Zhaoyang Xie, Haibin Zhang, Sisi Duan, Chao Liu · 11 authors
A key management service (KMS) is vital to modern mission-critical systems. At the core of KMS are the key generation process and the key refresh process. In this paper, we design and implement a purely asynchronous system for completely distributed KMS supporting traditional applications such as threshold cryptosystems and multiparty computation (MPC) as well as emerging blockchains and Web3 applications. In this system, we have built a number of new asynchronous distributed key generation (ADKG) protocols and their corresponding asynchronous distributed key refresh (ADKR) protocols. We have demonstrated that our ADKG and ADKR protocols in the standard model outperform existing ones of the same kind, while our protocols in the random oracle model (ROM) are more efficient than other protocols with small and medium-sized networks.
The healthcare sector increasingly explores Distributed Ledger Technology (DLT) and Health Web 3.0 Decentralized Applications (DApps) as promising solutions for patient-centric data management, data sovereignty, and privacy-preserving systems. Despite significant research at the intersection of blockchain and healthcare, current efforts predominantly address isolated technical challenges—focusing narrowly on specific mechanisms such as confidentiality, privacy, or individual smart contract vulnerabilities. Even cybersecurity assessments typically examine discrete attack vectors rather than comprehensive threat landscapes. This fragmented approach limits our ability to build trustworthy systems and delays real-world adoption, as stakeholders lack frameworks to holistically evaluate security posture. This study addresses this gap by conducting a comprehensive threat modeling analysis of Health Web 3.0 DApps, taking into account the complex and interconnected security challenges inherent in blockchain-based healthcare systems. We employ a multi-framework approach integrating LINDDUN threat modeling methodology, OWASP Top 10 Smart Contract Vulnerabilities catalog, and Threat Dragon analytical tool to systematically identify, categorize, and evaluate security risks across the entire application stack. Our analysis maps threats spanning smart contract design flaws, cross-chain interaction vulnerabilities, decentralized identity management weaknesses, unauthorized data access risks, and denial-of-service attack vectors. The primary contribution of this work is demonstrating the critical importance and practical value of holistic threat modeling in blockchain healthcare systems. Our findings reveal interdependencies between seemingly isolated vulnerabilities and show how comprehensive security assessment enhances data privacy protection, smart contract integrity, and overall application resilience. This research provides stakeholders with a systematic methodology for deriving trust in blockchain healthcare solutions, advancing both regulatory compliance and user confidence in decentralized medical data management systems.
Cloud identity management has evolved from a purely technical concern into a fundamental pillar of digital society, creating profound impacts that extend far beyond organizational boundaries. Modern cloud-based identity and access management systems serve as critical infrastructure enabling access to essential services including healthcare, education, government benefits, and financial services. These systems incorporate advanced technical mechanisms such as multi-factor authentication, single sign-on, zero trust architecture, and artificial intelligence-driven fraud detection to establish secure and inclusive digital environments. The transformation to cloud-based architectures addresses traditional limitations of on-premises systems while introducing new capabilities for digital inclusion through device-agnostic authentication, accessibility-first design, and multilingual support. However, this evolution presents significant challenges including privacy concerns arising from data aggregation, potential government surveillance, and algorithmic bias in automated decision-making systems. Strategic implementation through public-private partnerships, investment in open source components, and adoption of emerging technologies such as quantum-resistant cryptography and distributed ledger integration shapes the societal impact of these systems. The technical decisions made in designing and implementing cloud identity infrastructure have far-reaching implications for social equity, democratic participation, and economic opportunity in an increasingly digital world.
Jesus Gama-Rodnguez, Ekam Puri Nieto, Juan Francisco Martínez Gil, Agustín Marín Frutos
Exploration in the crucial role of cyber threat intelligence (CTI) sharing and lifecycle security in IoT ecosystems. It examines how the ERATOSTHENES project leverages distributed ledger technology (DLT) and an inter-ledger approach to facilitate secure and privacy-preserving CTI exchange across different domains. The chapter also discusses the use of Manufacturer Usage Description (MUD) files, including the proposed Threat MUD extension, to manage security configurations and mitigation actions throughout the device lifecycle. Additionally, it highlights the integration of these components to achieve a system that dynamically responds to cybersecurity incidents, ensuring the ongoing protection of devices and domains.
