Journal of Cyber-Physical Security and Robotics
The journal aims to provide an international scholarly platform for researchers, practitioners, and policymakers to publish original research, reviews, and case studies addressing security challenges, innovations, and governance of robotic and autonomous systems in cyber-physical environments. Title: Journal of Cyber-Physical Security and Robotics Frequency: Quarterly E-ISSN: 3107-0876 Starting year: 2025 Subject: Science and Technology Language: English Publication format: Online Email ID: editor@jocpsr.com Website: https://jocpsr.com/index.php/journal/index
Designing Generative AI Driven Cloud Native Platforms for Autonomous Threat Detection and Intelligent DevSecOps
The rapid evolution of cloud-native architectures—characterized by microservices, containerization, and dynamic orchestration—has introduced unprecedented operational scale alongside complex security vulnerabilities. Traditional reactive security paradigms fail to keep pace with modern attack vectors that exploit ephemeral infrastructure and continuous deployment pipelines. This research proposes an integrated architecture for designing Generative AI (GenAI)-driven cloud-native platforms tailored for autonomous threat detection and intelligent DevSecOps workflows. By synthesizing generative foundational models, natural language interfaces, and retrieval-augmented generation (RAG) with cloud-native monitoring stacks, the proposed framework automates real-time anomaly detection, incident response, and continuous compliance. Generative AI models analyze multi-modal telemetry—including system logs, network traces, and infrastructure-as-code (IaC) templates—to synthesize context-aware threat intelligence, predict attack vectors, and autonomously generate self-healing remediation scripts. Furthermore, the platform embeds intelligent security guardrails directly into CI/CD pipelines, transforming DevSecOps into an adaptive, autonomous ecosystem. Empirical evaluations demonstrate that the GenAI-driven approach reduces Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR) by up to 70% while drastically decreasing false-positive alerts compared to traditional signature-based systems. This study establishes a roadmap for building resilient, self-defending cloud infrastructure capable of mitigating emerging cyber threats autonomously.
Generative AI, Cloud-Native Security, DevSecOps, Autonomous Threat Detection, Self-Healing Infrastructure, Microservices Security, Continuous Compliance, Retrieval-Augmented Generation
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
Building Autonomous Enterprise Intelligence through Federated Learning Scalable Cloud Data Engineering Frameworks and Cyber Resilience
Modern multi-tenant enterprises generate immense, heterogeneous datasets across geographically siloed cloud regions, edge infrastructures, and independent business units. Traditional data engineering paradigms force the collection and centralization of this information into a single cloud data warehouse or lakehouse to build predictive analytics models. This centralized architecture is increasingly untenable due to strict cross-border data residency compliance rules, escalating data egress costs, and the physical constraints of data gravity. To resolve these operational challenges, this paper presents a holistic architectural blueprint for Building Autonomous Enterprise Intelligence through Federated Learning and Scalable Cloud Data Engineering Frameworks. The proposed framework orchestrates a decoupled data ecosystem that reverses traditional Extract-Transform-Load (ETL) paradigms. Instead of moving raw data across networks, the system leverages scalable cloud data pipelines to preprocess, validate, and clean transactional data natively within localized cloud data lakes. Autonomous federated learning nodes then train advanced machine learning architectures locally, executing parameter optimizations directly at the data source. A centralized cloud parameter orchestrator manages secure model updates, dynamically aggregating local weights using an optimized, privacy-preserving federated averaging protocol to synthesize a globally intelligent model. Empirical testing within a simulated multi-region enterprise cloud infrastructure proves that this framework guarantees high-fidelity predictive performance, minimizes network egress traffic by up to 88%, and ensures complete compliance with global privacy standards.
Federated Learning, Autonomous Enterprise Intelligence, Cloud Data Engineering, Data Gravity, Privacy-Preserving AI, Distributed Architectures.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
Artificial Intelligence–Based Incident Detection, Response, and Recovery in Cloud-Native Computing Environments: An Intelligent Framework for Autonomous Cyber Resilience
Cloud-native computing has transformed the deployment and operation of digital services through microservices, containers, Kubernetes orchestration, distributed applications, and dynamically scalable infrastructure. Although these technologies improve flexibility, availability, and deployment speed, they also increase operational complexity. Failures can emerge across interconnected services, containers, networks, databases, configurations, and security controls, making incident detection, diagnosis, response, and recovery more difficult. Conventional monitoring approaches often rely on static thresholds, fragmented dashboards, and manually coordinated response processes. These approaches can generate excessive alerts, provide limited contextual understanding of incidents, and delay the restoration of affected services. This study proposes an artificial intelligence-based framework for autonomous cyber resilience in cloud-native computing environments. The framework integrates telemetry collection and analysis, anomaly detection, incident classification, distributed root-cause analysis, risk-aware response orchestration, recovery validation, and continuous learning. It combines operational data from logs, metrics, traces, Kubernetes events, and security alerts to identify abnormal behaviour and determine probable causes across distributed microservice environments. The response layer applies predefined policies and confidence thresholds to recommend or execute remediation actions, including pod restart, workload scaling, traffic rerouting, deployment rollback, service isolation, and security escalation. High-risk actions remain subject to human approval and governance controls. The study adopts a design-science and experimental evaluation methodology. The proposed framework will be assessed in a Kubernetes-based cloud-native testbed using simulated operational and cybersecurity incident scenarios, including resource exhaustion, service failures, network latency, configuration errors, database disruptions, and suspicious access events. Performance will be evaluated using detection accuracy, precision, recall, F1-score, root-cause localisation accuracy, mean time to detect, mean time to respond, mean time to recover, recovery success rate, and service availability. The study contributes an integrated and policy-governed approach to intelligent incident management that aims to improve detection speed, reduce recovery time, strengthen service continuity, and support safer automated remediation in complex cloud-native environments.
Artificial intelligence; AIOps; cloud-native computing; Kubernetes; autonomous cyber resilience; incident response; root-cause analysis.
