Shengze Wang, Yi Liu, Xiaoxue Zhang, Liting Hu · 5 authors
Distributed Hash Tables (DHTs) are pivotal in numerous high-impact key-value applications built on distributed networked systems, offering a decentralized architecture that avoids single points of failure and improves data availability. Despite their widespread utility, DHTs face substantial challenges in handling range queries, which are crucial for applications such as LLM serving, distributed storage, databases, content delivery networks, and blockchains. To address this limitation, we present LEAD, a novel system incorporating learned models within DHT structures to significantly optimize range query performance. LEAD utilizes a recursive machine learning model to map and retrieve data across a distributed system while preserving the inherent order of data. LEAD includes the designs to minimize range query latency and message cost while maintaining high scalability and resilience to network churn. Our comprehensive evaluations, conducted in both testbed implementation and simulations, demonstrate that LEAD achieves tremendous advantages in system efficiency compared to existing range query methods in large-scale distributed systems, reducing query latency and message cost by 80% to 90%+. Furthermore, LEAD exhibits remarkable scalability and robustness against system churn, providing a robust, scalable solution for efficient data retrieval in distributed key-value systems.
Pierre-Luc Dallaire-Demers, William Doyle, Timothy Foo
Precise suites of benchmarks are required to assess the progress of early fault-tolerant quantum computers at economically impactful applications such as cryptanalysis. Appropriate challenges exist for factoring but those for elliptic curve cryptography are either too sparse or inadequate for standard applications of Shor's algorithm. We introduce a difficulty-graded suite of elliptic curve discrete logarithm (ECDLP) challenges that use Bitcoin's curve y^2=x^3+7 mod p while incrementally lowering the prime field from 256 down to 6 bits. For each bit-length, we provide the prime, the prime group order, and two deterministic nothing-up-my-sleeve (NUMS) points in compressed SEC1 form. All challenges are generated by a deterministic, reproducible procedure, and no private challenge scalar is chosen in advance. We calibrate classical cost against Pollard's rho records and quantum cost against resource estimation results for Shor's algorithm. We compile Shor's ECDLP circuit to logical counts and map them to physical resources for various parameters of the surface code, the repetition cat code and the LDPC cat codes. Under explicit and testable assumptions on physical error rates, code distances, and non-Clifford supply, our scenarios place the full 256-bit instance within a 2027--2033 window. The challenge ladder thus offers a transparent ruler to track fault-tolerant progress on a cryptanalytic target of immediate relevance, and it motivates proactive migration of digital assets to post-quantum signatures.
Elliptic Curve Cryptography (ECC) is a fundamental component of modern public-key cryptosystems that enable efficient and secure digital signatures, key exchanges, and encryption. Its core operation, scalar multiplication, denoted as $k \cdot P$, where $P$ is a base point and $k$ is a private scalar, relies heavily on the secrecy and unpredictability of $k$. Conventionally, $k$ is selected using user input or pseudorandom number generators. However, in resource-constrained environments with weak entropy sources, these approaches may yield low-entropy or biased scalars, increasing susceptibility to side-channel and key recovery attacks. To mitigate these vulnerabilities, we introduce an optimization-driven scalar generation method that explicitly maximizes bit-level entropy. Our approach uses differential evolution (DE), a population-based metaheuristic algorithm, to search for scalars whose binary representations exhibit maximal entropy, defined by an even and statistically uniform distribution of ones and zeros. This reformulation of scalar selection as an entropy-optimization problem enhances resistance to entropy-based cryptanalytic techniques and improves overall unpredictability. Experimental results demonstrate that DE-optimized scalars achieve entropy significantly higher than conventionally generated scalars. The proposed method can be integrated into existing ECC-based protocols, offering a deterministic, tunable alternative to traditional randomness, ideal for applications in blockchain, secure messaging, IoT, and other resource-constrained environments.
