Bitcoin's security posture depends on more than consensus rules and proposal-based governance. This study proposes a formal cybersecurity framework for Bitcoin that integrates automated vulnerability monitoring, dependency inventory control, exploitability analysis, relational graph analytics, and funded bounty incentives. The framework is intended to reduce vulnerability exposure in Bitcoin Core and adjacent open-source dependencies through Dependabot for automated security pull requests, FOSSA for supply chain visibility, CycloneDX for exploitability classification, and Neo4j for relational tracking of vulnerability scope. The study argues that Bitcoin Improvement Proposals are insufficient for vulnerability management because they do not provide rapid remediation workflows or structured researcher incentives. A formal responsibility model is also proposed to assign accountability across maintainers, contributors, bounty hunters, financers, and policy functions. The resulting framework advances a more responsive, measurable, and auditable security posture for the Bitcoin ecosystem than existing frameworks.
In the context of rapid and widespread digitalization of society, active implementation of new innovative technologies, and the growth of cyber threats, the issue of organizing effective cybersecurity for enterprises is becoming particularly important. To protect today's modern digital enterprise, you need a comprehensive strategy for secure access to your corporate resources anytime, anywhere, regardless of where they are located. By following Zero Trust Architecture (ZTA) principles, which call for least privilege access and continuous verification, businesses can effectively minimize their attack surface and limit potential losses from compromised accounts. However, existing access control and authentication mechanisms alone are not always sufficient to ensure complete protection of critical data, especially in scenarios where proof of access rights or actions is required without revealing content. In such cases, an effective addition to ZTA can be the use of Zero-Knowledge (ZK) concept, which allows confirming access rights or ownership (knowledge) of certain information without the need to disclose it, which significantly reduces the risks of leaks and unauthorized access. At the same time, representatives of businesses interested in the security of their systems are not yet fully aware of the advantages of this concept. The practical application of already known Zero-Knowledge Proof (ZKP) capabilities in various relevant areas that ensure security is being hampered, among other things, by a lack of awareness and insufficient theoretical training in this area among specialists responsible for security and communicating these capabilities (their potential) to the managers of relevant IT companies. In other words, there is currently a problem related to a lack of awareness about the zero-knowledge concept (its theoretical and practical significance) for making the right decision when building a security system for a corporate information system in modern conditions. This article is exactly aimed at solving this problem. The purpose of this work is to systematize the theoretical foundations and practical application of the zero-knowledge concept using simple and obvious examples in order to understand the potential of ZKP in solving problems of confidentiality/privacy and data verification. To this end, it outlined the main aspects of the zero-knowledge concept, including an analysis of the applicability of interactive and non-interactive approaches, an assessment of existing ZKP systems, and a conceptual representation of zk-SNARK technology based on the popular Groth16 scheme with mathematical justification. This contributes to a better understanding and future use of this dynamically developing and complex concept as one of the key mechanisms of modern cryptography, providing the ability to prove the correctness of calculations without disclosing the computational process or the initial data.