Mohammed Ibraheem Hussein, Ohood Saadoon Hlail, Asma Ibrahim Hussein, Amjed Abbas Ahmed · 6 authors
Balancing efficient threat detection with data privacy becomes increasingly difficult as cyber threats develop in complexity. The Adaptive Zero-Knowledge Threat Hunting Framework (AZTH), a revolutionary integration of zero-knowledge proofs (ZKP) and artificial intelligence (AI) for private and secure cybersecurity operations, is presented in the presented study. AZTH maintains strong confidentiality regarding sensitive data yet uses federated learning, quantum-resistant cryptography, and dynamic deception systems to improve threat intelligence sharing as well as real-time threat mitigation. Together with an assessment of its efficacy in several operating situations, the architecture, approach, and possible uses of the framework are given.
Francesco Salzano, Lodovica Marchesi, Cosmo Kevin Antenucci, Simone Scalabrino · 7 authors
Abstract In this paper, we investigate the strategies adopted by Solidity developers to fix security vulnerabilities in smart contracts. Vulnerabilities are categorized using the DASP TOP 10 taxonomy, and fixing strategies are extracted from 364 commits collected from open-source Solidity projects on GitHub. Each commit was selected through a two-phase process: an initial filter using natural language processing techniques, followed by manual validation. We assessed whether these fixes adhere to established academic guidelines. Our analysis shows that 60.55% of the commits aligned with at least one literature-based recommendation, particularly for well-documented vulnerability types such as Reentrancy and Arithmetic. However, adherence dropped significantly for categories like Denial of Service, Time Manipulation, and Bad Randomness, highlighting gaps between academic best practices and real-world developer behavior. From the remaining 143 non-aligned commits, we identified 27 novel fixing strategies not previously discussed in the literature. To evaluate their quality, we conducted a structured questionnaire involving 9 experts from both academia and industry. Their feedback indicated high perceived effectiveness of the new fixes, especially for vulnerabilities like Reentrancy and Unchecked Return Values. Generalizability received more varied responses, suggesting context-specific applicability. Finally, we performed a post-fix evolution analysis on over 6700 subsequent commits to assess the long-term stability of the fixes. Most patches remained unchanged, confirming their persistence in production code. Our findings offer practical insights into how vulnerabilities are fixed in smart contracts today, reveal promising emerging patterns, and help bridge the gap between academic guidelines and developer practices.
Philip Eappen, Adeyemi Abel Ajibesin, Narasimha Rao Vajjhala
This chapter examines future directions and emerging trends in cybersecurity for knowledge management, emphasizing the necessity of advanced protective strategies as organizations increasingly rely on digital platforms to store, share, and manage knowledge. While digital knowledge management offers efficiency and accessibility, it introduces significant security challenges, such as unauthorized access, data breaches, and knowledge theft. This chapter explores three critical emerging technologies—Artificial Intelligence/Machine Learning (AI/ML), Blockchain, and Quantum Computing—that have the potential to enhance knowledge security. AI/ML aids in threat detection, predictive modeling, and automated responses, allowing for rapid identification of cyber threats. Blockchain provides a decentralized, tamper-proof method for managing knowledge and data integrity, ensuring secure data sharing and transaction transparency. Additionally, the chapter discusses quantum-resistant encryption in response to the potential risks posed by quantum computing advancements. The chapter further examines advanced cybersecurity approaches, including Zero Trust Architecture, Privacy-Enhancing Technologies, and Security Automation, which contribute to safeguarding sensitive information in knowledge systems. This chapter also addresses the ethical and regulatory considerations essential for ensuring compliance and accountability. By integrating these cutting-edge technologies and approaches, this chapter provides a holistic framework for future cybersecurity practices in knowledge management, offering valuable insights for professionals, researchers, and policymakers focused on protecting digital knowledge assets in an evolving cyber landscape.