Hyper Adaptive Cloud Intelligence System for Secure Enterprise Modernization and Real Time Analytics
Hyper Adaptive Cloud Intelligence Systems represent the next evolutionary stage in enterprise cloud computing, integrating artificial intelligence, real-time analytics, and autonomous orchestration to enable secure, scalable, and self-optimizing digital infrastructures. As organizations undergo rapid digital transformation, traditional cloud architectures struggle to meet the demands of dynamic workloads, evolving security threats, and real-time decision-making requirements. The proposed system introduces a hyper-adaptive framework that continuously monitors, learns, and responds to enterprise environments using AI-driven policies, predictive analytics, and automated resource management. This architecture combines distributed cloud computing, edge processing, and intelligent data pipelines to ensure low-latency insights and resilient operations. Security is embedded at every layer through zero-trust models, behavioral anomaly detection, and adaptive encryption techniques. Furthermore, the system enables seamless enterprise modernization by supporting legacy system integration, microservices migration, and hybrid/multi-cloud interoperability. Real-time analytics play a central role by enabling continuous intelligence extraction from streaming data sources, empowering businesses to make proactive decisions. The hyper-adaptive nature of the system ensures continuous optimization of performance, cost, and security without manual intervention. This paper explores the conceptual framework, literature foundation, methodology, advantages, and limitations of such systems, highlighting their potential to redefine enterprise cloud ecosystems in the era of intelligent automation and data-driven decision-making.
Hyper Adaptive Cloud, Real-Time Analytics, Enterprise Modernization, Cloud Intelligence, AI Orchestration, Zero Trust Security, Edge Computing, Hybrid Cloud, Predictive Analytics, Autonomous Systems
Artificial Intelligence-Driven Stormwater Management: A New Paradigm in Environmental Engineering
Environmental engineering is undergoing a paradigm shift of historic proportions. For more than a century, stormwater management has been governed by deterministic design methodologies, empirical hydrological models, and rules-based operational protocols whose fundamental assumptions — statistical stationarity of precipitation, predictable urban growth trajectories, and the sufficiency of periodic human oversight — are being systematically invalidated by climate change, digital urbanization, and the explosive growth of environmental monitoring data. Artificial intelligence is emerging not merely as an incremental improvement to existing stormwater engineering tools but as the foundation of an entirely new engineering paradigm: one characterized by continuous learning, probabilistic performance assessment, autonomous adaptive control, and the capacity to optimize across far greater system complexity than human cognition alone can manage. This paper characterizes and validates this paradigm shift through systematic review of 140 peer-reviewed studies, engineering implementations, and technical assessments published between 2019 and 2025, spanning the full spectrum of AI application in stormwater management from deep learning flood forecasting and generative AI infrastructure design to large language model public communication and federated learning cross-city knowledge transfer. We introduce the AI-Driven Stormwater Paradigm Framework (ADSPF) — a structured characterization of the defining principles, operational architectures, professional practice implications, and governance requirements of AI-era stormwater engineering — and demonstrate its application through analysis of implementations across six contrasting national contexts
Artificial intelligence paradigm, Stormwater management, Deep learning hydrology, Generative AI engineering design,
Artificial Intelligence, Machine Learning, and Data Engineering for Digital Repair Twins in Modern Payment Ecosystems
Modern payment ecosystems have grown into sprawling, latency-sensitive networks of banks, card schemes, digital wallets, payment gateways, and regulatory intermediaries, all of which must remain continuously available while resisting fraud, downtime, and compliance failure. This paper introduces and formalizes the concept of the Digital Repair Twin (DRT), an operational extension of the classical digital twin that is purpose-built not merely to mirror a payment system’s state but to diagnose faults and autonomously propose or execute corrective actions. We examine how artificial intelligence (AI), machine learning (ML), and modern data engineering practices converge to make DRTs feasible at production scale. The paper presents a layered reference architecture spanning data ingestion, feature engineering, model serving, and closed-loop remediation, and it discusses supervised and unsupervised learning techniques for anomaly detection, root-cause analysis, and predictive maintenance of payment infrastructure. A simulated case study involving a card-not-present transaction pipeline illustrates how a DRT can detect a degrading authorization success rate, isolate the responsible microservice, and trigger a self-healing workflow within seconds.
Digital twin; digital repair twin; machine learning; data engineering; payment systems; anomaly detection; self-healing systems; fintech infrastructure; explainable AI; fraud detection.
A Framework for Migrating Real-Time Payment Gateways to Post-Quantum Cryptography
The imminent arrival of cryptographically relevant quantum computers poses an existential threat to the RSA, ECC, and ECDH primitives that underpin virtually every real-time payment gateway in operation today. This paper presents PQPG-MF, a structured six-phase migration framework designed to guide financial institutions and payment service providers through a comprehensive, risk-managed transition to NIST-standardised post-quantum cryptography (PQC) algorithms — specifically ML-KEM, ML-DSA, and SLH-DSA. The framework integrates hybrid classical/post-quantum cryptography during the transitional period, cryptographic agility engineering, hardware security module (HSM) compatibility planning, and regulatory compliance alignment covering PCI DSS v4.0 and ISO 27001:2022. We evaluate the framework against three reference architectures — a high-throughput card network gateway, an open-banking API hub, and a central bank digital currency (CBDC) node — demonstrating feasibility across diverse latency, throughput, and compliance constraints. Performance benchmarks reveal that ML-KEM-768 introduces handshake overhead of 1.4–2.1 ms over baseline TLS 1.3 in production-grade deployments, within the tolerance of real-time payment SLAs. Our threat modelling analysis using the HARVEST NOW, DECRYPT LATER attack vector underscores the urgency of migration even before large-scale quantum computers are available. The PQPG-MF framework provides actionable guidance supported by tooling recommendations, governance considerations, and a phased roadmap adaptable to institutions of varying scale.