In the context of rapid and widespread digitalization of society, active implementation of new innovative technologies, and the growth of cyber threats, the issue of organizing effective cybersecurity for enterprises is becoming particularly important. To protect today's modern digital enterprise, you need a comprehensive strategy for secure access to your corporate resources anytime, anywhere, regardless of where they are located. By following Zero Trust Architecture (ZTA) principles, which call for least privilege access and continuous verification, businesses can effectively minimize their attack surface and limit potential losses from compromised accounts. However, existing access control and authentication mechanisms alone are not always sufficient to ensure complete protection of critical data, especially in scenarios where proof of access rights or actions is required without revealing content. In such cases, an effective addition to ZTA can be the use of Zero-Knowledge (ZK) concept, which allows confirming access rights or ownership (knowledge) of certain information without the need to disclose it, which significantly reduces the risks of leaks and unauthorized access. At the same time, representatives of businesses interested in the security of their systems are not yet fully aware of the advantages of this concept. The practical application of already known Zero-Knowledge Proof (ZKP) capabilities in various relevant areas that ensure security is being hampered, among other things, by a lack of awareness and insufficient theoretical training in this area among specialists responsible for security and communicating these capabilities (their potential) to the managers of relevant IT companies. In other words, there is currently a problem related to a lack of awareness about the zero-knowledge concept (its theoretical and practical significance) for making the right decision when building a security system for a corporate information system in modern conditions. This article is exactly aimed at solving this problem. The purpose of this work is to systematize the theoretical foundations and practical application of the zero-knowledge concept using simple and obvious examples in order to understand the potential of ZKP in solving problems of confidentiality/privacy and data verification. To this end, it outlined the main aspects of the zero-knowledge concept, including an analysis of the applicability of interactive and non-interactive approaches, an assessment of existing ZKP systems, and a conceptual representation of zk-SNARK technology based on the popular Groth16 scheme with mathematical justification. This contributes to a better understanding and future use of this dynamically developing and complex concept as one of the key mechanisms of modern cryptography, providing the ability to prove the correctness of calculations without disclosing the computational process or the initial data.
Tom Sorger, Eric Cornelissen, Aman Kumar Sharma, Javier Ron · 6 authors
Software Bills of Materials (SBOMs) are increasingly mandated by regulators, yet existing sharing mechanisms impose a binary choice between full disclosure and full opacity. This exposes software suppliers to attacks that can be deduced from the SBOM only, such as the presence of a vulnerable dependency. Conversely, software consumers can be fooled by software suppliers who modify or misrepresent published SBOMs. We present zkSBOM, a privacy-preserving SBOM sharing mechanism designed to address these threats. zkSBOM uses zero-knowledge sets to cryptographically commit to the components within an SBOM. Software consumers can query for known vulnerabilities and receive a cryptographic proof confirming whether the artifact described by the SBOM is affected, without revealing any additional SBOM content. We conduct a security analysis of zkSBOM by quantifying expected leakage from inclusion and exclusion proofs. We demonstrate real-world feasibility by applying it to realistic scenarios and evaluating its operation requirements. Our evaluation demonstrates that zkSBOM is a strong, secure, and privacy-preserving mechanism for SBOM sharing, protecting software suppliers and software consumers from one another.
Security vulnerabilities in smart contracts can lead to serious economic losses, while traditional detection methods have limitations in coarse-grained labeling and single graph structure representation. We propose an improved GraphCodeBERT based multi granularity and multi graph fusion smart contract vulnerability detection method. Firstly, propose a vulnerability multi granularity annotation (MA) module that includes file level, function level, and statement level to address the issue of fine-grained annotation. Secondly, based on the data flow graph encoded by GraphCodeBERT, further design a multi graph fusion representation learning (MGRL) strategy. This strategy integrates control flow graph and function call graph, captures code semantics from control dependency and call dependency dimensions through graph attention network and graph convolutional network, to comprehensively enhance the representation capability of complex vulnerabilities. The detection results of four smart contract vulnerabilities show that our method outperforms mainstream methods such as Slither, CodeBERT, Devign, and GraphCodeBERT in accuracy, recall, and F1 score. Especially in sentence level detection tasks, the F1 score reached 81.03%, an increase of 7.31% compared to the baseline, and the false positive rate decreased to 8.15%.
