Search Results (707)

Agentic AI is changing enterprise cybersecurity as AI systems move beyond passive content generation toward autonomous planning, tool use, delegated execution, and operational action. As agents connect to email, code repositories, security operations center (SOC) platforms, finance workflows, cloud services, and enterprise application programming interfaces (APIs), they increasingly function as dynamic non-human identities rather than conventional software tools or service accounts. Existing identity and access management (IAM), Zero Trust, machine identity, and AI-governance approaches remain fragmented in their treatment of delegated authority, task intent, autonomy, runtime tool use, and auditable organizational consequences. This paper addresses these gaps by proposing the AIGATE (Agentic Identity Governance, Authority, Tool-Control and Evidence) Framework. AIGATE integrates eight governance layers: agent identity registration, lifecycle governance, delegated authority mapping, intent-bound access, least agency and least privilege, runtime tool-call control, audit evidence and accountability, and revocation and resilience. The framework treats agents as governed non-human enterprise identities whose actions remain attributable to designated human and organizational roles. AIGATE is developed through a structured critical synthesis of the recent literature on agentic AI security, machine identity, Zero Trust, runtime enforcement, and AI governance, with literature-derived governance requirements mapped explicitly to the eight framework layers. Three SOC, DevOps, and finance scenarios are used as illustrative applications rather than empirical validation. The contribution is an integrated governance architecture connecting identity, delegated authority, autonomy, runtime enforcement, evidence, and revocation across the agent lifecycle. Full article

In today’s digital infrastructure, organizations and enterprises increasingly rely on distributed and remote IT environments. These environments may include multiple network sites, remote branch offices, employee home networks, or servers hosted in geographically distant locations. Monitoring and managing these networks presents a critical challenge, especially when administrators are expected to maintain security, performance, and uptime across all nodes. This paper presents an architecture for a cross-platform, multi-tenant network monitoring and management system that enables centralized visibility and control over a distributed infrastructure. Full article

The transformation of forest landscapes is driven by complex socioecological dynamics. This research analyzes territorial transitions in the Zapotec indigenous community of San Pedro el Alto, Oaxaca, Mexico, from 1997 to 2021, linking vegetation cover changes with the consolidation of its community forest enterprise. Through a mixed methods design, we integrated a multitemporal geospatial analysis of cartographic series with systematic archival research and qualitative triangulation based on semi-structured interviews with key community stakeholders. Our results reveal a strong temporal correspondence, indicating that forest canopy alterations align with institutional milestones and silvicultural cycles rather than representing arbitrary degradation. The evidence indicates that intense forest disturbance and anthropic transitions, such as agricultural expansion, correspond with industrial operational crises, functioning as temporary, community-regulated social buffering mechanisms to secure livelihoods. Under the theoretical principles of the commons and New Institutional Economics, the forest functions as a highly resilient socioecological system where local governance and landscape modifications respond adaptively to sustain collective wellbeing. These findings highlight the value of combining spatial data with qualitative verification, proving that recognizing collective self-governance can inform contemporary territorial planning models to support biocultural heritage conservation. Full article

Resilience is increasingly emerging as a core concern in supply chain systems. Given the growing strategic importance of industry chain security, this paper focuses on the evaluation of resilience in automotive supply chains. Firstly, it identifies the influencing factors of supply chain resilience from an attribute perspective and develops an evaluation index system covering the key attributes, secondary attributes, and environmental attributes of the automotive supply chain. Then, it proposes an integrated resilience evaluation method that combines subjective and objective weighting approaches. Finally, taking the typical automotive enterprise BYD as an example, this paper verifies the feasibility of the proposed evaluation indicators and methodology through systematic application. The results show that the input-output ratio of R&D and return on education and training are the two most critical indicators affecting automotive supply chain resilience. These findings provide solid support and feasible suggestions for automotive enterprises to enhance their supply chain resilience. Full article