Michael Demmler, Gibrán Aguilar Rangel, Rodrigo Cuéllar Ramírez
This study investigates on the relationship between cryptocurrencies and financial inclusion in Mexico. Using a basic, descriptive and qualitative research design, first a brief literature review is conducted in order to analyze the impact of cryptocurrencies on financial inclusion according to the state-of-the-art opinion of other researchers on the topic. Secondly, aiming to improve the understanding of the potential that cryptocurrencies may have for financial inclusion in Mexico, a digital questionnaire is applied to a sample of 415 individuals. Main results of the literature review show that fintech and blockchain technology including cryptocurrencies have the potential to improve the situation of financial inclusion, especially in developing countries. The conducted survey on consumer perceptions of cryptocurrencies reveals that there exists an important growth potential for the use of cryptocurrencies in Mexico. However, security issues, distrust, a lack of technological and financial education and deficient regulation are major obstacles on the way.
Sri Handini, Garry Brumadyadisty, Susanto Soekiman, Denpharanto Agung Krisprimandoyo
The emergence of tokenized derivatives marks a significant innovation in decentralized finance (DeFi), offering potential improvements in market liquidity and pricing efficiency through blockchain-enabled mechanisms. As financial markets evolve with the integration of smart contracts and distributed ledgers, understanding how user perceptions influence market dynamics becomes increasingly critical. This study aims to examine the effect of perceived adoption of tokenized derivatives on market liquidity and pricing efficiency, while assessing the mediating role of liquidity in this relationship. Grounded in the Technology Acceptance Model (TAM), Innovation Diffusion Theory (IDT), and Market Microstructure Theory (MMT), the research utilizes Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data from 150 fintech professionals based in Surabaya. The analysis reveals that perceived adoption significantly enhances both liquidity and pricing efficiency, with liquidity serving as a key mediating variable. These findings underscore the importance of behavioral constructs in shaping decentralized market outcomes and provide strategic insights for regulators, fintech developers, and policymakers aiming to accelerate adoption and improve market functionality in the DeFi landscape through perception-driven approaches.
Ahmed M. Tawfik, Ayman Al-Ahwal, Adly S. Tag Eldien, Hala H. Zayed
Abstract In recent years, blockchain technology has emerged as a promising solution for securing electronic health records (EHRs) while preserving patient privacy. Traditional e-health systems facilitate EHR sharing among healthcare providers but also introduce significant privacy risks, such as unauthorized access and data breaches. Blockchain, when integrated with privacy-preserving techniques, enhances transparency, integrity, and availability in EHR management. Smart contracts further strengthen security by enabling automated authentication and access control. This paper provides a comprehensive survey of blockchain-based access control frameworks in healthcare, categorizing them into permissioned and permissionless approaches. It also explores cryptographic privacy-preserving techniques designed to mitigate privacy risks. Additionally, blockchain platforms and consensus protocols commonly used in these frameworks are analyzed. The methodology follows a structured paper selection process, leading to the final inclusion of 45 research papers focusing on blockchain-based privacy preservation and access control in healthcare. Furthermore, it presents real-world case studies that illustrate the practical implementation of blockchain-based access control in healthcare settings, highlighting their strengths and challenges. Finally, it identifies privacy-related challenges, open research issues, and future directions to guide further research in this evolving domain.
