Cybersecurity and Network Defense Research
type: channel
description: Welcome to the Frontline of Protection: 'Cybersecurity and Network Defense Research'. Step into the world where digital guardians stand sentinel, safeguarding against digital threats and vulnerabilities. Explore cutting-edge research, proactive defense strategies, and transformative insights driving the evolution of cybersecurity and network defense. Join us in the relentless pursuit of digital resilience, where innovation, vigilance, and collaboration defend the integrity of our digital world. Journal Snapshot Journal Name: Cybersecurity and Network Defense Research (CNDR) Editor-in-Chief: : Dr. Tabassum Ara (see Editorial Board) Publisher Imprint: The Science Brigade Publishers Publisher: Libertatem Media Private Limited h-index: 15 (By Google Scholar) i10-index: 22 (By Google Scholar) Journal Initials: CNDR Research Scope: Cybersecurity, Network Security, Information Security, Digital Forensics, Ethical Hacking and Penetration Testing, Cloud and IoT Security, Cryptography and Secure Communications, Cyber Threat Intelligence and Incident Response, Artificial Intelligence for Cyber Defense, Privacy and Data Protection Publication Mode: Digital (On this Website) Frequency: Semi-Annual (2 Issues a year) Launch Year: 2021 Review Mode: Double Blind Peer Review Plagiarism Allowed: 10% (as per Turnitin) Coverage: India Language: English Editorial Contact - editor@thesciencebrigade.com
publisher: The Science Brigade Publishers
language: en
publication name: Cybersecurity and Network Defense Research
issn: 0000-0000
copyright: License Terms Ownership and Licensing: Authors of this research paper submitted to the journal owned and operated by The Science Brigade Group retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License. License Permissions: Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal. This license allows for the broad dissemination and utilization of research papers. Additional Distribution Arrangements: Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this Journal. Online Posting: Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal. Online sharing enhances the visibility and accessibility of the research papers. Responsibility and Liability: Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
items: Translational Data Fusion Architectures for Efficacy Signal Propagation: AI-Enhanced Systems for Clinical-Preclinical Data Integration in Drug Development
items: Total Cost of Ownership Modelling and Procurement Intelligence: AI-Driven Approaches to Retail Supply Chain Cost Optimisation
items: Structure-Based Molecular Optimisation Through Deep Generative Networks: AI-Enhanced Computational Strategies for Rational Drug Design
items: Tumour Heterogeneity Profiling and Molecular Subtype Classification: AI-Enhanced Systems for Precision Oncology Drug Development Personalisation
items: Multi-Omics Data Fusion Through Attention-Based Neural Networks: AI-Enhanced Systems for Integrated Genomic, Proteomic, and Metabolomic Analysis in Drug Discovery
items: Multi-Target Neuroprotection Lead Identification Through Deep Learning: AI-Driven Platforms for Enhanced Drug Discovery in Neurodegenerative Disease
items: Network Topology Analysis and Claim Linkage Intelligence: Machine Learning Approaches to Enhanced Fraud Detection in Insurance Operations
items: Pharmacokinetic-Pharmacodynamic Modelling and Adaptive Dosing Intelligence: AI-Driven Platforms for Patient-Specific Drug Dosing Optimisation
items: Policyholder Behavioural Profiling and Dynamic Product Customisation: Machine Learning Frameworks for Personalised Insurance Offerings
items: Predictive Physicochemical Property Modelling and Process Parameter Optimisation: AI-Driven Platforms for Pharmaceutical Formulation Development
items: Probabilistic Demand Signal Decomposition in Multi-Echelon Networks: Machine Learning Architectures for Supply Chain Forecasting Accuracy
items: Proteome-Wide Binding Site Identification Through Graph Convolutional Networks: AI-Enhanced Computational Methods for Drug Target Prediction
items: RFID-Enabled Inventory State Estimation and Automated Replenishment: A Real-Time Machine Learning Framework for Retail Inventory Tracking and Management
items: High-Frequency Point-of-Sale Signal Processing and Inventory Trigger Intelligence: AI-Powered Demand Sensing Architectures for Retail Supply Chain Responsiveness
items: Graph-Based Anomaly Propagation Analysis in Point-of-Sale Networks: AI-Enhanced Fraud Detection in Retail Transaction Ecosystems
items: Multi-Modal Clinical Data Harmonisation and Feature Extraction: AI-Enhanced Systems for Automated Biomedical Data Analysis and Interpretation
items: Dynamic Risk Classification and Premium Calibration Through Streaming Analytics: A Real-Time Machine Learning Framework for Insurance Underwriting Optimisation
items: End-to-End Tracking Intelligence and Predictive Exception Management: AI-Enhanced Frameworks for Real-Time Supply Chain Visibility
items: Ensemble Demand Forecasting with Exogenous Signal Integration: AI-Driven Approaches to Enhanced Retail Supply Chain Planning Accuracy
items: Atomic-Level Binding Affinity Estimation via Physics-Informed Neural Networks: AI-Enhanced Computational Methods for Protein-Ligand Interaction Prediction
items: Automated Claims Adjudication and Resolution Pathway Intelligence: Machine Learning Frameworks for Insurance Settlement Process Optimisation
items: Bioprocess Parameter Optimisation and Cell Culture Intelligence: Machine Learning Approaches to Enhanced Biopharmaceutical Development and Manufacturing
items: Carbon Footprint Modelling and Circular Economy Optimisation: AI-Driven Frameworks for Environmental Sustainability in Supply Chain Management
items: Zero-Trust Architecture for Securing Multi-Cloud Environments
items: Data Encryption and IAM Policies: Best Practices for AWS Ecosystems
items: Securing Continuous Integration/Deployment Pipelines with Single Sign-On (SSO) in Cloud DevOps
items: Advancements in Passwordless Authentication: The Future of SSO in Cloud IAM
items: Enhancing Cloud DevOps Pipelines with SSO for CI/CD Security
items: Identity Federation for Cross-Cloud Workflows: Challenges and Best Practices
items: Security-First Frameworks for Multi-Tenant PaaS Platforms: Challenges and Solutions