The evolution of modern enterprise architecture has been strongly influenced by distributed web services, especially protocol-based standards such as Simple Object Access Protocol (SOAP) and resource-oriented architectural styles such as Representational State Transfer (REST). Cloud-native ecosystems, microservice architectures, and public API management commonly favor the lightweight, JSON-compatible, and horizontally scalable characteristics of RESTful services, whereas legacy configurations and highly regulated environments continue to use SOAP because of its formal contracts and compatibility with WS-* specifications for message-level security, reliable messaging, and transaction coordination. This paper presents a structured literature review that evaluates the architectural trade-offs, performance patterns, security boundaries, reliability considerations, and enterprise use cases of REST and SOAP. The review follows PRISMA-informed reporting practices and software-engineering review guidance, but it is not presented as an exhaustive systematic review because the original search strategy required REST and SOAP terms to appear together. IEEE Xplore, ACM Digital Library, ScienceDirect, and Scopus were searched for studies published between 2021 and 2026, resulting in 32 selected studies. The selected literature contains different evidence roles, including direct REST-SOAP empirical comparisons, REST-only and SOAP-only empirical studies, implementation studies, analytical papers, surveys, reviews, and contextual technical sources. The synthesis therefore separates direct empirical evidence from contextual and secondary evidence. The findings indicate that RESTful APIs generally show lower latency, smaller payloads, simpler parsing, and better horizontal scalability in the reported benchmark and web-facing settings, while SOAP remains relevant where formal service contracts, message-level protection, reliable messaging patterns, and transaction coordination are required. The paper identifies gaps in production-representative stress testing, empirical security comparison, reference-level traceability, and independent validation of hybrid REST-SOAP decision models. The resulting decision framework is presented as a provisional evidence-informed decision aid, not as an empirically validated tool. Full article

Remote-access virtual private networks (VPNs) are a key component of enterprise security infrastructures. In practice, the effectiveness of a remote-access VPN is determined not only by cryptographic mechanisms but also by its ability to remain available and recover quickly under realistic operating conditions, which directly affects the operational security guarantees provided by the underlying cryptographic protocols. Enterprise deployments are characterized by heterogeneous client platforms, wireless access networks, and frequent endpoint and network disruptions. In this paper, we execute an exploratory case study of the operation of remote-access VPNs in enterprise environments through an empirical evaluation of WireGuard, OpenVPN, and IPsec. Using a controlled but realistic testbed with a cloud-hosted gateway and heterogeneous client platforms, we evaluate baseline performance as well as behavior under endpoint CPU stress, network impairments, MTU variation, and mobility-related disruptions, reflecting constrained and dynamically changing deployment conditions. The results suggest that VPN operational characteristics are influenced by both protocol design and execution environment. Within the evaluated deployment scenarios, kernel-based implementations generally exhibited higher resilience under endpoint resource contention and faster recovery after disruptions, while layered and virtualized environments exhibited increased variability and sensitivity to network imperfections. These findings underline that resilience in remote-access VPNs should be interpreted as a system-level property emerging from the interaction of implementation architecture, endpoint characteristics, and deployment conditions. Full article

Fruit and vegetable supply chains generate heterogeneous data across production, storage, logistics, and sales, creating challenges for trusted data sharing, privacy protection, and real-time traceability across distributed supply-chain information systems. Conventional single-chain blockchains suffer from limited scalability, data redundancy, and low retrieval efficiency, making them inadequate for high-frequency full-process information management. This study proposes a multi-chain blockchain framework for trusted full-process information management of fruit and vegetable supply chains. The framework integrates traceability, enterprise, notary, and regulatory chains to support hierarchical data management and privacy isolation. A reputation-based notary node election mechanism and a threshold-signature scheme based on Shamir secret sharing are designed to enhance cross-chain security and distributed regulatory consensus. To improve retrieval efficiency, a Cuckoo-Augmented Merkle Tree (CMerkle) and a skip-list-based block index are developed. Simulation results show that all malicious nodes were restricted by the 19th round, signature aggregation required 70.16 ms in a 500-node setting, and CMerkle achieved retrieval speedups of 14.7 and 153 times at data scales of 500 and 10,000 records, respectively. The framework supports trusted data governance, real-time traceability, privacy-preserving sharing, and regulatory decision support in blockchain-enabled supply-chain information systems. Full article

Ecuadorian aquaculture plays an increasingly important role in food security, providing a sustainable complement to capture fisheries. Manabí constitutes a key economic sector that contributes to employment generation, income diversification, and supply of high-quality protein. However, the sector’s heterogeneity and limited legal registration of enterprises hinder the development of policies and targeted interventions. This study characterizes aquaculture enterprises in Manabí, integrating productive, technological, and socio-economic variables obtained from field surveys. Three distinct entrepreneurship patterns were identified: small-scale, informal farms cultivating native species with low technology but characteristics that may contribute to socio-ecological resilience; semi-formal producers operating with intermediate technology and mixed species; and freshwater shrimp farms operating at the largest relative scale within the sample, though still small-scale and family-based, with moderate technological development and market integration limited mainly to local and regional markets. The typology focused on a gradient in formalization, technological sophistication, and market orientation among enterprises. These findings provide a comprehensive understanding of aquaculture diversity in Manabí and offer a practical framework for differentiated policy design using a Strategic Producer Positioning Map. Strengthening formalization, technological innovation, environmental management, and social inclusion is essential to enhance the sector’s sustainability and competitiveness, contributing to sustainable blue economy development in coastal Ecuador. Full article