The article examines the financial aspects of the development of Ukraine's energy sector. It analyzes the current state of the country's energy sector and outlines its prospects for development in the post-war period, taking into account financial, institutional, and technological factors. It is determined that the war has led to large-scale destruction of generating capacities, electricity transmission and distribution systems, and damage to gas transportation infrastructure, which has led to increased energy risks and greater dependence on external sources of supply. The restoration of energy infrastructure requires significant investment resources, modernization of outdated equipment, and the introduction of innovative technologies that will ensure increased efficiency in energy production and distribution. Particular attention is paid to the financial aspects of post-war recovery in the industry. The possibilities of attracting domestic and foreign investment are considered, including the use of public-private partnership mechanisms and participation in international financial programs, as grant financing and targeted credit lines play an important role in the modernization of energy facilities and the introduction of renewable energy sources. The potential for decentralization of the energy system, which involves the development of local mini- and micro-generation facilities, energy storage devices, etc., was analyzed separately. The importance of integrating renewable energy sources, in particular solar and wind energy, was highlighted. The importance of digitizing energy system management processes, automating metering, and improving the level of cyber protection of critical infrastructure is emphasized. Strategic directions for the development of the energy sector are identified, including expanding Ukraine's integration into the EU energy market, creating favorable conditions for investors, and introducing mechanisms to support innovative projects. The proposed measures are aimed at forming a sustainable, competitive, and environmentally balanced energy sector capable of ensuring the country's energy security, supporting economic growth, and promoting Ukraine's integration into the common European energy space.
The demand for organ transplants is growing rapidly, yet the existing systems for organ donation face significant challenges, including lack of transparency, delays, and fraudulent activities. This paper explores a novel approach to address these issues by leveraging blockchain technology. Blockchain offers a decentralized, secure, and tamper-proof environment that can improve the efficiency and reliability of the organ donation process. By incorporating smart contracts and distributed ledger principles, the proposed system ensures that donor and recipient data are securely recorded, access is appropriately regulated, and organ matching and allocation are carried out transparently. The integration of blockchain also enhances trust and minimizes administrative overhead, making the donation process more accountable and streamlined. The study also outlines a conceptual framework for implementing this technology and highlights the potential impact on reducing illegal organ trade and ensuring ethical compliance. The study also explores how blockchain could help in maintaining a nationwide or even global donor registry that is both interoperable and scalable. In doing so, it opens avenues for real-time updates, faster allocation decisions, and the potential to curb illegal organ trafficking. Through a conceptual prototype and system design, the paper illustrates the feasibility of this approach and sets the foundation for future research and real-world implementation.
Remote service delivery and automation using Blockchain and Internet of Things (IoT) are revolutionising healthcare operations. Enhancing healthcare data interchange and providing real-time treatment is becoming more difficult due to the exponential growth of patient populations around the globe. It is still very difficult to develop a digital healthcare platform that is entirely decentralised, secure, trustworthy, interoperable, and scalable, even if existing studies have improved these platforms to improve patient outcomes and reduce hospital visits. This paper proposes a robust and scalable healthcare architecture using Blockchain smart contracts and IoT, with the integration of the InterPlanetary File System (IPFS). By securely storing sensitive medical records, the framework enhances data privacy and interoperability for patients as well as healthcare professionals. A device proxy monitors potentially vulnerable IoT devices and uses cryptography to ensure that data remains private. Experimental evaluations of the system’s performance have focused on key factors, including healthcare record upload, download, access, and mining times. The results show that public healthcare systems based on the Blockchain considerably boost efficiency and performance by integrating IPFS.
Among the plethora of literature on interlinkages in markets, more focus has been on peripheral factors. This study attempts to fill this gap by exploring volatility as driver for interlinkages between Bitcoin, Ethereum, Tether, USD-Coin, Binance Coin (BNB), and the crypto-volatility-index (CVI) from April 2019 to August 2022. Using various wavelet techniques, the study depicts significant interlinkages across short-term, medium-term, and long-term horizons, with relatively stronger interlinkages in the long term. The findings confirm that while CVI does not drive these interlinkages, Ethereum, Bitcoin, and CVI play dominant roles in the short, interim, and medium-term periods, respectively, offering new insights into the dynamism of cryptocurrency markets.