Nik Zulkarnaen Khidzir, Shekh Abdullah-Al-Musa Ahmed
Artificial intelligence and blockchain technologies are widely used in the Internet of Things (IoT). IoT for collecting and presenting data, blockchain to provide the infrastructure to define operational rules and AI optimization processes and rules are all possible connections between these technologies. AI involves analyzing and interpreting data, making informed decisions and predicting outcomes. In contrast, blockchain technology revolves around a decentralized ledger that securely stores and verifies data. Hence this chapter focuses on how, in blockchains or distributed ledger technologies (DLT), the data is structured into blocks and each block contains a transaction or bundle of transactions. Each new block connects to all the blocks before it in a cryptographic chain in such a way that it’s nearly impossible to tamper with.
Electronic Health Records (EHRs) are now a necessary component of contemporary healthcare, but managing them presents a number of security, privacy, and interoperability issues. In order to solve these issues, this study introduces a unique framework for EHR management that combines four cutting-edge technologies: blockchain, Zero-Knowledge Proofs (ZKP), Ciphertext-Policy Attribute-Based Encryption (CP-ABE), and InterPlanetary File System (IPFS). Our solution makes use of the Ethereum blockchain for transparent and safe record-keeping, IPFS for efficient and decentralized data storage, CP-ABE for fine-grained access control, and ZKP for private authentication. We offer computational proofs for important components together with a thorough security analysis utilizing formal verification tools like ProVerif and Tamarin Prover. Comparing our framework to other alternatives, the findings show that it provides stronger security guarantees, better privacy protection, and increased scalability. Our approach also defends against a broader variety of possible threats, such as man-inthe-middle attack, repudiation attacks, and side-channel attacks. This work opens the door for more effective and patient-cantered healthcare information systems by advancing secure and privacy-preserving EHR management.
Traditional finance and crypto aren't just different systems-they're different paradigms speaking different languages. Traditional banks measure processes in days, compliance in paperwork, and access in restrictions. Cryptocurrency platforms promise instant transactions but often at the cost of compliance frameworks that traditional institutions require. These systemic limitations in both traditional and cryptocurrency systems highlight the critical need for a unified approach. Using STORE's decentralized cloud computing protocol as an example, this paper emphasizes a different future as it demonstrates the feasibility and impact of automated compliance through the transformative CLEAR framework. Our dual-database architecture achieves authenticated regulatory compliance at global scale, with our implementation achieving 55.6 seconds (verified) for complete end-to-end transactions, with KYC verification (18.84s), document processing (14.24s), and payment settlement (6.55s). This transforms what traditional finance considers a weeks-long journey into a seconds-long verification, without compromising the compliance standards that make global finance possible. It's not just faster-it's fundamentally re-imagined.
Cybersecurity has encountered significant challenges, including identity theft, data breaches, and evolving threats to cyberspace. The decentralized, immutable, and transparent characteristics of blockchain technology have significantly enhanced its efficacy in bolstering cybersecurity. The application of blockchain in identity management, data privacy, and threat mitigation is examined, indicating it as a technology that addresses vulnerabilities inherent in conventional systems. Their capacity to enhance security, user autonomy, and trust is evidenced by decentralized Digital Identities (DIDs), smart contract-enforced data utilization policies, and blockchain-based threat intelligence systems. Despite its robustness, blockchain faces challenges, including scalability, interoperability, regulatory compliance, and energy consumption. Emerging trends (blockchain integration with AI and ML, quantum-resistant cryptography, etc.) are moving toward innovative solutions to these issues. Furthermore, the overlap of blockchain with zero-trust architectures highlights the utility of blockchain in present-day cybersecurity frameworks. The use of blockchain in finance is emphasized through this study as a demand for industry collaboration, scalable innovations, and a supportive regulatory framework to unleash the potential of the blockchain. A blockchain solution can help fill existing gaps in security strategies and pave the way to adoptive security.