Post-quantum cryptography; payment gateway; ML-KEM; Kyber; lattice-based cryptography; TLS 1.3; NIST PQC; cryptographic migration; financial infrastructure security; quantum computing
Next-Generation Cryptographic Migration Framework for Real-Time Payment Processing Systems
Contemporary real-time payment processing systems rely on classical asymmetric cryptographic primitives — RSA, Elliptic Curve Diffie-Hellman (ECDH), and ECDSA — that will become computationally vulnerable with the advent of cryptographically relevant quantum computers (CRQCs). This paper introduces NGCMF (Next-Generation Cryptographic Migration Framework), a comprehensive, seven-layer methodology engineered to guide payment processors, card networks, and financial institutions through a structured, auditable transition to NIST-standardised post-quantum cryptography (PQC). NGCMF spans the full cryptographic lifecycle: from automated cryptographic asset discovery and quantum risk classification, through hybrid classical/PQC architecture design and phased production rollout, to long-term cryptographic agility governance. The framework addresses the HARVEST NOW, DECRYPT LATER (HNDL) threat model — wherein adversaries archive encrypted transaction data today for future quantum decryption — as a primary migration driver, alongside conventional forward-secrecy and integrity requirements. We instantiate NGCMF across four real-world payment system archetypes: an ISO 8583 card network switch, an open-banking REST API hub, a CBDC settlement node, and a cross-border SWIFT MX messaging gateway. Performance evaluations demonstrate that ML-KEM-768 (FIPS 203) introduces TLS 1.3 handshake overhead of 1.4–2.3 ms, within real-time SLA tolerances, while ML-DSA (FIPS 204) requires HSM horizontal scaling for high-frequency signing workloads. Regulatory mapping confirms alignment with PCI DSS v4.0, ISO 27001:2022, EU DORA, and NIST SP 800-57 Part 1 Rev. 5. The NGCMF provides financial institutions with a practitioner-oriented, standards-grounded roadmap for achieving quantum-resilient payment infrastructure within a 12–18 month deployment horizon.
Next-generation cryptographic migration; real-time payment processing; post-quantum cryptography; ML-KEM; ML-DSA; NIST FIPS 203/204/205; TLS 1.3 hybrid KEM; ISO 8583; SWIFT MX; CBDC security; quantum-resilient infrastructure; cryptographic agility
Cognitive Enterprise Systems: Unifying AI Cloud-Native Intelligence Predictive Security and Autonomous Decision-Making
Future-ready enterprise systems are undergoing a profound transformation driven by the convergence of artificial intelligence (AI), cloud-native computing, predictive security, and autonomous decision-making capabilities. Organizations operating in increasingly dynamic and competitive environments require digital infrastructures that are scalable, intelligent, resilient, and capable of responding to real-time business challenges. Traditional enterprise systems, often constrained by rigid architectures and reactive operational models, are insufficient to meet the demands of digital transformation. The integration of AI enables advanced analytics, automation, and intelligent insights, while cloud-native technologies provide agility, elasticity, and rapid deployment capabilities. Predictive security introduces proactive threat detection and risk mitigation through machine learning and behavioral analytics, reducing vulnerabilities before they can be exploited. Autonomous decision-making further enhances organizational efficiency by enabling systems to analyze data, evaluate alternatives, and execute actions with minimal human intervention. Together, these technologies create a synergistic framework that supports continuous innovation, operational excellence, and strategic adaptability. This study explores the evolution of enterprise systems toward future-ready architectures and examines the technological foundations, benefits, challenges, and implementation considerations associated with their adoption. The findings suggest that organizations embracing this integrated approach can achieve sustainable competitive advantages while enhancing security, performance, and business resilience in the digital era.
AI Driven Secure HR Data Exchange and Intelligent Learning Optimization in SAP SuccessFactors Ecosystems
The increasing adoption of cloud-based Human Resource Management systems has transformed organizational workforce operations, data management practices, and employee learning strategies. SAP SuccessFactors has emerged as a leading Human Capital Management platform capable of integrating recruitment, payroll, employee engagement, learning management, and workforce analytics within unified enterprise ecosystems. However, the expansion of digital HR infrastructures has intensified concerns regarding data security, privacy protection, cyber threats, interoperability, and adaptive workforce development. Artificial Intelligence has become a critical technological solution for addressing these challenges through intelligent automation, predictive analytics, secure data exchange mechanisms, and personalized learning optimization. This essay examines the role of AI-driven technologies in enhancing secure HR data exchange and intelligent learning optimization within SAP SuccessFactors ecosystems. The discussion evaluates the contributions of machine learning, natural language processing, predictive analytics, blockchain-supported verification, and adaptive learning systems in improving organizational efficiency, cybersecurity resilience, and employee development. The essay further explores ethical concerns, algorithmic bias, regulatory compliance, and organizational readiness associated with AI implementation in HR environments. Using a qualitative research-oriented analytical approach based on secondary data and conceptual synthesis, the study identifies that organizations integrating AI-powered security and intelligent learning capabilities within SAP SuccessFactors ecosystems achieve stronger compliance management, improved workforce engagement, enhanced productivity, and sustainable digital HR transformation.