This article explores how the integration of Artificial Intelligence (AI), Machine Learning (ML), Web3.0, Blockchain, Metaverse, and Non-Fungible Tokens (NFTs) will revolutionize various aspects of public life globally over the next decade. We introduce novel perspectives such as AI-driven decentralized governance, blockchain-based universal basic income, and metaverse-enabled global education platforms. These technologies will transform global supply chains through AI-driven forecasting and blockchain-verified logistics, ensuring transparency and efficiency. Healthcare will advance with AI-powered telemedicine and personalized treatments, reducing disparities in underserved regions. Autonomous systems will enhance urban mobility and disaster response, fostering sustainable smart cities. Web3.0 will empower users with decentralized digital identities and data sovereignty, redefining advertising and social media through token-based models. Blockchain will secure academic credentials, streamline insurance, and enable transparent philanthropy, while carbon credit markets promote sustainability. The metaverse will revolutionize remote work, healthcare consultations, and cultural preservation through immersive virtual environments. NFTs will democratize real estate and creative economies, enabling tokenized ownership and secure voting systems. Synergistically, these technologies will create decentralized e-commerce, disaster response systems, and virtual innovation hubs, fostering equitable digital ecosystems. However, challenges like digital divides, AI biases, and blockchain scalability must be addressed to ensure inclusive adoption. This article envisions a future where these advancements redefine governance, economies, and social interactions, paving the way for an innovative, equitable global society. AI-driven avatars and decentralized AI training platforms will further enhance virtual collaboration, while tokenized cultural assets empower communities, ensuring a resilient, inclusive digital future.
As Bitcoin continues to establish itself as a global asset and discussions around relevant regulations become more active, there is an increasing demand for a comprehensive price prediction framework. To address this necessity, this study aims to enhance the accuracy of Bitcoin price predictions by integrating sentiment information with technical indicators, on-chain data, and cryptocurrency price data. Recognizing Bitcoin’s sensitivity to market sentiment, the proposed framework incorporates sentiment features derived from both lexicon-based methods and large language models. As unsupervised sentiment tools can introduce label noise particularly in domain-specific or ambiguous financial contexts, this study combines the outputs of multiple sentiment models at the feature level to construct a more stable representation. This design improves the robustness of downstream regression performance and distinguishes the framework from previous hybrid models that relied on a single sentiment source without component-wise evaluation. Experimental results using a dataset spanning 2700 days showed that the long short-term memory (LSTM) model with a 3-day window achieves the best performance with mean absolute percentage error (MAPE) of 3.93% and R-squared value of 0.99106. Feature importance analysis further demonstrates sentiment index as the most impactful feature, as excluding it resulted in the largest decline in predictive accuracy. Additionally, the model's performance was evaluated under four major volatility periods, revealing MAPE values ranging from 1.49 to 4.03%, highlighting the framework’s practical capability in rapidly adapting to sudden market shifts. In summary, integrating sentiment information attained from multiple language models significantly enhanced prediction accuracy compared to single source approaches. These findings highlight the framework’s practical value for sentiment-informed investment strategies and risk alerts, with a modular design that enables flexible adaptation and potential integration into automated trading systems.
The rapid development of large language models (LLMs) has significantly propelled the development of artificial intelligence (AI) agents, which are increasingly evolving into diverse autonomous entities, advancing the LLM-based multi-agent systems (LaMAS). However, current agentic ecosystems remain fragmented and closed. Establishing an interconnected and scalable paradigm for Agentic AI has become a critical prerequisite. Although Agentic Web proposes an open architecture to break the ecosystem barriers, its implementation still faces core challenges such as privacy protection, data management, and value measurement. Existing centralized or semi-centralized paradigms suffer from inherent limitations, making them inadequate for supporting large-scale, heterogeneous, and cross-domain autonomous interactions. To address these challenges, this paper introduces the blockchain-enabled trustworthy Agentic Web (BetaWeb). By leveraging the inherent strengths of blockchain, BetaWeb not only offers a trustworthy and scalable infrastructure for LaMAS but also has the potential to advance the Web paradigm from Web3 (centered on data ownership) towards Web3.5, which emphasizes ownership of agent capabilities and the monetization of intelligence. Beyond a systematic examination of the BetaWeb framework, this paper presents a five-stage evolutionary roadmap, outlining the path of LaMAS from passive execution to advanced collaboration and autonomous governance. We also conduct a comparative analysis of existing products and discuss key challenges of BetaWeb from multiple perspectives. Ultimately, we argue that deep integration between blockchain and LaMAS can lay the foundation for a resilient, trustworthy, and sustainably incentivized digital ecosystem. A summary of the enabling technologies for each stage is available at https://github.com/MatZaharia/BetaWeb.
