web3 applications identifies widespread deviations from established homoglyph mitigation practices, with more than 60% digital wallets and 80% dApps (decentralized applications) not able to produce consistent ENS resolving results, potentially impacting millions of users. This analysis underscores the urgent need for a standardized implementation of normalization processes to safeguard the integrity and security of ENS domains.
The article explores non-fungible token (NFT) art as the ultimate form of commodification of art, thus positioning it as a potential ideal art form for capitalism. The article synthesizes theories from fields outside of art history, including critical theory, new materialism, and object-oriented ontology, to provide a comprehensive explanation for the rise of NFTs and their impact on the art world. By utilizing critical theory, power dynamics and hierarchies within a given context are analyzed, while new materialism and object-oriented ontology expand the scope of analysis to include non-human entities and their agency. These theoretical frameworks also provide insights into the material and ontological aspects of the NFT art phenomenon. The study's findings reveal the entanglement of digital assets with the social, economic, and technological dimensions of capitalism. This study aims to contribute to the ongoing debates surrounding the intersections of art and capitalism, while providing a theoretical foundation.
The rise of digital healthcare has intensified concerns over data privacy, particularly in cross-institutional medical data exchanges. This study introduces a blockchain-based protocol leveraging Zero-Knowledge Proofs (ZKP), specifically zk-SNARK, to enable verifiable yet privacy-preserving health data sharing. Built on a permissioned Ethereum blockchain, the protocol ensures that medical data validity can be confirmed without disclosing sensitive content. System implementation involves Python-based zk-circuits, smart contracts in Solidity, and RESTful APIs supporting HL7 FHIR formats for interoperability. Performance evaluations show promising results: proof verification times remained under 100 ms, with average proof sizes below 2 KB, even under complex transaction scenarios. Gas consumption analysis indicates a trade-off—ZKP-enabled transactions consumed approximately 93,000 gas units, compared to 52,800 in baseline cases. Interoperability testing across 10 FHIR-based scenarios resulted in 100% parsing success and an average data integration time of 1.7 seconds. Security assessments under white-box threat models confirmed that sensitive information remains unreconstructable, preserving patient confidentiality. Compared to previous implementations using zk-STARK, this protocol offers a 30% improvement in verification efficiency and a 45% reduction in proof size. The novelty lies in combining lightweight ZKP mechanisms with an interoperability-focused design, tailored for realistic hospital infrastructures. This research delivers a scalable, standards-compliant architecture poised to advance secure digital healthcare ecosystems while complying with regulations like GDPR
Xinyao Xu, Ziyu Mao, Jianzhong Su, Xingwei Lin · 7 authors
The rapid growth of decentralized applications, while revolutionizing financial transactions, has created an attractive target for malicious attacks.Existing approaches to detecting attacks often rely on predefined rules or simplistic and overly-specialized models, which lack the flexibility to handle the wide spectrum of diverse and dynamically changing attack types.To address this challenge, we present a general and extensible framework, MoE (Monitoring Ethereum), that leverages runtime verification to detect a wide range of attacks on Ethereum.MoE features an expressive attack modeling language, based on Metric First-order Temporal Logic (MFOTL), that can formalize a wide range of attacks.We integrate a novel semantic lifting approach that extracts system behaviors relevant for various attacks, which can be analyzed using the monitoring tool MonPoly.Furthermore, we also equip MoE with quantitative capabilities to evaluate the similarity between a transaction and an attack formula to enhance its performance in identifying attacks, including near-miss attacks.We carry out extensive experiments with MoE on a labeled benchmark and a large-scale dataset containing over one million transactions.On the labeled benchmark, MoE successfully detects 92.0% attacks and achieves a 45.0% higher recall rate than competing state-of-the-art tool.MoE finds 3,319 attacks with 95.4% precision on the large dataset.Furthermore, MoE uses quantitative analysis to uncover 8% additional attacks.Finally, the average time for * Xinyao Xu and Ziyu Mao contributed equally.