Artificial Intelligence, SAP SuccessFactors, HR Data Exchange, Intelligent Learning, Human Resource Management, Cloud Security, Predictive Analytics, Machine Learning, Workforce Analytics, Cybersecurity, Employee Learning Optimization, Digital Transformation, Learning Management Systems, Data Privacy, Enterprise Integration
Ransomware Resilience in Cloud and Enterprise Systems: Detection, Response, and Recovery Frameworks
Ransomware has become one of the most serious threats to cloud and enterprise systems, not only because it can encrypt critical data, but also because it can disrupt operations, expose sensitive information, damage public trust, and weaken business continuity. Modern ransomware attacks are no longer limited to simple malware infections. They often involve data theft, double extortion, compromised backups, lateral movement across enterprise networks, and attacks on cloud-based workloads, storage systems, and identity services. As organizations continue to depend on hybrid infrastructure, remote access, third-party platforms, and cloud-native applications, ransomware resilience has become a major requirement for cybersecurity planning. This article examines ransomware resilience in cloud and enterprise systems by reviewing the evolution of ransomware threats, major attack vectors, detection methods, incident response strategies, and recovery practices. It discusses the importance of early detection through behavioral monitoring, endpoint protection, cloud log analysis, anomaly detection, and machine learning-based techniques. The article also examines response and containment measures, including incident triage, endpoint isolation, access restriction, communication planning, and forensic preservation. In addition, it highlights recovery strategies such as immutable backups, disaster recovery planning, clean restoration, recovery testing, and business continuity management. Based on these discussions, the article proposes an integrated ransomware resilience framework that connects governance, prevention, detection, response, recovery, and continuous improvement. The proposed framework emphasizes that ransomware defense should not be treated only as a technical detection problem, but as a wider resilience issue involving people, processes, technology, and organizational decision-making.
Ransomware resilience, Cloud security, Enterprise cybersecurity, Ransomware detection, Incident response, Cyber resilience, Zero trust, Disaster recovery.
Advanced Machine Learning and Cloud Data Engineering Architectures for Secure IoT and Enterprise Analytics Systems
AI-powered distributed computing and secure cloud transformation frameworks are redefining the architecture of modern intelligent enterprise applications. As organizations increasingly adopt data-intensive and latency-sensitive systems, traditional centralized computing models struggle to meet scalability, resilience, and security requirements. Distributed computing combined with cloud-native paradigms enables dynamic resource allocation, parallel processing, and fault-tolerant execution across geographically dispersed nodes. The integration of Artificial Intelligence (AI) further enhances these systems by enabling predictive scaling, anomaly detection, workload optimization, and intelligent orchestration of computing resources. However, this transformation introduces significant challenges in terms of data security, privacy preservation, multi-tenant isolation, and regulatory compliance. Secure cloud transformation frameworks address these challenges by embedding zero-trust architectures, cryptographic mechanisms, and policy-driven access controls into distributed environments. This paper explores the convergence of AI-driven orchestration and secure distributed cloud systems for intelligent enterprise applications. It highlights architectural models, enabling technologies, and operational frameworks that support scalability, efficiency, and security. The study also examines how enterprises can transition from legacy systems to intelligent cloud ecosystems while maintaining operational continuity and minimizing risk. Ultimately, the research emphasizes that the future of enterprise computing lies in adaptive, self-healing, and intelligence-augmented distributed cloud infrastructures capable of supporting next-generation digital transformation initiatives.
AI-powered distributed computing, secure cloud transformation, intelligent enterprise applications, edge computing, cloud-native architecture, zero trust security, workload optimization, data privacy, microservices, orchestration frameworks
Explainable AI and Cloud-Native Security Solutions for Enterprise Transformation and Threat Mitigation
Explainable Artificial Intelligence (XAI) and cloud-native security solutions are becoming essential technologies in modern enterprise transformation and cybersecurity management. Organizations increasingly depend on cloud-native infrastructures, microservices, containerized applications, and distributed computing environments to support digital operations and enterprise innovation. However, the rapid growth of cloud-based systems has also increased the complexity of cybersecurity threats, including ransomware attacks, insider threats, data breaches, and advanced persistent attacks. Traditional security mechanisms often fail to provide transparency and adaptive threat response capabilities in dynamic enterprise ecosystems. Explainable AI addresses these limitations by offering interpretable and transparent machine learning models that enable organizations to understand, trust, and validate AI-driven security decisions. Cloud-native security solutions integrate automated monitoring, container security, zero-trust architectures, identity management, and real-time threat intelligence to protect enterprise infrastructures from evolving cyber risks. The combination of XAI and cloud-native security frameworks improves operational resilience, enhances compliance management, and supports intelligent decision-making processes. This study explores the significance of Explainable AI and cloud-native cybersecurity technologies in enterprise transformation and threat mitigation. It further examines existing research, implementation methodologies, technological challenges, advantages, and limitations associated with these intelligent security frameworks in modern cloud-based enterprise environments.
Explainable Artificial Intelligence, Cloud-Native Security, Enterprise Transformation, Threat Mitigation, Cybersecurity, Machine Learning, Cloud Computing, Zero Trust Architecture, Intelligent Security Systems, Enterprise Applications, Data Protection, Threat Intelligence
Explainable AI for Threat Modelling and Decision Support in Engineering Assets
The growing sophistication and interdependency of engineering resources predisposes vulnerable cyber-physical infrastructures to emerging cyber threats. Explainable Artificial Intelligence (XAI) can be a valuable solution in the field of threat modelling and the decision-making process because this method will make predictions made on the roots of AI understandable. This paper addresses the concept of implementing XAI methods to engineer asset threat-assessment systems with a focus on transparency, accountability, and trust in AI-assisted decisions. The choice of interpretable models, the post-hoc methods of explaining, and the way AI output can fit in the understanding of the human operators are the main factors to evaluate. The prospects of XAI to enhance situational awareness, risk prioritization, and proactive response plans are shown in case applications in the energy, transportation, and industrial systems. The study verifies the existing shortcomings such as model scalability and the trade-off between interpretability and predictive accuracy and suggests future development directions of auditable and human-centric AI systems in the protection of critical assets.
Explainable AI, Threat Modelling, Decision Support Systems, Engineering Assets, Cyber-Physical Security, Interpretable AI, Risk Management
A Review of AI-Based Data Governance Frameworks for Enterprise Data Systems
Enterprise data governance is changing as Artificial Intelligence (AI) is implemented that enables automated proactive and scalable management of the data assets throughout their lifecycle. The work showcases how AI can improve modern enterprises’ decision-making, security, compliance, and quality. It offers the postulates of the data governance core, e.g. policy management, metadata management, and lineage management, as well as privacy, lifecycle control, and explains how AI technologies, e.g. ML, NLP, anomaly detection, federated learning, differential privacy, and homomorphic encryption are used to improve such functionality. The study also contrasts the traditional forms of governance systems and those of the AI systems and observes that they are more precise, scalable, transparent, and real-time responsive. The architecture of AI-based systems of governance is presented which consists of the ingestion, metadata services, AI analytics, policy enforcement, observability, and access control layers. The paper also addresses the enterprise frameworks, ethical considerations, and risk management strategies. Finally, it identifies crucial problems such as the presence of the data silos, the problems of interoperability, and regulatory constraints and considers the recent developments around the autonomous, privacy-centric, and regulation-based systems of governance.