Non-fungible tokens (NFTs) have become a significant digital asset class, each uniquely representing virtual entities such as artworks. These tokens are stored in collections within smart contracts and are actively traded across platforms on Ethereum, Bitcoin, and Solana blockchains. The value of NFTs is closely tied to their distinctive characteristics that define rarity, leading to a growing interest in quantifying rarity within both industry and academia. While there are existing rarity meters for assessing NFT rarity, comparing them can be challenging without direct access to the underlying collection data. The Rating over all Rarities (ROAR) benchmark addresses this challenge by providing a standardized framework for evaluating NFT rarity. This paper explores a dimension reduction approach to rarity design, introducing new performance measures and meters, and evaluates them using the ROAR benchmark. Our contributions to the rarity meter design issue include developing an optimal rarity meter design using non-metric weighted multidimensional scaling, introducing Dissimilarity in Trades (DIT) as a performance measure inspired by dimension reduction techniques, and unveiling the non-interpretable rarity meter DIT, which demonstrates superior performance compared to existing methods.
Recent advancements in money laundering detection have demonstrated the potential of using graph neural networks to capture laundering patterns accurately. However, existing models are not explicitly designed to detect the diverse patterns of off-chain cryptocurrency money laundering. Neglecting any laundering pattern introduces critical detection gaps, as each pattern reflects unique transactional structures that facilitate the obfuscation of illicit fund origins and movements. Failure to account for these patterns may result in under-detection or omission of specific laundering activities, diminishing model accuracy and allowing schemes to bypass detection. To address this gap, we propose the MPOCryptoML model to effectively detect multiple laundering patterns in cryptocurrency transactions. MPOCryptoML includes the development of a multi-source Personalized PageRank algorithm to identify random laundering patterns. Additionally, we introduce two novel algorithms by analyzing the timestamp and weight of transactions in high-volume financial networks to detect various money laundering structures, including fan-in, fan-out, bipartite, gather-scatter, and stack patterns. We further examine correlations between these patterns using a logistic regression model. An anomaly score function integrates results from each module to rank accounts by anomaly score, systematically identifying high-risk accounts. Extensive experiments on public datasets including Elliptic++, Ethereum fraud detection, and Wormhole transaction datasets validate the efficacy and efficiency of MPOCryptoML. Results show consistent performance gains, with improvements up to 9.13% in precision, up to 10.16% in recall, up to 7.63% in F1-score, and up to 10.19% in accuracy.
As cryptocurrencies gain popularity, the digital asset marketplace becomes increasingly significant. Understanding social media signals offers valuable insights into investor sentiment and market dynamics. Prior research has predominantly focused on text-based platforms such as Twitter. However, video content remains underexplored, despite potentially containing richer emotional and contextual sentiment that is not fully captured by text alone. In this study, we present a multimodal analysis comparing TikTok and Twitter sentiment, using large language models to extract insights from both video and text data. We investigate the dynamic dependencies and spillover effects between social media sentiment and cryptocurrency market indicators. Our results reveal that TikTok's video-based sentiment significantly influences speculative assets and short-term market trends, while Twitter's text-based sentiment aligns more closely with long-term dynamics. Notably, the integration of cross-platform sentiment signals improves forecasting accuracy by up to 20%.