M. K. Ghosh, Swapnil Srivastava, Apoorva Upadhyaya, Raju Halder · 5 authors
Phishing scams on Ethereum have expanded with the surge of the platform, posing substantial challenges due to the sheer similarity in user behaviours and sparse temporal instances. Current methods often fail to tackle these concerns and overlook the temporal sequence of transactions, resulting in suboptimal performance. In this paper, we aim to address these gaps by focusing on the alignment of two aspects: (1) User-specific local temporal behavior, and (2) Divergences from global activity patterns of the network. Hence, we introduce CATALOG (CApturing joint TemporAl dependencies from LOcal and Global user behaviour), a novel representation learning model that jointly captures the local and global user behviours and their correlations by leveraging a dual cross-attention mechanism paired with a bi-directional Masked Language Modelling (MLM) transformer. Our proposed model simultaneously learns from local behavioral shifts, global market trends, and contextually enriched embeddings, effectively distinguishing phishing from non-phishing users while addressing existing research gaps. Extensive experiments on real-world Ethereum transaction data show that our framework improves phishing detection by 7-8% in the F1-Score along with demonstrating the generalization to Ethereum versions 1.0 and 2.0.
In recent years, a large number of on-chain attacks have emerged in the blockchain empowered Web3 ecosystem. In the year of 2023 alone, on-chain attacks have caused losses of over 585 million. Attackers use blockchain transactions to carry out on-chain attacks, for example, exploiting vulnerabilities or business logic flaws in Web3 applications. A wealth of efforts have been devoted to detecting on-chain attack transactions through expert patterns and machine learning techniques. However, in this ever-evolving ecosystem, the performance of current methods is limited in detecting new on-chain attacks, due to the obsoleting of attack recognition patterns or the reliance on on-chain attack samples. In this paper, we propose a universal approach for detecting on-chain attacks even when there are few or even no new on-chain attack samples. Specifically, an in-depth analysis of the transaction characteristics is conducted, and we propose a new insight to train a generic attack transaction detecting model, i.e., transaction reconstruction. Particularly, to overcome the over-fitting in the transaction reconstruction task, we use the web-scale function comments related to transactions as supervision information, rather than expert-confirmed labels. Experimental results demonstrate that the proposed approach surpasses the supervised state-of-the-art by 13% in AUC, with just 30 known on-chain attack samples. Moreover, without any known attack samples, our method can still detect new on-chain attacks in the wild (with a precision of 61.83%). Among attacks detected in the wild, we confirm 1,692 address poisoning attacks, a new type of on-chain attack targeting token holders. Our code is available at: https://github.com/wuzhy1ng/attack_trans_detection_www25.
Blockchains have block-size limits to ensure the entire cluster can keep up with the tip of the chain. These block-size limits are usually single-dimensional, but richer multidimensional constraints allow for greater throughput. The potential for performance improvements from multidimensional resource pricing has been discussed in the literature, but exactly how big those performance improvements are remains unclear. In order to identify the magnitude of additional throughput that multi-dimensional transaction fees can unlock, we introduce the concept of an $α$-approximation. A constraint set $C_1$ is $α$-approximated by $C_2$ if every block feasible under $C_1$ is also feasible under $C_2$ once all resource capacities are scaled by a factor of $α$ (e.g., $α=2$ corresponds to doubling all available resources). We show that the $α$-approximation of the optimal single-dimensional gas measure corresponds to the value of a specific zero-sum game. However, the more general problem of finding the optimal $k$-dimensional approximation is NP-complete. Quantifying the additional throughput that multi-dimensional fees can provide allows blockchain designers to make informed decisions about whether the additional capacity unlocked by multidimensional constraints is worth the additional complexity they add to the protocol.