Artificial Intelligence, Data Governance, Enterprise Data Management, Privacy-Preserving Technologies, AI-Driven Compliance, and Data Quality and Security.
Intelligent Mortgage Rate Forecasting Using AI: Enhancing Accuracy, Governance, and Financial System Resilience
The mortgage rate forecasting is a difficult task because financial markets are subject to complex interaction of macroeconomic indicators and market dynamics. In this paper, an intelligent AI-based prediction system in mortgage rates prediction is proposed based on Multisource Stock Market Trends dataset that involves stock prices, economic variables, and sentiment variables. The processing of the data is done via the treatment of the missing values, encoder of the sentiment, feature engineering (lag, rolling, and interaction features), StandardScaler scaling, and the time order division of the data. In order to achieve better predictive performance, a hybrid ensemble model is resorted to that is trained with XGBoost and LightGBM as well as a meta-learner which is stacking-based. The experimental findings which show that the hybrid model is superior to the base models since it offers a superior accuracy with a higher value of R2 of 0.9799, lower value of RMSE of 0.1920, lower value of MAE of 0.1462 and less value of M APE of 2.69%. Moreover, the study employs explainable AI (SHAP), stress testing, and stability testing with the aim of ensuring transparency and strength. The findings reveal how the proposed framework can be useful in improving the precision of the prediction and facilitating the implementation of the model governance and resilience evaluation AI-based mortgage rate forecasting.
financial system, multisource stock market trends dataset, mortgage forecasting, hybrid model, machine learning, governance framework.
Distributed Edge Intelligence for Patent Analytics: Secure RAG-Driven Knowledge Retrieval in Decentralized Innovation Networks
The rapid expansion of global patent repositories and the increasing complexity of decentralized innovation ecosystems have created significant challenges for traditional patent analytics systems. Centralized architectures often suffer from high latency, limited scalability, and critical privacy risks, particularly when handling sensitive intellectual property data across distributed environments. This study proposes a novel distributed edge intelligence framework that integrates edge computing, federated learning, blockchain technology, and retrieval-augmented generation to enable secure, scalable, and real-time patent knowledge retrieval. The framework leverages edge nodes for localized data processing, reducing communication overhead and latency, while federated learning ensures collaborative model training without exposing proprietary datasets. A blockchain-based trust layer provides secure validation and immutable record-keeping, enhancing transparency and data integrity across decentralized networks. Furthermore, the integration of a retrieval-augmented generation mechanism enables context-aware knowledge extraction, significantly improving the accuracy and relevance of patent insights. The proposed system is evaluated through a distributed experimental setup, demonstrating substantial improvements in latency reduction, retrieval accuracy, throughput performance, and security robustness compared to conventional centralized systems. The findings highlight the potential of combining distributed intelligence and advanced language models to transform patent analytics into a more adaptive, secure, and efficient process. This work contributes a unified architectural paradigm for next-generation innovation intelligence systems.
Distributed Edge Intelligence, Patent Analytics, Federated Learning, Blockchain Security, Retrieval-Augmented Generation, Decentralized Innovation Networks.
Big Data and Cloud Computing Integration: A Review of Scalable Information Retrieval Techniques
This presents an extensive overview of the integration of big data with cloud computing, focusing on scalable information retrieval methodologies capable of managing the volume, velocity, and diversity of data, as the imperative to amalgamate big data technologies with cloud computing emerges from the rapid and complex expansion of data originating from diverse digital sources. The paper also explores fundamental architectural models such as distributed storage systems, parallel processing frameworks, and cloud-based service models for data analytics on a large scale how retrieval performance, fault tolerance, and resource utilization could be enhanced through the use of the technologies like Hadoop, MapReduce, Spark, and NoSQL databases. Additional topics covered in the article include security, scalability, data heterogeneity, latency, and cloud-based big data conditions. The comparative insights into different existing retrieval techniques have been offered to point out the advantages, limitations, and application domains. Cloud computing’s function in big data information retrieval and unanswered questions about how to build more effective, safe, and scalable retrieval systems are the results of their most recent study.
Big Data, Cloud Computing, Scalable Information Retrieval Technique, Distributed File Systems, NoSQL Databases, Big data in cloud integration
Data-Driven Artificial intelligence (AI)-based System for Identifying Fraudulent Telephony Activities
The telecommunications industry is an essential part of the modern world of communication tools as it links billions of users across the globe. Nevertheless, telephony fraud has become a widespread issue, and it includes an assortment of fraud types, including subscription fraud, SIM cloning, toll fraud, and voice phishing (vishing), which cost people significant amounts of money and compromised the credibility of the service. The traditional number-based fraud detection techniques feel restricted by the dynamic strategies used by the fraudsters such as spoofing of caller IDs and spoofing of numbers, thereby causing high rate of false positive and late response. This paper suggests a machine-driven Artificial Intelligence (AI) model to detect suspicious telephony transactions with a big amount of textual information. The Baidu search engine was used to gather textual information about actual telecom fraud cases, which was preprocessed by removing noise, segmenting, and tagging part-of-speech and converted into TF-IDF feature vectors. This data was trained in an Artificial Neural Network (ANN) to learn complicated patterns of fraud. Measures of evaluation such accuracy, precision, recall, and F1-score indicate a strong performance of the system with an accuracy of 98.53. The findings indicate that AI-based models have the potential to identify the constantly changing telephony fraud, which is scalable and can deliver timely information to defend users and service providers. It is a new and innovative adaptive scheme of telecom fraud detection, as opposed to the conventional schemes.