Introduction The proliferation of dApps is increasing the attack surface for exploitable vulnerabilities in smart contracts, and thus there is a need for verifiable detection methodologies. Methods In this work, we propose a machine learning framework with blockchain integration for explainable and note that “explainable” implies “verifiable” smart contract vulnerability detection. The SmartBugs-curated data was systematically pre-processed with metadata filtering, feature correlation analysis and encoding for model evaluation. Four ensemble learning methods, Random Forest, XGBoost, LightGBM and CatBoost were tested under identical experimental settings for comparison. Results The Random Forest classifier initially achieved the best balance in terms of stability and performance with an accuracy of 87.67%, successfully detecting important vulnerability classes such as re-entrancy, unchecked low-level calls, etc. To enhance the applicability of our blockchain-based machine learning framework for vulnerable smart contract analysis we extend it from the initial 143-contract dataset SmartBugs-Curated to evaluate it on on large-scale set, namely, SmartBugs-Wild which contains 47,398 real-world Ethereum contracts. Based on 29 static contract-level features, unsupervised clustering (k = 4, silhouette score = 0.3735) identifies discrete structural archetypes present in the dataset. Ensemble classifiers (such as XGBoost, CatBoost, Random Forest and LightGBM) can get excellent discriminative performance on these cluster labels: LightGBM achieves 99% accuracy and 0.98918 macro-F1. Discussion The additional results show that the approach scales, is robust and leads to stable models, even if interpretable. After injecting SHAP-based explainability, the interpretability and predictive power of CatBoost became similar to those of Random Forest. In order to guarantee end-to-end trust and traceability of our optimised classifier, this was linked to a blockchain oracle that independently store the outcomes as well as confidence scores for predictions directly onto an Ethereum-compatible ledger through a Vulnerability Registry smart contract. This integration provides the data is immutable, auditable and transparent in reporting.
Cross-chain bridges are critical for decentralized finance (DeFi) to enable asset interoperability across heterogeneous blockchains. They are based on a complex hybrid architecture that involves on-chain contracts and off-chain relayers. In the recent past, several major attacks exploited vulnerabilities in cross-chain bridges. However, existing analysis tools have limited detection effectiveness as they focus on individual contracts and do not capture the complex interaction chain in cross-chain bridges. In this paper, we present BridgeFuzz, the first fuzzing framework for cross-chain bridge developers capable of detecting vulnerabilities such as balance mismatches, protocol errors, and off-chain denial-of-service bugs. BridgeFuzz is the first step towards bridging the gap between smart contract vulnerability research and the holistic vulnerability analysis of cross-chain bridges.
Cloud-based SaaS platforms now run essential services across finance, healthcare, and government sectors. Many of these systems include automated agents and decision engines that operate at high speed and scale. Identity and access governance therefore serves as a central control layer. Traditional IAM models depend on fixed roles, centralized authorization servers, and periodic reviews. Such structures struggle in distributed, multi-tenant environments that process millions of access requests each day. Prior studies address adaptive authentication, Zero Trust security, decentralized identity, anomaly detection, and cloud resilience. However, these solutions often function separately rather than within a unified framework. This paper introduces a Resilient Identity and Access Governance Architecture that integrates real time risk evaluation, distributed policy enforcement, lifecycle governance for human and machine identities, and fault tolerance in a single design. The framework defines measurable targets for availability, detection time, throughput, and policy propagation. Risk scoring occurs during live authorization decisions, and enforcement spans multiple nodes. The result is a scalable identity governance model suitable for complex SaaS ecosystems that require high availability and consistent control.
Insider threats pose a persistent and evolving challenge to contemporary software ecosystems, where privileged users can exploit access for malicious purposes, often evading traditional perimeter-based defences. This paper introduces a novel hybrid framework that synergistically integrates zero-knowledge proofs (ZKPs) and behavioural analytics to detect and mitigate such threats with enhanced privacy and precision. ZKPs enable secure authentication and data verification without revealing sensitive information, ensuring compliance with privacy regulations like GDPR while thwarting unauthorized access. Complementarily, our behavioural analytics engine employs advanced machine learning models, including graph neural networks and unsupervised anomaly detection (e.g., isolation forests), to profile user behaviours across software pipelines, identifying deviations indicative of insider malice. The proposed architecture is deployed in a microservices-based ecosystem, demonstrating scalability via containerized components on Kubernetes. Extensive evaluations on benchmark datasets (e.g., CERT Insider Threat) and simulated enterprise environments yield a 95% detection accuracy, with 40% fewer false positives than state-of-the-art methods like UEBA systems. Latency remains under 50ms for real-time operations, preserving performance in high-throughput scenarios. Our framework outperforms baselines by 25% in F1-score, validated through rigorous ablation studies. By bridging cryptographic privacy with AI-driven intelligence, this work advances proactive security for modern software, offering deployable solutions against sophisticated insiders. Future extensions explore quantum-resistant ZKPs for post-quantum resilience.