We examine whether simulated attack-action evidence can inform a defender that they must commit before an attacker’s type is known. We formulate a five-stage Bayesian Stackelberg security game with five stage-specific actions per player. A Monte Carlo predictor produces type-conditioned action likelihoods on an enterprise graph, while prediction confidence weights the next Bayesian update. The defender strategy is computed by exact follower-response enumeration and linear programming. Evaluation used a simulated 10-node enterprise network, 30 paired trials, bootstrap confidence intervals, and Holm-adjusted Wilcoxon tests. Against a fixed-prior Bayesian Strong Stackelberg Equilibrium, mean gross defense utility increased from 2.141 to 2.197. Mean attack success decreased from 0.691 to 0.686. The paired differences remained significant after multiplicity correction. Outcomes did not differ significantly from an equilibrium updated with coarse reference likelihoods, and the simulation cost reduced net utility by 0.08. Maximum follower regret and constraint violation remained at the specified numerical tolerance. Runtime remained near 0.39 s across networks of 10–100 nodes. An action/type experiment showed rapid growth as follower-response profiles increased. Exact commitment and sequential updating were feasible in the abstraction; simulation was not automatically cost-effective when a usable reference model was available. Full article

Small and medium-sized enterprises (SMEs) face increasing cyber threats, while existing cyber resilience approaches remain largely conceptual or provide static assessments with limited predictive capability. This study develops a dynamic mathematical framework for analysing and forecasting cyber resilience in SMEs. Building upon a previously developed conceptual model, the framework formalises the interactions among company security, cyber risk, cybersecurity capability, incident response and recovery, and digital maturity using normalised state variables, bounded nonlinear difference equations, and autoregressive forecasting. The theoretical analysis establishes boundedness of the state variables, equilibrium existence, and local stability of the proposed dynamic system. The framework further integrates computational resilience metrics, a Dynamic Resilience Index (DRI), scenario analysis, and sensitivity analysis within a unified analytical structure. An illustrative simulation demonstrates the computational implementation of the framework by generating resilience trajectories, supporting conditional forecasting, and comparing alternative cybersecurity scenarios. The study concludes that cyber resilience can be represented as a dynamic and measurable organisational capability. The proposed framework provides a transparent and extensible mathematical basis for continuous resilience monitoring, predictive analysis, and evidence-based cybersecurity decision-making in resource-constrained SMEs. Full article

Confidential ledgers cannot usually be pooled across entities, yet generic private federated learning does not preserve the identities that make accounting data meaningful. This paper develops Balance-Aware Federated Analytics (BAFA), a constrained federated framework that combines voucher-level differential privacy, debit–credit and period-continuity regularization, account-hierarchy smoothing, secure aggregation, and a one-sided balance-aware update correction. The formulation defines neighboring ledgers by replacement of one complete voucher, bounds the sensitivity of the released representation, model update, and compressed balance sketch, and composes one cached representation release and all round-level aggregate releases with a Rényi differential-privacy accountant that explicitly models the minimum number of non-colluding noise contributors. It also specifies period-complete aggregation for multi-line vouchers, derives the one-sided correction from a half-space projection, and gives first-order balance-safety, hierarchy-stability, convergence, and complexity results under non-IID data, clipping, privacy noise, and sketch error. Our experiments use PaySim, IEEE-CIS Fraud Detection, and UCI Online Retail transformed into accounting-style multi-entity ledgers. The reported points indicate that BAFA improves predictive utility and normalized balance consistency relative to private federated baselines while keeping membership-inference attack AUC near random guessing. The transformed-ledger evaluation is intended as controlled evidence; validation on native enterprise ledgers remains necessary. Full article

The rapid integration of digital technologies including cloud infrastructures, Internet of Things ecosystems, artificial intelligence, and large-scale enterprise platforms has reshaped the operational and architectural foundations of contemporary organizations. Although these technologies enhance flexibility, scalability, and analytics-driven decision processes, they simultaneously increase system complexity, broaden attack surfaces, and intensify governance requirements. This manuscript adopts an engineering-oriented viewpoint on digital governance and enterprise cybersecurity, focusing on secure system architectures, policy-driven control mechanisms, and resilience-focused operational strategies. An integrated framework that unifies governance structures, quantitative risk assessment, compliance automation, cybersecurity engineering is introduced to enable secure-sustainable digital transformation across enterprise environments. Full article