Money laundering and illicit financial flows facilitate criminal operations and undermine economic stability. Cryptocurrencies present regulatory challenges due to their anonymity and decentralized nature. Anomalous transactions refer to financial transactions that deviate from established patterns, indicating potential fraud, errors, or unusual behavior. This paper reviews machine learning techniques for detecting anomalous cryptocurrency transactions from an anti-money laundering/counter-terrorist financing (AML/CFT) perspective. A real-world Bitcoin transaction dataset is analyzed for our study. The paper assesses how well various machine learning models perform in detecting anomalous transactions. Detecting these anomalies is important in preventing fraud in areas like banking, e-commerce, and financial services.
This study aims to address the challenges and propose solutions for the Optimization of Blockchain-Based Cybersecurity Systems to Enhance Resilience Against Ransomware Attacks using a Systematic Literature Review (SLR) approach. Blockchain is increasingly recognized as a transformative technology in cybersecurity due to its decentralized structure, transparency, and robustness in securing data. Despite these advantages, its widespread adoption is hindered by several challenges, including scalability, interoperability, high energy consumption, and limited access to representative ransomware datasets. This research highlights that integrating blockchain with advanced technologies such as data analytics, machine learning, and Explainable AI (XAI) can significantly enhance its effectiveness in combating ransomware.The findings reveal that Graph Convolutional Neural Networks (GCN) enable real-time detection of ransomware patterns in network traffic with an accuracy of up to 95%. Furthermore, Layer-2 solutions like the Lightning Network and sharding effectively alleviate the load on main blockchains, thereby increasing transaction throughput. Efficient consensus mechanisms, including Proof of Stake (PoS) and Delegated Proof of Stake (DPoS), address energy consumption issues, making blockchain more adaptable to IoT and resource-constrained environments. These approaches have proven successful in enabling early detection, mitigation, and prevention of ransomware in IoT systems, cloud infrastructures, and smart grid networks. The implications of this study underscore the potential of blockchain as a critical component of proactive and adaptive cybersecurity systems. However, overcoming existing challenges requires further development of hybrid frameworks that integrate blockchain with data analytics and machine learning technologies. In addition, efforts should focus on standardizing global security protocols to enhance interoperability and creating robust, diverse ransomware datasets to support more accurate detection systems. Future research should also explore methods to minimize latency and improve blockchain efficiency in real-time cybersecurity applications.
This chapter looks at key features of Web3 and the metaverse from privacy and security angles. It offers a detailed description of technological-environment and institutional-level factors that can lead to increased privacy violations and security breaches in Web3 and the metaverse. The chapter will discuss how newness, novelty, and complexity of technologies involved and weak architectural security of Web3 and the metaverse are likely to provide a fruitful environment for cybercriminals and other perpetrators. On the regulatory front, it points out that privacy and security laws of the Web2 era are not sufficient to deal with the environments of Web3 and the metaverse. It analyzes the level and nature of the impacts of privacy violations and security breaches on consumers and victims in the Web3 and the metaverse environments. It gives special consideration to the multisensory environment of the metaverse, which can lead to more adverse impacts on users and victims in cases of privacy violations and security breaches. The chapter also demonstrates how security breaches in Web3 and the metaverse are likely to lead to immediate harm to victims. It promotes an understanding of how blockchain, which is the key building block of Web 3 and the metaverse, can be vulnerable under certain conditions. The chapter also delves into cyberattacks and other malicious behaviors targeting crypto-assets such as cryptocurrencies and non-fungible tokens (NFTs), which are key enabling technologies of Web3 and the metaverse economies. It provides guidelines and suggestions for consumers, businesses, industries, and nations to enhance security of Web3 and the metaverse.
As the demand for cloud computing is increasing across the globe, more and more companies are shifting their data and IT infrastructure to the cloud. Log data integrity, security, and compliance are important in such an environment for anomaly detection, audit trails, and regulatory adherence. However, traditional log management systems suffer from tampering, data breaches, and centralized control. Recent research has, therefore, looked into blockchain technology as a means of boosting cloud security. This paper presents a blockchain-based approach to securing and authenticating AWS cloud logs. Utilizing the blockchain’s immutable and decentralized nature, the proposed system hashes AWS CloudWatch logs with a Python-based system and records the hash on the Ethereum blockchain via a smart contract. This process prevents undetected log alteration or deletion. The smart contract manages log hash storage and verification, with the Infura node facilitating Ethereum blockchain interactions and MetaMask handling private keys and test ETH. Original and modified log hashes are compared to assure the system’s efficiency, besides computing transaction costs and processing times. The findings suggest blockchain’s potential as a solid framework for log data security and compliance, offering a decentralized, tamper-proof log storage solution that enhances cloud infrastructure security and compliance for organizations.