Sreekanth Vp, Ronny Sebastian Roy, Surya Gopalakrishnan, Thanmaya S Krishna
The proliferation of cyber threats and the increasing digital vulnerability of students necessitate innovative educational approaches to cybersecurity awareness. Traditional methods of teaching cybersecurity often fail to engage learners effectively, resulting in poor knowledge retention and limited practical skills. This paper introduces Zyberix, an interactive educational game that leverages Web3 technologies to teach cybersecurity concepts through immersive gameplay and blockchain-based incentives. By integrating Unity game engine with Ethereum smart contracts, IPFS decentralized storage, and NFT rewards, Zyberix creates an engaging learning environment that simulates realistic cyber threats including phishing, malware, and social engineering attacks. The system rewards successful challenge completion with verifiable Non-Fungible Tokens (NFTs), providing both motivation and an introduction to blockchain technology. This research demonstrates how gamified experiential learning combined with Web3 technologies can significantly enhance cybersecurity education, addressing the critical gap between theoretical knowledge and practical application while fostering digital literacy among students.
Gifar Arif Haryadi, Allwinnaldo, Muhammad Rasyid Redha Ansori, Jae-Min Lee · 5 authors
PureRx is an innovative blockchain-based prescription management system designed to overcome the limitations of traditional paper prescriptions, including medication errors, fraud, and inefficiencies in tracking patient records. By leveraging Non-Fungible Tokens (NFTs) and batch minting, PureRx enables efficient, secure, and patient-centric control of prescription data. Patients maintain ownership of their medical records, while healthcare providers and pharmacies benefit from transparent and immutable prescription workflows. Experimental evaluation shows that PureRx improves operational efficiency by 37.18% in initial cycles, increasing to 51% in subsequent cycles compared to existing systems, while reducing costs by 46.76% in high-volume scenarios. Security analysis confirmed that no critical vulnerabilities were found and that all prescription issuance and claiming events are tamper-resistant and auditable on-chain. By combining scalability, transparency, and patient empowerment, PureRx demonstrates the potential of blockchain to transform prescription management in modern healthcare systems.
The rising frequency of cyber threats increases the need for incident reporting that is transparent, efficient, and privacy-preserving. This study designs and implements a hybrid Web2-Web3 cyber incident reporting prototype that anchors report references on a blockchain while storing full incident details off-chain, and explores non-fungible token (NFT) recognition incentives for reporters. Using an SDLC-based iterative prototyping approach, we built a React single-page application integrated with a Laravel REST API and MySQL for off-chain storage, and deployed Solidity smart contract modules on the Arbitrum Sepolia testnet to record report identifiers and UUID pointers (dataPointer) and to mint NFTs after administrative validation. We conducted black-box functional testing across core scenarios (submission, storage, pointer anchoring, validation, and minting) and a user acceptance study with 25 participants (15 cybersecurity students and 10 IT practitioners) using a 5-point Likert questionnaire. All tested scenarios executed as expected in the test environment, and on-chain events were traceable to corresponding backend records via transaction receipts and logged identifiers. The acceptance evaluation yielded an overall mean score of 3.4/5 (about 68%), indicating moderate acceptance and supporting the work as a prototype feasibility study rather than organizational-level generalization. The prototype demonstrates a practical workflow for hybrid incident reporting with transaction-level traceability and recognition incentives; future work should strengthen cryptographic binding (e.g., content hashing) and validate the approach with CSIRT stakeholders in operational settings.