Layer 2 (L2) protocols, payment channels, sidechains, and rollups, are central to blockchain scalability, enabling off-chain execution while preserving on-chain security. Despite growing deployment, existing security models remain protocol-specific and monolithic, hindering compositional reasoning and principled comparison of assumptions and requirements. We present a general security framework for L2 protocols in the IITM-style Universal Composability (iUC) model. At its core is a modular ideal functionality F_layer2 that abstracts mechanism-specific details while capturing the essential structure of L2 systems through composable subroutines for joining, submission, updating, reading, and settlement under adversarial conditions. This yields uniform definitions of safety, liveness, and data availability across a broad class of L2 protocols. We demonstrate generality by instantiating the framework for three representative constructions: the Brick payment channel, the Liquid sidechain, and the Arbitrum Nitro rollup. Each case study yields a protocol-specific ideal functionality derived from F_layer2 and tailored to its assumptions. Our analysis (i) establishes security via simulation-based proofs, (ii) exposes inherent trade-offs among safety, liveness, and data availability, and (iii) derives lower bounds characterizing fundamental limitations of each design class. Finally, we illustrate the framework as a design tool by presenting FRoll, the first optimistic rollup protocol with fast-finality guarantees, together with a security analysis in our model, showing how the framework supports requirement-driven design of L2 protocols.
Stefanos Chaliasos, Imam Al-Fath, Alastair F. Donaldson
Zero-knowledge proofs (ZKPs) have evolved from a theoretical cryptographic concept into a powerful tool for implementing privacy-preserving and verifiable applications without requiring trust assumptions. Despite significant progress in the field, implementing and using ZKPs via \emph{ZKP circuits} remains challenging, leading to numerous bugs that affect ZKP circuits in practice, and \emph{fuzzing} remains largely unexplored as a method to detect bugs in ZKP circuits. We discuss the unique challenges of applying fuzzing to ZKP circuits, examine the oracle problem and its potential solutions, and propose techniques for input generation and test harness construction. We demonstrate that fuzzing can be effective in this domain by implementing a fuzzer for \texttt{zk-regex}, a cornerstone library in modern ZKP applications. In our case study, we discovered \textit{$10$} new bugs that have been confirmed by the developers.
Isaac Zhang, Kshitij Kulkarni, Tan Li, Daniel Wong · 9 authors
Blockchain technology promises a decentralized, trustless, and interoperable infrastructure. However, widespread adoption remains hindered by issues such as limited scalability, high transaction costs, and the complexity of maintaining coherent verification logic across different blockchain layers. This paper introduces Verifiable Applications (vApps), a novel development framework designed to streamline the creation and deployment of verifiable blockchain computing applications. vApps offer a unified Rust-based Domain-Specific Language (DSL) within a comprehensive SDK, featuring modular abstractions for verification, proof generation, and inter-chain connectivity. This eases the developer's burden in securing diverse software components, allowing them to focus on application logic. The DSL also ensures that applications can automatically take advantage of specialized precompiles and hardware acceleration to achieve consistently high performance with minimal developer effort, as demonstrated by benchmark results for zero-knowledge virtual machines (zkVMs). Experiments show that native Rust execution eliminates interpretation overhead, delivering up to an 197x cycle count improvement compared to EVM-based approaches. Precompiled circuits can accelerate the proof by more than 95%, while GPU acceleration increases throughput by up to 30x and recursion compresses the proof size by up to 230x, enabling succinct and efficient verification. The framework also supports seamless integration with the Web2 and Web3 systems, enabling developers to focus solely on their application logic. Through modular architecture, robust security guarantees, and composability, vApps pave the way toward a trust-minimized and verifiable Internet-scale application environment.
The design of Enterprise-Wide Financial Architectures (EWFA) is crucial for ensuring financial efficiency, regulatory compliance, risk management, and digital transformation in complex organizations.The paper explores theoretical frameworks, implementation strategies, real-world applications, and emerging innovations shaping modern financial architectures.Key theoretical models such as TOGAF, COSO, and Basel III provide foundational principles for structuring financial systems, while advancements in AI, blockchain, cloud computing, and cybersecurity are redefining financial operations.The paper presents case studies from the banking, manufacturing, and government sectors, demonstrating the impact of AI-driven risk management, blockchain-enabled financial transparency, and ERP-based financial integrations.Additionally, future trends indicate that AI-powered predictive analytics, blockchainbased decentralized finance (DeFi), and quantum-resistant cryptographic security will play a vital role in securing financial transactions and optimizing financial decision-making.Organizations that integrate these cutting-edge technologies into their financial architectures will enhance efficiency, security, and compliance while gaining a competitive edge.This study provides a comprehensive roadmap for the successful design, implementation, and optimization of enterprise-wide financial architectures in the rapidly evolving financial landscape.