The growing use of blockchain in e-commerce has produced hybrid environments in which private enterprise ledgers and public blockchain networks operate side by side. Consequently, efficient and secure interoperability between these networks has become increasingly important. This study presents a cross-chain interoperability framework that links a permissioned Hyperledger Fabric network with a public Ethereum network. The framework provides attestations of selected business events rather than moving assets. An interoperability smart contract on Fabric emits cross-chain events; an off-chain validator enforces uniqueness and replay protection; and a public verification contract on Ethereum records an immutable, publicly verifiable attestation of each event. The framework uses a two-of-three validator threshold to attest events, so safety holds as long as no more than one of the three validators is compromised. The prototype was evaluated by processing 21,000 events across sequential, concurrent, and peak-load workloads. On the local network, message validation averaged approximately 12 ms per event, and the interoperability layer added less than 200 ms of overhead per attestation. Sustained throughput ranged from 13.2 to 14.2 attestations per second, while the validator used approximately 16% mean CPU and less than 194 MiB of memory, with no sustained memory growth during the full experiment. On the Ethereum Sepolia public testnet, 55 transactions were confirmed with a 100% success rate and a mean confirmation time of 10,676.62 ms. Gas consumption stayed stable at about 51,743 gas per verification on the local network and about 189,092 gas on Sepolia, and the mean public testnet transaction cost was 0.000692 Sepolia ETH. Five adversarial tests were conducted, covering replay, forgery, malicious relayers, concurrent replay, and denial-of-service attacks. All five tests passed, including the rejection of 500 concurrent replay attempts with zero double registrations. The results show that the framework provides efficient, verifiable, and replay-resistant cross-chain interoperability suited to hybrid e-commerce ledgers. Full article

Rare earths are a strategically vital mineral resource on a global scale; the security of their supply chain and their recycling face severe challenges amid dual pressures of resource scarcity and environmental protection. This study focuses on the reverse supply chain for rare earth permanent magnet materials—specifically those based on praseodymium and neodymium—constructing a two-level game model comprising a rare earth oligopoly (Stackelberg leader) and two recyclers (Cournot followers). It systematically analyzes the dual decision-making behavior of recyclers between “recycling and selling” and “in-house remanufacturing”, and examines the impact of two policy instruments—government subsidies and environmental taxes—on the supply chain equilibrium. The study employs reverse induction to solve the game equilibrium and combines this with numerical simulation methods to compare the differing effects of the two policies on key indicators such as product output, market share, profit distribution, waste recovery volume, and recycling rates. The results indicate that there is strategic coordination and resource competition in recyclers’ decisions regarding recycling and remanufacturing, causing them to assume dual roles as both “suppliers” and “competitors” within the supply chain. Subsidy policies significantly incentivize recycling and remanufacturing activities, thereby increasing the recycling rate, but exert a slight squeeze on the profits of rare earth conglomerates. Environmental tax policies effectively curb primary mining and promote resource circulation, but may have a negative impact on the remanufacturing industry. Currently, value within the closed-loop rare earth supply chain is highly concentrated among upstream oligopolistic enterprises, whilst the recycling segment suffers from insufficient economic incentives and significant policy dependency. This study provides theoretical support and decision-making references for the government to optimize policy combinations and promote the high-quality development of the rare earth recycling industry. Full article

Railway safety is an important component of public security. Ensuring the safety of high-speed rail systems and passengers is also crucial for railway transportation enterprises. The performance of X-ray security inspection systems is one of the key factors in improving intelligent railway security. Previous studies have shown that, due to the complexity of X-ray images, both traditional vision methods and deep learning approaches struggle to meet the requirements of real-world railway security inspection. With the development of vision-language models, this paper proposes XrayCLIP, a CLIP-based method designed to improve the accuracy and robustness of computerized X-ray security inspection. XrayCLIP adapts CLIP to the semantic and imaging characteristics of prohibited-item inspection through security-oriented prompts, texture-aware visual representations, and global–local supervision. The key component of the model is a set of learnable prompt templates, which guide the model to learn generic features of prohibited objects in complex environments. Multi-level global–local (glocal) features enable the model to focus on both global context and local details, while text space optimization, texture enhancement, text–image fusion, and inference enhancement further improve model performance. XrayCLIP is evaluated on the PIDray and derived HiXray(seg) benchmarks against eight conventional and recent single-view baselines under a common evaluation protocol. A railway-station study is additionally conducted using operational X-ray data, including a same-set missed-detection comparison with a commercial system for knives and power banks. The results show that XrayCLIP achieves the best performance among the evaluated methods on PIDray and HiXray(seg), while reducing the missed-detection rate relative to the commercial system on the two categories examined. Full article