Introduction: Digital content, including images and videos, is increasingly ruling the online world, and so multimedia services form a part of this modern life. However, the digital resources face significant problems, especially regarding copyright infringement. In such an instance, any modification without authority infringes intellectual property rights. Methods: Based on Inter Planetary File System (IPFS) and blockchain technology, a decentralized and distributed framework has been proposed in this study for dealing with insecurity over digital assets and openness of multimedia resources. In this respect, secure, transparent, and immutable transactions in regard to the transfer and ownership of creative works have been facilitated by the use of such a framework. Results: This paper proposes novel decentralized and Blockchain enabled framework to address the problem of video copyright protection by employing solidity based smart contract in a Ethereum network, that allows the content creators to register their videos. The designed smart contract performs copyright checks and release copyright disputes by generating and comparing perceptual hash's (Phash) for original video and modified video. Discussion: Phash techniques play a crucial role in multimedia content analysis, particularly in verifying the integrity and similarity of the video data under various transformations. Additionally, the framework generates Inter Planetary File System (IPFS) main values that signifies the ownership of the video content. Then it compars the phash values, IPFS and similarly score in public Blockchain environment i.e. Ethereum. The framework performance was measured by simulating the contracts of the Application Binary Interface (ABI), JSON file in the Hyperledger Caliper environment. This result shows the performance in the form of video registration, the measured latency was 5.02 seconds with a throughput of 409.87 seconds. For video verification the latency was 4.57 seconds with a throughput of 484.23 seconds.
Digital currency, as an emerging financial instrument, is having a profound impact on the traditional financial system. This paper explores the transformative role of digital currencies on the global financial system by analysing the types of digital currencies, their technological foundations and their impact on the areas of money supply, banking, payment systems and capital markets. First, digital currencies have improved payment efficiency and financial inclusion, especially central bank digital currencies (CBDC) and decentralized finance (DeFi) have driven innovation in payment systems and cross-border payments. Second, the popularity of digital currencies also poses regulatory and compliance challenges, particularly in terms of monetary policy, financial stability, and cross-border regulation. Finally, the paper highlights the potential of digital currencies to drive financial services inclusion and market innovation, particularly in the area of decentralised finance. Nonetheless, issues of technical security, market risk and legal compliance still need to bead dressed. In the future, the development of digital currencies will depend on technological advances and regulatory harmonization on a global scale.
Driven by globalization and digitization, the Mobile Industrial Supply Chain Internet of Things (IoT) has gradually developed, utilizing mobile devices and IoT technologies to enable real-time monitoring and efficient responses across various stages. However, with the growing demand for high-frequency data exchange, the Mobile Industrial Supply Chain IoT faces significant challenges in data security, authentication, and privacy protection. This paper proposes a security authentication scheme based on blockchain and group key management, leveraging the decentralized and tamper-resistant features of blockchain, the privacy-preserving authentication method of Zero-Knowledge Proofs (ZKP), and a hierarchical key management mechanism based on binary key trees. This approach aims to enhance the security and scalability of Mobile Industrial Supply Chain IoT. The experimental section simulates scenarios such as dynamic node addition and key updates, evaluating the performance in terms of encryption, decryption, and key management efficiency, thus demonstrating its superiority in multi-party collaborative environments.
This article examines how the design features of retail central bank digital currencies (CBDCs) influence the detection and prevention of crypto-enabled money laundering. Drawing on a comparative analysis of Russia, the European Union, the United States, and Malta, it evaluates the effectiveness of CBDC-integrated anti-money laundering (AML) mechanisms in addressing the three key stages of illicit finance: placement, layering, and integration. Using primary sources, including pilot program data, legislative texts, and policy consultations, alongside secondary academic literature, the Study explores how design elements such as ledger visibility, programmable transaction limits, sanctions screening, and tiered privacy structures can be embedded into CBDC infrastructure. The findings reveal significant variation in enforcement capacity, privacy protection, and governance transparency across jurisdictions, shaped by political economy, legal traditions, and technological architectures. The article argues that while CBDCs offer unprecedented opportunities to embed compliance at the core of payment systems, their legitimacy and adoption depend on the careful balancing of enforcement effectiveness with constitutional safeguards, civil liberties, and public trust. Policy recommendations emphasize jurisdiction-specific typology mapping, programmable safeguards, stakeholder engagement, tiered anonymity, cross-border interoperability, and independent oversight. The analysis concludes that CBDCs, if responsibly designed, can modernize AML frameworks and strengthen financial integrity without undermining democratic principles.