Telecommunication fraud, Artificial Intelligence (AI), Machine Learning, Fraud Detection, Textual Data, Real-Time Detection, Telephony activity.
Customer Sentiment Analysis for Online Shopping Platforms Using NLP and Deep Learning Methods
Customer sentiment classification has emerged as an essential part of digital commerce intelligence as online malls to shop in grow exponentially in product and user base. Social media like Flipkart have hundreds of product categories and millions of reviews. But the user feedback is very unstructured and may include HTML objects, URLs, punctuations and colloquialisms, which restrict the capability of contextual learning. In order to solve these difficulties, the structured Natural Language Processing pipeline is used, which involves removing null-texts, removing HTML tags and URLs, filtering by numbers and punctuation, removing stop-words, lexical-similarity stemming, tokenization, sentiment labeling by ratings, label encoding, and TF-IDF weighted features extraction. To be tested, the processed data is separated. It is advised to use a recurrent neural network (RNN) for categorization in order to preserve sequential sentiment and word order. The evaluation’s findings show that sentiment is very dependable, with 95.08% accuracy; SVM achieved 81.77% and 90.02% with a feedforward neural network. The rapid loss reduction, constant epoch convergence, and the limited validation deviation of 25 training cycles suggest that it can be easily used to ensure robust generalization when faced with class imbalance to enable scalable sentiment intelligence to be used in automated review interpretation, analytics, and adaptive decision support in e-commerce websites.
Sentiment Analysis, Flipkart Reviews, Natural Language Processing, Deep Learning Classification, E-Commerce Analytics.
Federated Learning for Privacy-Preserving AI
As Artificial Intelligence (AI) systems increasingly rely on large-scale data for model training, concerns over data privacy, security, and regulatory compliance have become paramount. Traditional centralized learning approaches require aggregating sensitive data from multiple sources, increasing the risk of data breaches and violating privacy regulations. Federated Learning (FL) has emerged as a promising paradigm to address these challenges by enabling decentralized model training. In FL, multiple devices or organizations collaboratively train a shared model without transferring raw data to a central server, thereby preserving privacy while still benefiting from collective knowledge. This paper explores the principles, architecture, and applications of Federated Learning in privacy-sensitive domains such as healthcare, finance, and edge computing. It examines key techniques, including secure aggregation, differential privacy, and communication-efficient algorithms, that enhance privacy and security in federated settings. Challenges such as model heterogeneity, communication overhead, and fairness across participating clients are also discussed. Furthermore, the integration of Federated Learning with privacy-preserving AI frameworks highlights the balance between performance, security, and regulatory compliance. The study concludes that Federated Learning is a pivotal approach for developing AI systems that respect data privacy, comply with regulations, and maintain high predictive performance. As privacy concerns intensify and regulations evolve, FL offers a scalable and responsible pathway for collaborative AI development across distributed environments.
Federated Learning, Privacy-Preserving AI, Decentralized Machine Learning, Secure Aggregation, Differential Privacy, Edge AI, Data Security, Model Collaboration, Regulatory Compliance, Distributed AI, Ethical AI, Privacy-Preserving Machine Learning.
Artificial Intelligence (AI) systems increasingly influence decisions in critical domains such as healthcare, finance, education, employment, and criminal justice. While these systems offer efficiency and predictive power, they also risk perpetuating and amplifying existing social inequalities embedded in historical data. Bias in AI can arise from multiple sources, including skewed datasets, flawed model assumptions, algorithmic design choices, and societal structures reflected in training data. Such biases may lead to discriminatory outcomes, unequal access to opportunities, and violations of fundamental rights. This paper examines the concepts of bias, fairness, and ethical AI, exploring how algorithmic systems can unintentionally disadvantage individuals or protected groups. It reviews different types of bias—data bias, sampling bias, measurement bias, and algorithmic bias—and analyzes formal fairness definitions such as demographic parity, equal opportunity, and predictive equality. The discussion also highlights technical and organizational strategies for mitigating bias, including data preprocessing, fairness-aware model training, post-processing corrections, and transparent evaluation frameworks. Beyond technical solutions, the paper emphasizes the ethical dimensions of AI deployment, including accountability, transparency, explainability, privacy, and human oversight. Regulatory frameworks such as the General Data Protection Regulation (GDPR) underscore the importance of fairness and transparency in automated decision-making systems. The study argues that achieving ethical AI requires a multidisciplinary approach combining technical rigor, legal safeguards, governance structures, and inclusive stakeholder engagement. The paper concludes that bias mitigation and fairness are not optional enhancements but foundational requirements for trustworthy and socially responsible AI systems. Proactive ethical design, continuous monitoring, and robust accountability mechanisms are essential to ensure that AI technologies promote equity rather than reinforce inequality.
Bias in AI, Algorithmic Fairness, Ethical AI, Responsible AI, Discrimination, Fairness Metrics, Accountability, Transparency, Explainable AI (XAI), Human Rights, Data Bias, AI Governance, Trustworthy AI, Regulatory Compliance.
From Automation to Intelligence: The Evolution of Secure Networks
The evolution of network infrastructure has progressed from manual configuration and basic automation toward intelligent, adaptive, and security-centric systems. As digital environments expand across cloud, edge, and hybrid architectures, the traditional separation between networking and security has become increasingly ineffective. Secure networks must now operate at machine speed, continuously adapting to dynamic workloads, users, and threat landscapes. This shift marks the transition from automation-driven operations to intelligence-driven network security. This article examines how secure networks have evolved through successive stages—from rule-based automation and static security controls to AI-enabled, context-aware, and self-optimizing systems. It explores the role of machine learning and advanced analytics in enabling real-time threat detection, behavioral analysis, predictive risk assessment, and automated response. By leveraging continuous telemetry from network traffic, endpoints, and applications, intelligent networks can dynamically enforce security policies, support zero trust architectures, and reduce attack surfaces across distributed environments. The paper analyzes key architectural and technological enablers of this evolution, including software-defined networking (SDN), network function virtualization (NFV), cloud-native security platforms, security orchestration, automation, and response (SOAR), and intent-based networking. It also addresses operational and governance challenges such as data quality, model explainability, adversarial attacks, compliance requirements, and the need for human-in-the-loop oversight to ensure trust and accountability. Through real-world use cases and emerging best practices, this article demonstrates how the transition from automation to intelligence enhances network resilience, reduces operational complexity, and strengthens security posture. It concludes that intelligent, secure networks are foundational to next-generation digital infrastructure, enabling organizations to proactively defend against evolving cyber threats while supporting scalability, agility, and continuous innovation.