Prof. R. C. Pachhade, Shubham Gaikwad, Aditya Sawwase, Dahihande Rohan
The idea focuses on enhancing the security and reliability of data exchange between military units. Traditional methods of secure communication often involve centralized systems, which can be vulnerable to breaches and single points of failure. By utilizing blockchain technology, the implementing idea introduces a decentralized approach that ensures data integrity and security through a distributed ledger system. In this system, blockchain provides a tamper-proof record of all communications, ensuring that data is encrypted, verified, and resistant to unauthorized access. This decentralized model eliminates the need for a central authority, reducing potential vulnerabilities and increasing the resilience of the communication network. As a result, the system aims to offer a more secure, reliable, and robust solution for confidential data transmission between army stations, enhancing operational security and efficiency.
Blockchain technology has evolved from being the foundation of cryptocurrencies like Bitcoin to becoming a versatile solution for multiple industries. The unique characteristics of it, such as decentralization, transparency, immutability, and security, have opened up new possibilities in multiple domains, from finance to healthcare, supply chains, and governance.
Growing worries about data security and privacy are driving the development of privacy-enhancing technologies (PETs) like secure multiparty computation (MPC) and zero-knowledge (ZK) proofs. These technologies offer strong theoretical guarantees for protecting sensitive data while still allowing its use. Critical sectors like finance and healthcare are increasingly adopting PETs, facilitated by complex PET systems designed for secure and efficient implementation. However, despite the theoretical strengths of PETs, the intricate nature of these systems can create practical vulnerabilities. Severe incidents have already caused significant financial losses and eroded trust. This thesis tackles these reliability concerns by systematically testing modern PET systems. The first work in this thesis uncovers logic bugs in secure multiparty computation (MPC) compilers. These compilers automatically transform high-level MPC programs, written in domain-specific languages (DSLs), into low-level MPC executables. We introduce MT-MPC, a metamorphic testing (MT) framework, to test MPC compilers using three tailored metamorphic relations (MRs). Despite the high engineering quality of MPC compilers, MT-MPC finds 13 bugs in leading compilers, which compromises the dependability of MPC systems. The second work focuses on the correctness and security of zero-knowledge (ZK) compilers, which compile ZK DSL programs into ZK circuits. We propose MTZK, a MT framework that uncovers logic bugs in ZK compilers. These bugs can allow attackers to generate false ZK proofs that ZK verifiers unexpectedly accept, leading to security breaches and financial losses. MTZK uses two carefully designed MRs to deliver effective test cases for ZK compilers. Evaluation of four industrial ZK compilers reveals 21 bugs. We also demonstrate the severe security implications of these bugs through potential exploits. The third work unveils a new class of vulnerabilities in PET-enhanced machine learning (ML) models. We present ConPETro, the first attack on PET-enhanced ML models with maliciously crafted configurations. These configurations cause PET-enhanced models to behave similarly to plaintext models under normal inputs, but exhibit significantly reduced robustness under trigger-embedded inputs. ConPETro achieves an average maximum attack success rate of 65.6% while maintaining merely 4% of accuracy drop on normal inputs. We also show that such attacks are highly stealthy and can hardly be detected or defended by traditional mechanisms.