Ronald Sakaya, Charles B. Niwagaba, A.Y. Katukiza, Christoph Lüthi
ABSTRACT This study reviews the status of access to water supply, sanitation, and solid waste management (SWM) services in Uganda, examining the influence of national government policies, institutional frameworks, and financing mechanisms. It employs a mixed‐methods approach, combining qualitative analysis of primary data from key informant interviews with secondary data from systematically reviewed literature, including performance reports, project data from ministries, development partner websites, and peer‐reviewed journal articles. The findings reveal persistent challenges related to intersectoral coordination, overlapping roles, policy inconsistencies, stakeholder conflicts, and funding inefficiencies. Water services receive a disproportionately high percentage of funding (over 80%), whereas sanitation and SWM receive significantly less (approximately 5% and 10%, respectively). Regulations advocate for sustainability, but their implementation in small towns remains weak. Policy analysis at the local level suggests a misalignment that prioritizes SWM over individual water and sanitation services. Decentralization empowers local authorities, but successful service delivery depends on robust enforcement of available policies. The analysis of funding and service delivery gaps underscores the need for equitable resource allocation and integrated approaches. The study concludes by emphasizing the need for active engagement by governments, development partners, local communities, and the private sector to achieve equitable access to basic services.
This study delves into the differences between traditional financial markets, as proxied by their corresponding future contracts, and the cryptocurrency market, focusing on Bitcoin, during major global events: the COVID-19 pandemic, the Russia-Ukraine war, and the Israel–Palestine conflict. It reveals Bitcoin’s increased trading volume post-COVID-19, highlighting its appeal as a digital safe haven. This trend persists during subsequent crises, suggesting a strategic shift towards cryptocurrencies as diversification tools. Despite volume fluctuations, Bitcoin’s price stability reflects investor confidence in its long-term viability. The significant change in EuroStoxx 50 returns during the Israel–Palestine conflict, highlights localized geopolitical influences on markets. The study underscores the importance of considering both global and regional factors in investment decisions. It emphasizes cryptocurrencies’ growing significance in the global financial market, particularly during crises, and suggests further exploration into investor behavior and regulatory effects. Understanding these dynamics is crucial for navigating the evolving financial landscape.
Suhail Adel Alansary, Sarah M. Ayyad, Fatma M. Talaat, Mahmoud M. Saafan
Abstract The rise of artificial intelligence (AI) revolutionized both cybersecurity defenses and cybercriminals' methods to exploit vulnerabilities. Cybercriminals continue to exploit previously undiscovered vulnerabilities, known as zero-day attacks, posing severe threats to cybersecurity. These attacks are particularly challenging to detect, as they target unknown weaknesses in systems before security teams can respond or act. Traditional intrusion detection systems (IDS) rely heavily on pre-existing attack signatures, making them ineffective against zero-day threats. Machine learning (ML) algorithms have recently become a promising solution for enhancing IDS capabilities by identifying anomalies and predicting potential vulnerabilities in real time. This review paper explores how cutting-edge AI techniques, specifically ML, DL, and federated learning (FL), are harnessed to counter zero-day attacks. AI is used to defend against cyberattacks that exploit vulnerabilities unknown to existing security software. This research explores different AI methods used in cybersecurity, analyzes the data used to train these AI models, and evaluates how well various algorithms perform in actual cyberattacks. Moreover, key challenges in deploying ML for zero-day detection are highlighted, including handling imbalanced data, generalization across diverse types of attacks, and the trade-offs between accuracy and computational cost. The paper outlines future research directions to enhance AI-based zero-day attack defenses and strengthen proactive cybersecurity strategies.