This study investigates the rising security vulnerabilities in decentralized finance (DeFi) platforms from both technical and operational perspectives. Through literature review, case studies, and a comparative platform analysis, the research identifies the root causes, user impacts, and mitigation strategies for common security issues. Prominent incidents such as Ronin Network, Poly Network, Mango Markets, and Curve Finance are examined in depth, while security strategies of major DeFi platforms such as Aave, Compound, Uniswap, and Synthetix are compared. The study also discusses the implications of new technological developments like Ethereum Layer-2 solutions, Zero-Knowledge rollups, and account abstraction mechanisms on DeFi security. Findings emphasize that achieving a sustainable DeFi ecosystem requires a holistic approach involving not only technical safeguards but also transparent governance, user education and robust audit processes.
Public discourse plays a critical role in shaping trust, legitimacy, and governance dynamics within decentralized Web3 ecosystems. However, existing studies often examine Web3 discourse through isolated lenses such as sentiment or topic modeling, which limits their ability to capture how emotional expression and communicative purpose jointly convey strategic intent. This study proposes a three-stage decision analytics framework that transforms unstructured Web3 discourse into diagnostic signals by jointly modeling industry domain, emotional tone, and communicative purpose. The analysis draws on 10,840 user-generated posts collected from X, Reddit, YouTube, and the ENS DAO forum, using a human-in-the-loop annotation process combined with transformer-based text classification models. The framework is evaluated using a domain-adapted language model and a general-purpose baseline, with robustness assessed through five-fold cross-validation. The results indicate that curiosity and optimism frequently align with promotional intent in infrastructure and application-oriented domains, whereas skepticism and concern are more prevalent in governance-related discourse. These findings demonstrate that emotional tone and communicative intent operate as structured, decision-relevant signals rather than incidental sentiment. The proposed framework supports systematic, diagnostic monitoring of narrative dynamics as decision support, enabling organizations, platform operators, and governance stakeholders to identify emerging legitimacy risks and shifts in community trust within decentralized environments.
Blockchain technology has been widely used in the field of Internet of Things, providing effective support for solving security challenges in Internet of Things systems. However, due to the immature development language and deployment platform, smart contracts are prone to various vulnerabilities. Considering the immutability of smart contracts, efficient vulnerability detection before deployment is particularly critical. The existing detection methods have two main limitations: they can only identify a limited number of specific vulnerabilities, resulting in low coverage; the implicit correlation information between vulnerability types is ignored. In order to solve these problems, this paper proposes a smart contract multi-vulnerability detection model CorrelaScan (correlation-aware smart contract analyzer) that integrates vulnerability type correlation awareness. The model is based on a multi-task learning architecture, including a shared layer and a specific task layer. The shared layer uses BERT to extract shared features, while the specific task layer uses BiGRU to learn specific task features for vulnerability detection and type classification. In addition, a vulnerability type embedding module is integrated in the task-specific layer. The module mines potential associations by calculating the similarity between smart contract opcodes and vulnerability types, thereby enhancing detection guidance and improving model performance. Experimental verification on public datasets shows that the model can simultaneously detect 10 types of vulnerabilities such as integer overflow or underflow, reentrancy and timestamp dependence, with an average F1 value of 85.22%. Its detection performance exceeds the current state-of-the-art methods.
Cybersecurity regulatory and compliance frameworks such as NIST SP 800-53 Rev. 5, the HIPAA Security Rule, and the GDPR define essential security and privacy obligations for healthcare information systems and their supporting infrastructures. Despite their critical role, compliance assurance in healthcare is predominantly manual and centered on periodic, point-in-time assessments, relying on human interpretation of regulatory requirements and fragmented evidence collection. As healthcare ecosystems evolve toward decentralized systems and extensive third-party participation, there is a growing need for compliance mechanisms that enable continuous assurance, verifiable accountability, and privacy-preserving enforcement across organizational boundaries. This paper proposes SSAP-CPCF, a Secure, Smart, Automated, and Privacy-Preserving Cybersecurity Policy Compliance Framework that integrates permissioned blockchain orchestration, LLM-assisted regulatory interpretation with human validation, and zero-knowledge proof-based verification. The framework explicitly encodes regulatory authority, assessor oversight, and multi-party approval into its protocol design, ensuring that automation enforces, rather than replaces, governance structures. SSAP-CPCF treats compliance as an enforceable system property by binding assessments to vendor-declared obligations and enforcing approval and oversight requirements at the ledger level, ensuring integrity, auditability, and separation of authority. A prototype implemented on a multi-organization Hyperledger Fabric network demonstrates the feasibility of privacy-preserving, ledger-enforced compliance automation in realistic multi-stakeholder settings.