Secure Networks Network Automation Intelligent Networking
Building Intelligent Networks with AI and Advanced Technologies
The rapid growth of cloud computing, edge environments, Internet of Things (IoT), and data-intensive applications has significantly increased the complexity of modern networks. Traditional network design and management approaches—largely manual and rule-based—are no longer sufficient to meet demands for scalability, performance, resilience, and security. Building intelligent networks powered by artificial intelligence (AI) and advanced technologies has become essential for supporting next-generation digital infrastructure. This article explores how AI, machine learning, and complementary technologies are enabling the development of intelligent networks capable of learning, adapting, and autonomously optimizing operations. By analyzing large volumes of real-time and historical network telemetry, AI-driven systems can predict congestion and failures, optimize traffic flows, automate configuration management, and dynamically enforce security policies. These capabilities transform networks from static connectivity layers into active, context-aware platforms that support business agility and user experience. The paper examines key enabling technologies such as software-defined networking (SDN), network function virtualization (NFV), intent-based networking, cloud-native architectures, and edge computing. It also discusses the role of automation, orchestration, and observability platforms in providing the data pipelines and feedback loops required for effective AI-driven decision-making. Architectural considerations—including model deployment strategies, scalability, interoperability, and resilience—are analyzed in detail. In addition, the article addresses challenges associated with building intelligent networks, including data quality, model explainability, operational trust, security risks, and the skills required to manage AI-enabled infrastructure. Through real-world use cases and emerging best practices, this paper demonstrates how intelligent networks enhance efficiency, reliability, and security while laying the foundation for self-healing, autonomous systems that can evolve alongside rapidly changing technological and business requirements.
Intelligent Networks Artificial Intelligence (AI) Machine Learning Advanced Network Technologies Software-Defined Networking (SDN) Network Function Virtualization (NFV) Intent-Based Networking Network Automation Network Observability Cloud and Edge Computing Autonomous Networks Self-Healing Systems Digital Infrastructure
The Role of Machine Learning in Next-Generation Cyber Defense
The escalating frequency, scale, and sophistication of cyber attacks have outpaced the capabilities of traditional, signature-based and rule-driven security defenses. As organizations adopt cloud computing, edge architectures, and highly distributed digital ecosystems, next-generation cyber defense demands adaptive, intelligent, and automated security mechanisms. Machine learning (ML) has emerged as a foundational technology enabling this shift, redefining how threats are detected, analyzed, and mitigated across modern environments. This article examines the role of machine learning in next-generation cyber defense, focusing on its ability to identify anomalous behavior, uncover previously unknown attack patterns, and enable predictive threat intelligence. By leveraging supervised, unsupervised, and reinforcement learning techniques, ML-powered security systems analyze vast and heterogeneous data sources—including network traffic, endpoint telemetry, user behavior, and application logs—to detect zero-day attacks and advanced persistent threats with greater accuracy and speed. The paper explores architectural and operational models for integrating ML into cyber defense frameworks, including cloud-native security platforms, security information and event management (SIEM), extended detection and response (XDR), and security orchestration, automation, and response (SOAR). It also addresses critical challenges such as data quality, model drift, explainability, adversarial machine learning, and the need for human-in-the-loop governance to maintain trust and accountability. Through real-world use cases and emerging best practices, the article demonstrates how machine learning enhances situational awareness, reduces mean time to detection and response, and strengthens overall cyber resilience. It concludes that while ML is not a replacement for traditional security controls or human expertise, it is a vital enabler of next-generation cyber defense strategies capable of evolving alongside an increasingly complex threat landscape.
Machine Learning Cyber Defense Next-Generation Security Threat Detection Anomaly Detection Predictive Threat Intelligence SIEM XDR SOAR
Artificial Intelligence in Network Security: Opportunities and Threats
The increasing scale, complexity, and dynamism of modern networks have made traditional security mechanisms insufficient to defend against sophisticated and rapidly evolving cyber threats. Artificial intelligence (AI) has emerged as a powerful enabler in network security, offering advanced capabilities for threat detection, behavioral analysis, and automated response. At the same time, the adoption of AI introduces new risks, including adversarial attacks, model exploitation, and operational trust challenges. This duality presents both significant opportunities and critical threats for network security practitioners. This article explores how AI and machine learning techniques are transforming network security by enabling continuous monitoring, real-time anomaly detection, and predictive threat intelligence. By analyzing vast volumes of network telemetry, logs, and behavioral data, AI-driven systems can identify complex attack patterns, detect zero-day threats, and reduce response times through security orchestration, automation, and response (SOAR). These capabilities support modern security paradigms such as zero trust architectures and continuous risk-based access control. In parallel, the paper examines the emerging threat landscape associated with AI in network security. Adversarial machine learning techniques, data poisoning, model evasion, and algorithmic bias pose significant risks to the reliability and effectiveness of AI-based defenses. Additionally, challenges related to explainability, regulatory compliance, and human oversight complicate operational deployment in enterprise and critical infrastructure environments. Through analysis of real-world use cases and architectural patterns, this article highlights best practices for harnessing AI’s defensive potential while mitigating its inherent risks. It argues that successful adoption requires a balanced approach combining robust data governance, transparent models, continuous validation, and human-in-the-loop decision-making. Ultimately, AI will play a pivotal role in shaping the future of network security—amplifying both defensive capabilities and the sophistication of cyber threats.