Blockchain technology, the bedrock innovation in the digital economy, is steadily reengineering the operational logic of the traditional financial system. Harnessing its core attributes of decentralization, immutability, and transparency, it is making profound inroads. This paper undertakes an all-encompassing and systematic review of relevant literature from both domestic and international sources. It particularly focuses on the empowerment routes by which blockchain technology can fuel the reform of the traditional financial setup, thus furnishing theoretical support and practical blueprints for financial systemic revamp. Blockchain technology yields numerous advantages. It significantly boosts payment efficiency, slashes costs, and vigorously promotes financial inclusion. Moreover, it augments regulatory transparency and alleviates information asymmetry. However, several hurdles remain, such as scalability glitches, security threats, and regulatory compatibility issues. This research not only enriches the FinTech knowledge pool but also steers financial institutions in their digital transformation endeavors, enabling them to render more efficient and inclusive services. Going forward, future work should concentrate on technological innovation and regulatory adaptation to fully realize the potential of blockchain in the financial sector.
This study investigates the impact of financial, trade, and economic openness on energy consumption, focusing on renewable, nonrenewable, and fossil energy sources in Belt and Road Initiative (BRI) nations. The BRI framework, introduced by China in 2013, emphasizes economic collaboration and infrastructure development, including renewable energy projects. As participating nations navigate energy transitions to address climate change and achieve sustainable development, understanding the role of openness is crucial. Motivated by the dual challenges of energy security and environmental sustainability, this study explores how openness influences energy consumption patterns and identifies pathways for policy intervention. Using data from 2004 to 2020, the study employs advanced econometric techniques, including Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) and Nonlinear ARDL models, to examine short- and long-term relationships. Control variables such as urbanization, financial development, and education are integrated to provide a comprehensive understanding of the dynamics. The analysis reveals that financial openness positively impacts energy consumption across all types, with a significant contribution to renewable energy in the long term. Trade openness facilitates technology transfer and renewable energy adoption, while economic openness through foreign direct investment (FDI) supports clean energy projects but also sustains fossil fuel reliance in some contexts. Urbanization drives nonrenewable energy demand but offers opportunities for renewable integration contingent on governance quality. Education enhances renewable energy consumption by fostering a skilled workforce and knowledge development. The findings suggest key policy implications. First, financial openness should be directed toward green finance and renewable energy investments. Second, trade policies must focus on reducing barriers to renewable technology imports and fostering global collaborations. Third, economic openness should prioritize sustainable FDI in clean energy sectors. Fourth, urban planning must incorporate decentralized energy systems and green technologies. Finally, investing in education and institutional reforms is essential to drive innovation and ensure effective governance. This study contributes to the discourse on energy transitions in BRI nations, emphasizing the critical role of openness and offering actionable policies to balance economic growth with sustainability.
This article takes Company A as an example to focus on the optimization path of a decentralized financial sharing platform that integrates blockchain technology and big data. By analyzing the hierarchical architecture and core technologies of blockchain and combining them with the capabilities of big data in data integration, analysis, and risk prediction, the author proposes a smart contract design scheme for a financial sharing platform based on "blockchain + big data." The author studied and designed a solution including architecture reconstruction, security optimization, and process automation to address the centralized data security risks, information asymmetry, business process redundancy, and talent shortage issues of Company A's existing platform. Empirical results indicate that this solution effectively addresses the pain points of data silos, high trust costs, and inefficient processes in traditional financial sharing platforms, providing a balanced, secure, and intelligent technological path for the digital transformation of large enterprise finance. The blockchain technology itself has unique advantages: decentralization, trustlessness, and a unique distributed ledger form. These advantages can be used to optimize the architecture of financial sharing platforms, promote and apply them, thereby improving financial work efficiency and expanding enterprise economic benefits.