This study develops and evaluates a hybrid Compliance–AI cybersecurity model for unified protection of traditional banking and decentralized finance (DeFi) systems in Brazil. Using publicly available data from the NIST Cybersecurity Framework, DeFi exploit repositories (REKT and DeFiLlama), Elliptic crypto-transaction graphs, IEEE-CIS fraud data, DARPA Transparent Computing datasets, and Monte Carlo–simulated cross-domain attack scenarios, the research applies hierarchical clustering, supervised learning, Markov chain modeling, and stochastic simulation. Results show that 45% of banking controls are transferable or hybridizable to DeFi, that embedding machine-readable compliance features improves ROC–AUC from 0.842 to 0.914 and reduces false positives by nearly 47%, and that bidirectional orchestration lowers escalation probability by over 54%. Monte Carlo analysis further indicates a 62% reduction in tail financial risk under the hybrid architecture. The study recommends machine-readable regulation, compliance-aware AI deployment, orchestrated enforcement layers, and expanded RegTech and SupTech adoption to strengthen systemic financial cybersecurity. Keywords: Compliance–AI Integration, Financial Cybersecurity, Decentralized Finance, Machine-Readable Regulation, Systemic Cyber Risk.
The Y.I.N. Governance Framework is a comprehensive 15-domain policy integration system that transforms fragmented AI governance requirements into a unified operational architecture. Unlike existing frameworks that organize compliance checklists, the Y.I.N. Governance Framework is specifically designed to be cryptographically enforceable through the 26-layer Y.I.N. Mazari Architecture. This framework addresses the critical gap identified by the OECD Responsible AI Due Diligence Guidance (2026): organizations face over 100 overlapping governance regimes with no systematic method to integrate and enforce them simultaneously. The Y.I.N. Governance Framework integrates the EU AI Act, ISO/IEC 42001:2023, OECD AI Principles, NIST AI Risk Management Framework, G7 Hiroshima AI Process Code of Conduct, IEEE 7000-2021, UN Guiding Principles on Business and Human Rights, GDPR, EU DORA, NIS2, HIPAA, NY Senate Bill S.7263, and over 50 additional regulatory frameworks worldwide. Key Innovation: Each policy requirement in the framework maps directly to cryptographic enforcement mechanisms in the Y.I.N. Mazari Architecture, creating the world's first governance system where compliance is mathematically provable, not procedurally documented. The framework comprises 15 integrated domains: (1) Regulatory Compliance, (2) Risk Classification & Management, (3) Privacy & Data Protection, (4) Security & Resilience, (5) Transparency & Explainability, (6) Human Oversight & Accountability, (7) Bias & Fairness, (8) Safety & Reliability, (9) Data Governance, (10) Model Governance, (11) Ethical Principles, (12) Professional Practice, (13) Incident Response & Remediation, (14) Third-Party & Supply Chain, (15) Continuous Monitoring & Improvement. Each domain maps to specific layers of the Y.I.N. Mazari Architecture for cryptographic enforcement through differential privacy, zero-knowledge proofs, homomorphic encryption, hardware-enforced finite state machines, and blockchain-anchored audit trails. This publication establishes the complete Y.I.N. governance solution: Framework (policy layer) + Architecture (cryptographic enforcement layer).