Artificial Intelligence Network Security Machine Learning Cyber Threat Detection Anomaly Detection Security Automation SOAR Zero Trust Architecture Adversarial Machine Learning Data Poisoning Model Explainability Threat Intelligence
Smart Networks: How Machine Learning Is Redefining Digital Infrastructure
The rapid expansion of cloud computing, edge environments, and highly distributed applications has fundamentally increased the complexity of modern digital infrastructure. Traditional, rule-based network management approaches struggle to maintain performance, reliability, and security at scale. Smart networks—powered by machine learning (ML)—are emerging as a transformative solution, enabling networks to become adaptive, predictive, and increasingly autonomous. This article examines how machine learning is redefining digital infrastructure by embedding intelligence directly into network operations and architecture. ML models analyze real-time and historical telemetry data to optimize traffic routing, predict congestion and failures, automate capacity planning, and dynamically adjust network configurations. Beyond performance optimization, smart networks leverage anomaly detection and behavioral analytics to enhance security posture, identify zero-day threats, and support continuous risk assessment across hybrid and multi-cloud environments. The paper explores key enabling technologies such as software-defined networking (SDN), network function virtualization (NFV), intent-based networking, and cloud-native observability platforms that provide the data foundation for machine learning-driven decision-making. It also discusses architectural considerations for deploying ML at scale, including data pipelines, model training and inference placement, edge versus cloud trade-offs, and integration with automation and orchestration frameworks. Additionally, the article addresses critical challenges associated with smart networks, including data quality and bias, model explainability, operational trust, and the risks posed by adversarial machine learning. Through real-world use cases and industry examples, the paper demonstrates how machine learning-driven networks improve operational efficiency, resilience, and user experience while laying the groundwork for self-healing, autonomous digital infrastructure capable of evolving alongside business and technology demands.
Smart Networks Machine Learning Digital Infrastructure Intelligent Networking Network Automation Predictive Analytics Anomaly Detection Software-Defined Networking (SDN) Network Function Virtualization (NFV) Intent-Based Networking
The Convergence of Networking, Cybersecurity, and Artificial Intelligence
The rapid evolution of digital infrastructure has led to a profound convergence of networking, cybersecurity, and artificial intelligence (AI). As modern networks become increasingly distributed, cloud-native, and software-defined, traditional security models and manual network management approaches are no longer sufficient to address the scale, complexity, and sophistication of emerging cyber threats. This convergence represents a fundamental shift from reactive, rule-based systems toward intelligent, adaptive, and autonomous networked environments. This article explores how AI-driven techniques are transforming both networking and cybersecurity by enabling real-time threat detection, predictive analytics, automated incident response, and self-optimizing network operations. Machine learning models embedded within network architectures analyze massive volumes of telemetry data to identify anomalies, anticipate failures, and dynamically enforce security policies. At the same time, cybersecurity frameworks are evolving to integrate directly with network fabric, enabling zero-trust architectures, continuous authentication, and context-aware access control. The paper further examines key architectural patterns supporting this convergence, including software-defined networking (SDN), network function virtualization (NFV), cloud-native security services, and AI-powered security orchestration, automation, and response (SOAR). It also discusses challenges such as data quality, model explainability, adversarial AI, regulatory compliance, and the skills gap required to operate intelligent network-security ecosystems. By analyzing real-world use cases across enterprise IT, cloud environments, and critical infrastructure, this article highlights how the fusion of networking, cybersecurity, and AI is redefining digital resilience. The convergence not only enhances threat prevention and operational efficiency but also establishes the foundation for autonomous, self-healing networks capable of defending against evolving cyber risks in real time.
Networking Cybersecurity Artificial Intelligence (AI) Machine Learning Intelligent Networks Zero Trust Architecture Software-Defined Networking (SDN) Network Function Virtualization (NFV) Threat Detection Security Automation SOAR Cloud-Native Security Predictive Analytics Autonomous Networks
Digital Transformation Strategies in Indian Organizations: A Techno- Managerial Perspective
Digital transformation (DT) has become a key priority for organizations worldwide. In India, changing market conditions, new regulations, higher customer expectations, and new technologies have sped up DT adoption. This paper examines how Indian organizations approach digital transformation from both technological (such as technology stack, cloud, AI/ML, IoT) and managerial (leadership, culture, processes, strategy) perspectives, forming a “techno-managerial” view. Using secondary literature, industry reports, and case studies, the paper highlights common strategies, enablers, barriers, and best practices. The findings show that many large and mid-sized Indian firms have started their digital transformation, often with cloud migration, process automation, and data-driven decision-making. However, they still face major challenges, mainly skills gaps, old infrastructure, cybersecurity issues, and resistance to change. The paper offers a framework for building effective DT strategies in Indian organizations and gives practical advice for managers and policymakers.
AI/ML, Organizational Culture, Strategy, MSMEs,
Application of Machine Learning in Demand Forecasting and Inventory Optimization
Accurate demand forecasting and efficient inventory management are crucial for organizations to stay competitive in today’s fast-changing, data-driven business world. Traditional forecasting methods often miss complex demand patterns caused by seasonality, consumer behavior, promotions, and outside factors. Machine learning (ML) has changed how companies approach demand forecasting and inventory optimization by making decisions more data-driven, flexible, and predictive. This paper explores how machine learning is used in demand forecasting and inventory optimization, looking at both management and technology aspects. It reviews key ML algorithms, how they fit into supply chain systems, their benefits, challenges, and ethical issues. The paper also presents a framework showing how ML can improve forecast accuracy, lower inventory costs, and boost service levels. The findings show that ML can make supply chains more responsive and efficient, but success depends on good data, organizational readiness, and strong collaboration between people and machines. The paper ends with practical advice for managers and ideas for future research.