Recently, with the gradual development of machine learning technology, more and more people are trying to apply machine learning technology in various fields, and finance is one of the important fields. This work investigates the optimization of cryptocurrency portfolios by combining Long Short-Term Memory (LSTM) time series forecasting with traditional portfolio optimization methods. The focus of the paper is on using the historical price data from the past six years of Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC) to train LSTM models, which are then used to predict the prices of these cryptocurrencies for the period from January to June 2024. These predictions are subsequently incorporated into an extended Markowitz framework to optimize the portfolio on a monthly basis. The results indicate that the LSTM-enhanced portfolio optimization method yields higher returns and better risk management compared to traditional methods. This finding could prove that it is feasible and effective to apply machine learning methods, especially time series forecasting methods, to cryptocurrency portfolios.
Abstract: Algebraic geometry offers a powerful and elegant mathematical framework for the design and analysis of modern cryptographic protocols. This research paper investigates the application of algebraic geometry methods—such as elliptic curves, abelian varieties, and projective algebraic structures—in enhancing the security, efficiency, and scalability of cryptographic systems. By bridging advanced algebraic structures with cryptographic primitives, the study demonstrates how algebraic geometry enables the construction of secure public key protocols, zero-knowledge proofs, and post-quantum resilient schemes. Through theoretical modeling, performance benchmarking, and comparative analysis with classical cryptographic approaches, the paper illustrates the advantages of algebraic geometry in terms of computational hardness assumptions, structural integrity, and potential for innovation in secure communications. The findings contribute to the evolving landscape of cryptography by positioning algebraic geometry as a foundational tool in next-generation cryptographic protocol design. Keywords: algebraic geometry, cryptographic protocols, elliptic curves, public key cryptography, post-quantum cryptography, projective varieties, zero-knowledge proofs, secure communication, mathematical cryptography, abelian varieties
In the context of global cross-border payments exceeding $150 trillion, traditional mediation architectures, such as SWIFT, face challenges due to high costs, inefficiencies, and fraud risks. However, blockchain technology become an important driver of innovation in cross-border payments with the characteristics of decentralization, real-time and immutable. This paper aims to answer two core questions: Blockchain technology how to improve the efficiency of cross-border payments through smart contracts, cross-chain protocols and other technical features? How to identify and prevent key risks such as private key security and regulatory conflicts? Through the logical framework of "technical basis - efficiency analysis - risk identification - prevention and control strategy", combined with case comparison (such as RippleNet and SWIFT) and quantitative data (such as Stellar network $0.01 / transaction cost). This paper systematically analyzes the role of blockchain in disintermediation, cost compression, and transparency optimization. Besides, the risks of technological vulnerabilities, regulatory fragmentation and market volatility are revealed. Then, this paper proposes a collaborative governance scheme of hybrid architecture, zero-knowledge proof and multilateral regulatory sandbox. Research finding, blockchain technology can reduce cross-border payment time to seconds and reduce costs by more than 90%, but it needs to deal with challenges such as throughput constraints, conflicting regulatory standards and the volatility of digital currencies. It is suggested that future research focus technology optimization, multilateral regulatory collaboration and market ecological integration, provide theoretical and practical support for building an efficient and secure global payment system.
While blockchain’s immutability ensures data integrity, it also poses significant challenges when dealing with illegal or erroneous data that require modification. The concept of redactable blockchain has emerged, utilizing Chameleon Hash (CH) and subsequent Policy-based Chameleon Hash (PCH) for controlled data editing. However, current redactable blockchain implementations exhibit significant limitations, particularly in their inability to separate data editing from policy modification and their insufficient support for decentralized management of diverse editing operations. To address these issues, this paper initially introduces the concept of Flexible Policy Chameleon Hash (FPCH), which integrates PCH with non-interactive zero-knowledge proofs to enable enhanced policy management flexibility. Moreover, this paper proposes a Redactable Blockchain Framework with Fine-grained Access Control (RBFAC) based on FPCH. The RBFAC framework employs a hybrid cryptographic approach to separate the right of data editing from policy modification. The framework also provides essential functionalities, including editing accountability, key tracking and revocation mechanisms, and policy privacy protection. Finally, experimental evaluations demonstrate that the RBFAC framework maintains acceptable performance overhead while delivering these advanced features. The results indicate that the proposed solution addresses the limitations of existing redactable blockchain systems, offering a more flexible and secure approach to controlled data editing in blockchain environments.