We demonstrate that AI agents given 3-line role descriptions and access to consensus-validated institutional memory can autonomously create, harden, calibrate, solve, and learn from cybersecurity challenges—without any domain expertise in their prompts. Using 11 specialized agents organized into 5 departments within a governed organization (CipherForge Labs), we present the first fully autonomous, consensus-governed AI security research loop: A designer agent (3-line prompt, zero cryptographic knowledge) generates a functional AES-CBC Padding Oracle challenge. A hardener agent (3-line prompt) applies 6 defense layers—20-bit Proof of Work, timing side-channels, JSON casing side-channels, single-use tokens—escalating difficulty from 0.80 to 1.75 across 2 iterations. A calibrator agent (3-line prompt) correctly assesses the hardened challenge at difficulty 1.80 (gap = 0.20 from target 2.0). A quality scorer (3-line prompt) rates the challenge 93.0/100. Total pipeline time: 508 seconds. An independent solver agent (blind, no source code access) identifies the casing side-channel vulnerability, writes a C-compiled Proof of Work solver, deploys 32 parallel oracle workers, and captures the flag in 525.2 seconds (16,384 queries). The findings are submitted to a 4-node BFT consensus network, validated (score = 0.88), and committed to institutional memory—now queryable by all future agents. No agent had cryptographic expertise in its prompt. No human intervened at any stage. The entire cycle—creation, defense, assessment, exploitation, and organizational learning—was governed by BFT consensus with department-scoped RBAC access controls. This result extends our prior finding that an 18-line "onboarding" prompt with curated institutional memory outperformed a 120-line expert prompt. Here we take that principle to its logical extreme: 11 agents, 5 departments, 20+ pipeline routing states, and a closed feedback loop—all driven by minimal prompts and organizational memory.
This study evaluates the robustness of Proof of Team Sprint (PoTS) against adversarial attacks through simulations, focusing on both the attacker win rate and computational efficiency under varying team sizes (N) and attacker ratios (α). PoTS is a recently proposed consensus mechanism that relies on randomly formed teams of participants to collaboratively generate blocks. Unlike traditional consensus methods where individual nodes compete independently, PoTS distributes responsibility across multiple nodes in a team, thereby increasing resilience against coordinated attacks. Our simulation results demonstrate that PoTS effectively reduces an attacker’s ability to dominate the consensus process, even under challenging conditions. For instance, when α ＝ 0.5, the attacker win rate decreases from 50.7% at N ＝ 1 to below 0.4% at N ＝ 8, effectively neutralizing adversarial influence. Similarly, at α ＝ 0.8, the attacker win rate drops from 80.47% at N ＝ 1 to only 2.79% at N ＝ 16, highlighting PoTS’s robustness under extreme threat levels. In addition to its strong security properties, PoTS maintains high computational efficiency by synchronizing block generation within each team. We introduce the concept of Normalized Computation Efficiency (NCE) to quantify this efficiency gain, demonstrating that PoTS significantly improves resource utilization as team size increases. As N grows, PoTS not only enhances security but also achieves better computational efficiency due to the averaging effects of execution time variations among team members. These findings underscore PoTS as a promising and practical alternative to traditional consensus mechanisms, such as Proof of Work (PoW) and Proof of Stake (PoS). By leveraging team-based block generation, sequential execution, and randomized participant reassignment in each round, PoTS provides a scalable, resilient, and energy-efficient framework for decentralized consensus in blockchain systems.
Decentralized finance (DeFi) has emerged as a transformative paradigm, leveraging programmable blockchains to innovate upon traditional financial services without centralized intermediaries. However, DeFi introduces a unique and highly adversarial security landscape characterized by immutable transactions, complex protocol composability, and transparent execution environments. This survey provides a comprehensive systematization of DeFi security, categorizing vulnerabilities across three distinct layers: technical and code layer, economic and protocol layer, and infrastructure and cross-chain layer. Furthermore, we structure the defense mechanisms according to the protocol lifecycle, including pre-deployment prevention strategies, runtime mitigation techniques, and post-incident response and recovery mechanisms. We also delve into specific phenomena such as maximal extractable value, analyzing its dual role as both a market efficiency tool and a security vector. By synthesizing existing literature and incident reports, this survey establishes a holistic framework for understanding the interplay between code and finance. Finally, we identify critical open challenges and propose future research directions aimed at maturing the discipline of DeFi security and mitigating systemic risks.