Open access
Information and Cyber Security
Transportation Systems and Safety
Physical Unclonable Functions (PUFs) and Hardware Security
Decentralized Finance (DeFi) protocols face significant security challenges, with over $500 million lost to exploits in 2024. While technical vulnerabilities are well-studied, the security implications of novel governance mechanisms remain underexplored, particularly for protocols using competitive dynamics rather than traditional voting. This thesis presents the first comprehensive security analysis of the Foresight Protocol, a DeFi system that optimizes reserve composition through competitive game-theoretic mechanisms. The research adapts traditional threat modeling for blockchain by extending STRIDE with three DeFispecific categories: Governance Vulnerabilities (G), Parameter Interaction Vulnerabilities (P), and Systemic Risks (SR). The analysis reveals that Foresight’s economic design effectively eliminates traditional attack vectors including flash loans, MEV exploitation, and bank runs through stake requirements and extended evaluation periods. However, critical vulnerabilities emerge from the protocol’s governance complexity. The reliance on decentralized decision-making introduces social and educational requirements that actors could exploit through identity spoofing, economic bribery, or information asymmetries. Additionally, the mathematical sophistication of competition parameters creates opportunities for manipulation disguised as optimization, while distinguishing genuine value from market manipulation remains an inherent challenge. Key findings indicate that while economic incentives can provide robust security, governance mechanisms introduce attack surfaces just as significant as technical vulnerabilities. The extended threat modeling framework offers a reusable methodology for analyzing innovative DeFi protocols. The research demonstrates that next-generation DeFi protocols can achieve security through economic design, but success requires sophisticated community capabilities and ongoing empirical validation. This work emphasizes the importance of threat modeling starting from the design phase to ensure comprehensive security in novel DeFi systems.
Open access
Blockchain Technology Applications and Security
Information and Cyber Security
Infrastructure Resilience and Vulnerability Analysis
The article examines the evolution of the concept of Integrated Information Security Systems (IISS) in the context of the digital transformation of the public sector, modernization of the national cybersecurity framework, and harmonization of Ukrainian legislation with international information security standards. The study reveals the relationship between classical approaches to building IISS – based on mandatory certification of technical protection complexes – and the modern paradigm of risk-oriented security management introduced by the new Law of Ukraine No. 4336-IX “On Amendments to Certain Laws of Ukraine on the Protection of Information and Cybersecurity of State Information Resources and Critical Information Infrastructure Objects.” The research emphasizes the shift from a formal certification model to a process-oriented approach based on security profiles, risk management, continuous monitoring, and security auditing. Special attention is devoted to analyzing the potential of blockchain technologies in enhancing the resilience of state information systems against cyberattacks, insider threats, and unauthorized data modifications. The study substantiates the feasibility of using distributed ledgers to ensure the immutability, authenticity, transparency, and accountability of information processes. It is determined that blockchain can serve as an innovative component of the modern IISS architecture, complementing cryptographic protection mechanisms, access control, user activity auditing, and event monitoring. A conceptual model of blockchain integration into the traditional structure of IISS is proposed, forming a new trust ecosystem within state information resources. The combination of technological innovation with the legal requirements of Law No. 4336-IX creates a foundation for improving the effectiveness of the national cybersecurity system. The purpose of the study is to substantiate the scientific, methodological, and technological directions for the modernization of Ukraine’s Integrated Information Security Systems through the integration of blockchain technologies in protecting state information resources in accordance with current legislation and international standards ISO/IEC 27001, ISO/IEC 27701, and GDPR.
Vishnu Priya Arivanantham, Hayder M. A. Ghanimi, Vijaya Krishna Sonthi, Firas Tayseer Ayasrah · 8 authors
The software development model is changing fast, and the challenge of security remains the primary attention, particularly with the implementation of Development and Operations (DevOps), Distributed Ledger Technology (DLT), and other methods. Thus, when such technologies are integrated within organizations, it becomes essential to measure the use of the technologies in Anomaly Detection (AD) and access management. This research work evaluates the competence of these technologies based on measures such as the Detection Rate (DR) and the False Positive Rate (FPR). Statistics reveal that as the number of records increases. DR improves across all methods, with DevOps Resilience with Authentication (DevOps-RAS) achieving the highest DR of 95.94% at 500 records, compared to 81.23% for CAMS and 85.43% for DLT. Conversely, FPR increases with record size, with DevOps RAS presenting a higher FPR of 95.78% at 500 records, compared to 76.45% for CAMS and 83.56% for DLT. These visions should help organizations determine the most effective security method for Cyber-Physical Systems (CPS), thereby maximizing DR while minimizing the FPR required to enhance the system’s overall robustness.