C. Aparna, S. Radha, C. Aarthi, K. M. Karthick Raghunath
ABSTRACT Mobile Ad hoc networks (MANETs) are key for applications in which flexibility and organization are paramount, but the security of such networks entails threats that can exploit the vulnerability of their open architecture, resulting in various attacks. To address such issues, a novel architectural framework is always required. One such framework is introduced, namely, the HoneyFed Secure Architecture (HFSA), which provides the combination of an advanced honey encryption system with federated learning‐based decentralized security to improve the security of MANET. Honey encryption, on the other hand, employs adaptive deception techniques to generate plausible decoy data on decryption failure, employs dynamic key management for tamper resistance, and provides perfect authentication through multi‐factor methods and zero‐knowledge proofs. We found that federated learning offers decentralized model training, where nodes jointly train local models while exchanging progress updates without exposing raw data, enabling 81.4% more detections of emerging threats while preserving data privacy. Using the proposed HFSA approach achieves a 78% protection improvement against attacks and a 71% reduction in unauthorized access. HFSA offers a robust and scalable framework of security that uses continuous learning and adaptation to the vulnerabilities of the MANETs to enhance network resilience.
Dirin Mchirgui, Mohammed Ali Sulyman Digheem, Fawzi Salem Adwela
This paper explores the interconnectedness and spillover relationships among Bitcoin, gold, gold-backed cryptocurrencies, and energy commodities during the COVID-19 pandemic and the Russia-Ukraine military conflict. Using a quantile connectedness approach, we reveal diverse influence dynamics among digital assets, with Gold, DGX, and PAXG emerging as key contributors to the network’s total connectedness. Notably, the cTCI/TCI ratio underscores substantial direct linkages, emphasizing significant interconnections among digital assets. DGX acts as a principal information transmitter, while gas plays a crucial role as a primary receiver, suggesting its potential as a diversifier. The time-quantile analysis highlights heightened connectedness during significant events, providing valuable insights for investors and risk managers. Results underscore varying roles of assets, with PAXG persistently acting as a net transmitter and Bitcoin and Gold displaying nuanced patterns. Interestingly, Gold demonstrated certain safe haven characteristics only during the Russia-Ukraine war. The time-frequency analysis at the median quantile emphasizes the dominance of short-term dynamics, prompting the need for adaptive risk management strategies. Overall, this study facilitates a nuanced understanding of market dynamics, offering practical insights for different periods.
The rapid growth of the Internet of Things (IoT) has introduced critical challenges related to security, scalability, and data integrity. Blockchain technology, with its decentralized, immutable, and tamper-resistant framework, presents a transformative solution to address these challenges. This study explores blockchain applications in the IoT, focusing on security, automation, scalability, and data sharing. Industry-specific applications, including supply chain management, smart cities, and healthcare, highlight the potential of blockchains to optimize operations, ensure compliance, and foster innovation. Additionally, blockchain technology enables robust audit trails, enhances accountability, and reduces fraud in sensitive IoT applications, such as finance and healthcare. The synergy between blockchains and the IoT creates a secure and transparent platform for managing device interoperability and data exchange, fostering seamless communication between diverse IoT components. Furthermore, this paper discusses layer 2 scaling techniques and tokenization to address scalability, ownership, monetization, and cost challenges, providing practical solutions for real-world deployments. Future directions emphasize integrating blockchain systems with artificial intelligence (AI), machine learning (ML), and edge computing, offering groundbreaking capabilities to further revolutionize IoT ecosystems. By merging these advanced technologies, organizations can build secure, scalable, and intelligent systems to drive innovation and trust.