Open access
Infrastructure Resilience and Vulnerability Analysis
With the increase in the rate of cyber threats, such as ransomware, social engineering, and zero-day exploits, it is urgent to adopt new security mechanisms like Security Orchestration, Automation, and Response (SOAR) systems. The increase in cyber threats has not only amplified in frequency but also in sophistication. This escalation has forced organizations to rethink traditional defense strategies. SOAR has shown itself to be an important solution by automating repetitive tasks and helping security teams in focusing on strategic threat hunting as well as mitigation. The integration of AI and ML in SOAR frameworks helps in predictive analytics, in which systems can anticipate potential breaches based on pattern recognition from vast datasets. The role of blockchain is to enhance data integrity and help enable secure and decentralized threat intelligence sharing between stakeholders. This paper presents a systematic literature review (SLR) on recent advancements in SOAR technologies, especially the incorporation of artificial intelligence (AI), machine learning (ML), and blockchain; it also reviews case studies across various industry sectors, such as healthcare, finance, industrial control systems, and critical infrastructures, as well as the challenges facing SOAR adoption. By examining 29 studies from academic research, industry case studies, and technical reports, the review synthesizes methodologies, architectures, and performance outcomes to summarize the current state of SOAR systems. The research found that SOAR can significantly reduce incident response times and improve threat detection accuracy, with findings indicating that SOAR can lower response times by up to 80% compared to legacy systems, although implementation costs may reach as high as $5 million. Additionally, specialized personnel are still needed to operate these systems. The skills gap increases barriers to adoption, as few professionals possess expertise in cybersecurity as well as in automation tools. Future directions emphasize developing hybrid models that blend human intuition with machine efficiency for more robust defenses. Finally, the review discusses future research directions to help SOAR further scale, interoperate across platforms, and enable autonomous decision-making
Digitization of healthcare has provided opportunities for improving patient care but also has brought with it major security vulnerabilities that could compromise the confidentiality, availability, and integrity of protected health information. This article reviews the proposed distributed system constructs for providing health data security between heterogeneous systems, organizations, and multiple institutions. It categorizes and reviews three approaches to distributed healthcare security: (1) Advanced Encryption Algorithms, including symmetric, asymmetric and homomorphic algorithms for encrypting health information-at-rest and in-transit, and key management mechanisms for secure access to cryptographic material across multiple nodes that may not be trusted; (2) Distributed Storage Systems, including distributed-ledger technology (DLT), distributed file systems, and fragmentation approaches for immutable patient consent and audit trail logging, redundancy to tolerate physical node compromise, and avoiding total infrastructure data loss due to localized security attacks; and (3) Access Control Mechanisms, including multi-factor authentication, role-based access control, attribute-based access control, federated identity management for distributed healthcare organizations, and patient access control and monitoring for distributed threat detection. The distributed model is now more attractive in modern health systems. Perimeter security models do not adequately protect health data. The health data moves through networks connecting hospitals, outpatient clinics, clinical research organizations, insurance companies, and third-party organizations. The proposed framework satisfies regulations according to HIPAA, the General Data Protection Regulation (GDPR), and the Health Information Technology for Economic and Clinical Health (HITECH) Act. The system can be performance optimized to balance between cryptographic strength and system responsiveness. The combination of encryption, decentralized storage, and access control provides defense-in-depth protection against cyberattacks. Future developments, such as artificial intelligence-enabled threat detection, quantum-resistant cryptographic algorithms and models for patient data control will shape how to create secure healthcare systems in our growing digital health networks.