Background: Delay-Tolerant Networks (DTNs) are engineered to function in environments with intermittent connectivity, making them vital for applications ranging from wildlife tracking to disaster response. However, their defining store-carry-forward nature exposes them to unique security vulnerabilities, such as black hole and gray hole attacks, which are difficult to counter with traditional security protocols. Existing DTN routing mechanisms, while efficient in message delivery, often lack robust, integrated security layers, creating a critical need for novel defense strategies.

Methods: This paper introduces and evaluates a Geospatial Anomaly Detection (GAD) framework designed to enhance security in DTNs. Using The ONE (Opportunistic Network Environment) simulator, we modeled node mobility based on real-world map data from OpenStreetMap. We implemented the GAD framework on top of the MaxProp routing protocol [2]. The framework establishes normal behavior profiles by analyzing nodes' historical geospatial-temporal data. Anomalies are flagged when a node's movement significantly deviates from its established patterns, indicating a potential compromise or malicious intent. We evaluated the framework's effectiveness against a simulated black hole attack.

Results: The simulations demonstrate that the GAD framework is highly effective. It successfully identified over 94% of malicious nodes (True Positive Rate) while maintaining a False Positive Rate below 5%. Crucially, this security enhancement introduced minimal network overhead, with a negligible impact on key performance metrics such as packet delivery ratio and latency when compared to the baseline MaxProp protocol operating in a non-hostile environment.

Conclusion: The findings confirm that leveraging geospatial data for anomaly detection is a viable and potent strategy for securing DTNs. The proposed GAD framework offers a practical and resource-efficient security layer that can be integrated with existing routing protocols. This approach represents a significant step toward building more resilient and trustworthy communication systems for challenged network environments.

Background: As organizations migrate to cloud-centric workflows, the web browser has emerged as the primary interface for enterprise data access. Traditional perimeter-based security models are increasingly insufficient against sophisticated threats targeting the application layer. This paper explores the efficacy of Secure Enterprise Browsers (SEB) as a critical enforcement point within Zero Trust Architectures (ZTA), specifically within the high-stakes context of the financial services industry.

Methods: We employed a Fuzzy Multi-Criteria Decision Making (MCDM) approach to evaluate security technologies. Drawing upon methodologies typically used for personnel selection, we adapted fuzzy logic algorithms to assess three distinct remote access mechanisms: Virtual Private Networks (VPNs), Virtual Desktop Infrastructure (VDI), and Secure Enterprise Browsers. The evaluation criteria included data leak prevention (DLP) capabilities, user experience (UX), deployment cost, and maturity alignment with NIST 800-207 standards.

Results: The Fuzzy MCDM analysis reveals that while VDI provides high security, it suffers from significant cost and UX penalties. Secure Enterprise Browsers demonstrated the highest composite score for balance between security efficacy and operational agility. Specifically, SEBs reduced the theoretical attack surface by 40% compared to standard browsers patched with extensions.

Conclusion: The findings suggest that the Secure Enterprise Browser is not merely a tool but a strategic imperative. By decoupling security from the underlying device and embedding it into the browser, organizations can achieve a higher maturity level in Zero Trust, particularly in sectors where data privacy and financial stability are paramount.

This article presents an integrative theoretical framework for Adaptive DevSecOps that tightly couples threat intelligence, dynamic data management for continuous retraining, privacy-by-design, and containerized CI/CD security to enable automated, risk-aware security decisions before code reaches production. The work synthesizes multiple strands of recent scholarship and practitioner guidance—threat intelligence mapping, blockchain-enabled intelligence lifecycle support, continuous retraining pipelines, DevOps metrics and project alignment, container security techniques, regulatory privacy constraints, and event-consistency tradeoffs in distributed systems—into a single, coherent model for securing modern cloud-native software delivery. The article first identifies gaps in contemporary DevSecOps practice, then defines methodological constructs for integrating automated threat feeds, retraining controls, and privacy-preserving data governance within CI/CD workflows. A detailed, text-based methodology describes architecture patterns, data flows, decision points, and governance controls; results are presented as conceptual outcomes and expected operational benefits, grounded in the referenced literature. The discussion examines theoretical implications, counter-arguments, limitations (including data quality, false positives, and compliance complexity), and avenues for future empirical validation. This contribution intends to guide researchers and practitioners toward provable, auditable, and privacy-respecting automation of security actions in the software delivery lifecycle.