印孚瑟斯

Cybersecurity threats are becoming increasingly sophisticated, making traditional defense mechanisms and manual red teaming approaches insufficient for modern organizations. While red teaming has long been recognized as an effective method to identify vulnerabilities by simulating real-world attacks, its manual execution is resource-intensive, time-consuming, and lacks scalability for frequent assessments. These limitations have driven the evolution toward auto-mated red teaming, which leverages artificial intelligence and automation to deliver efficient and adaptive security evaluations. This systematic review consolidates existing research on automated red teaming, examining its methodologies, tools, benefits, and limitations. The paper also highlights current trends, challenges, and research gaps, offering insights into future directions for improving automated red teaming as a critical component of proactive cybersecurity strategies. By synthesizing findings from diverse studies, this review aims to provide a comprehensive understanding of how automation enhances red teaming and strengthens organizational resilience against evolving cyber threats.

Large Language Model (LLM) agents increasingly interact with external systems through tool-calling protocols such as the Model Context Protocol (MCP). In prevailing architectures, the agent must reason about every tool invocation in every session, consuming tokens proportional to the number of actions performed–even when the task has been solved before. We present the MCP Workflow Engine, a novel MCP-native orchestration layer that decouples intelligence (deciding what to do) from execution (carrying it out). An agent reasons once to produce a declarative workflow blueprint–a JSON document specifying a directed sequence of MCP tool calls with parameterized templates, loops, parallel branches, and data piping. Subsequent executions are triggered by a single run_workflow tool call, consuming one invocation's worth of tokens regardless of the blueprint's internal complexity. We formalize the MCP Mediator architectural pattern–an MCP server that simultaneously acts as a client to downstream MCP servers–and implement it in TypeScript against the MCP SDK. We evaluate the engine on a production-scale Kubernetes CMDB synchronization task spanning 67 orchestrated steps across 2 MCP servers, 38 namespaces, 13 worker nodes, and 22 distinct resource types. The engine reduces per-execution token cost by over 99

Gas flaring, practiced for over 160 years in oil production, has become a critical environmental and economic issue; wasting substantial resources and accelerating climate change. Each year, nearly 148 billion cubic meters of natural gas are burned off globally, emitting methane (CH4), black carbon, and volatile organic compounds. These pollutants contribute to respiratory health problems, increased cancer risks, and extensive environmental degradation, especially impacting communities near flaring sites. If harnessed, this flared gas has the potential to fuel substantial energy needs on a national or even continental scale. This review explores the transformative potential of converting flare gas into low-carbon hydrogen (H2), a process that addresses both greenhouse gas (GHG) reduction and clean energy generation. Examining advanced scientific and technological pathways, this study discusses the thermodynamics, catalyst development, and gas purification challenges necessary for efficient H2 production. Economic considerations are also assessed, with attention to cost-benefit analyses, carbon markets, and examples where H2 generation from flare gas has proven financially viable and sustainable. Case studies showcase diverse implementations worldwide, providing a blueprint for scaling H2 production from flaring operations. This review also highlights the prospects of flare gas-to-H2 conversion as a critical pathway for advancing sustainable energy and decarbonization innovations. Combinedly, it offers a valuable resource for researchers, industry leaders, and policymakers, advancing sustainable H2 production from flare gas and supporting critical efforts in global decarbonization.

The increasing demand for secure and efficient healthcare data management necessitates innovative solutions beyond traditional Electronic Health Record (EHR) systems. This work presents BEST, a blockchain-based EHR management platform enhanced with AI-powered speech transcription to address challenges in data security, accessibility, interoperability, and compliance brought forth by modern clinic-based care. By integrating the InterPlanetary File System (IPFS) for decentralized data storage and smart contracts on a private Ethereum blockchain for secure metadata management, BEST ensures tamper-proof and transparent medical data handling. The system employs Angular framework for the frontend user interface and Django backend framework for FHIR-compliant interoperability to ensure seamless interaction and functionality. BEST offers a secure, patient-centered platform that simplifies clinical documentation, is compliant with such regulations as HIPAA, GDPR, DPDP and is architecturally designed for frictionless data exchange based on FHIR standards, breaking down data silos between ambulatory settings and the broader healthcare ecosystem. The system design followed by an implementation with a Raspberry Pi platform and a general computing platform is demonstrated.

The rapid progression of Internet of Medical Things (IoMT) and Wireless Body Area Network technologies drastically transformed remote healthcare monitoring by facilitating continuous and real-time patient monitoring. The challenge of establishing secure data transmission together with reliable communication systems and accurate cardiovascular disease (CVD) prediction models still remains as a key obstacle. The research presents a modern healthcare monitoring system that utilizes IoMT and Wireless Body Area Network technologies to monitor patient vital signs while processing data through cloud-based computing systems. The feature selection process uses the Binary Grasshopper Optimization Algorithm (BGOA) as its first step before applying the Bidirectional Long Short-Term Memory (BiLSTM) network to perform disease binary classification. The Cleveland heart disease dataset serves as the training and evaluation resource. The experimental results demonstrate that the proposed model achieves 99.80% training accuracy and 99.50% testing accuracy, together with 99.35% precision and 99.41% recall, and an F1 score of 99.26% during the testing process. The training and assessment performance results show only minor differences, which demonstrate high generalization ability and strong performance capacity. The proposed framework delivers a reliable monitoring system that effectively scales to provide secure real-time cardiovascular disease monitoring and clinical decision support.