Translational Data Fusion Architectures for Efficacy Signal Propagation: AI-Enhanced Systems for Clinical-Preclinical Data Integration in Drug Development
type: item
description: Integrating clinical and preclinical information with emerging evidence during drug development is crucial for increasing the probability of making the correct decisions. A number of different types of information can be combined. This can be done at the level of connecting clinical trial results to real-world evidence, walking through the enriched virtual trial concept.
subject: Articles
  topic: section
subject: translational data fusion architectures
  topic: keywords
subject: efficacy signal propagation
  topic: keywords
subject: ai-enhanced systems
  topic: keywords
subject: clinical-preclinical data integration
  topic: keywords
subject: machine learning
  topic: keywords
creator: Li Guo
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 30
ending page: 37

Total Cost of Ownership Modelling and Procurement Intelligence: AI-Driven Approaches to Retail Supply Chain Cost Optimisation
type: item
description: Retail supply chain processes require substantial investment to maintain their competitive edge, including expenditure on transportation, warehousing, information technology, and other functions. To stay ahead of the competition in resource allocation, more and more researchers take advantage of artificial intelligence methods to optimize retail supply chain operations from multiple perspectives, such as satisfying customer demands, improving operational efficiency, and reducing operational cost
subject: Articles
  topic: section
subject: total cost
  topic: keywords
subject: ownership modelling
  topic: keywords
subject: procurement intelligence
  topic: keywords
subject: approaches to retail supply chain cost optimisation
  topic: keywords
subject: machine learning
  topic: keywords
creator: Jean-Philippe Lambert
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 22
ending page: 29

Structure-Based Molecular Optimisation Through Deep Generative Networks: AI-Enhanced Computational Strategies for Rational Drug Design
type: item
description: Chemical biology can't escape from the marriage between computational science and high-throughput experimentation, either intentionally or unintentionally. The large amounts, variability, higher dimensions, and flexibility of data are supported and applied through model-driven AI techniques, producing clinical interest modules represented as models implicated in pathological situations or patient responses, such as bioactivity, imaging, and mathematical models, blending strong learning from targ
subject: Articles
  topic: section
subject: structure-based molecular optimisation
  topic: keywords
subject: deep generative networks
  topic: keywords
subject: ai-enhanced computational strategies
  topic: keywords
subject: rational drug design
  topic: keywords
subject: machine learning
  topic: keywords
creator: Ying Liu
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 12
ending page: 21

Tumour Heterogeneity Profiling and Molecular Subtype Classification: AI-Enhanced Systems for Precision Oncology Drug Development Personalisation
type: item
description: Cancer drug development and treatment strategies have undergone a revolution in the past two decades. New technologies have propelled oncology research, providing doctors and scientists with tools to delve deeper into individual patient tumors and create revolutionary treatment strategies. To date, tumor mutations and patient-specific characteristics have been largely ignored in the design of cancer treatment strategies.
subject: Articles
  topic: section
subject: tumour heterogeneity profiling
  topic: keywords
subject: molecular subtype classification
  topic: keywords
subject: ai-enhanced systems
  topic: keywords
subject: precision oncology drug development personalisation
  topic: keywords
subject: machine learning
  topic: keywords
creator: Yan Zhang
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 38
ending page: 49

Multi-Omics Data Fusion Through Attention-Based Neural Networks: AI-Enhanced Systems for Integrated Genomic, Proteomic, and Metabolomic Analysis in Drug Discovery
type: item
description: The integration of genomic, proteomic, and metabolomic data in a systems biology approach is expected to be transformative for drug discovery and development, providing an opportunity to expand the discovery of novel molecules for new targets and, importantly, to determine whether these novel treatments will be effective and safe. This potential is still not fully realized, making it difficult for many researchers to obtain biologically meaningful results from their data.
subject: Articles
  topic: section
subject: multi-omics data fusion
  topic: keywords
subject: attention-based neural networks
  topic: keywords
subject: ai-enhanced systems
  topic: keywords
subject: integrated genomic, proteomic
  topic: keywords
subject: machine learning
  topic: keywords
creator: Paulo Leitão
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 20
ending page: 31

Multi-Target Neuroprotection Lead Identification Through Deep Learning: AI-Driven Platforms for Enhanced Drug Discovery in Neurodegenerative Disease
type: item
description: exhaustive knowledge of disease at the biochemical, genetic, and physiological levels, combined with clinical trials directly on patients, is not only important but absolutely essential for achieving efficacious therapies that truly improve patient outcomes. To date, the need for this comprehensive understanding cannot be overstated as it serves as the foundation for innovation in medical treatment.
subject: Articles
  topic: section
subject: multi-target neuroprotection lead identification
  topic: keywords
subject: deep learning
  topic: keywords
subject: platforms
  topic: keywords
subject: enhanced drug discovery
  topic: keywords
subject: machine learning
  topic: keywords
creator: Eric Verschueren
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 32
ending page: 45

Network Topology Analysis and Claim Linkage Intelligence: Machine Learning Approaches to Enhanced Fraud Detection in Insurance Operations
type: item
description: Insurance fraud - a global issue Insurance fraud, both hard and soft, is rapidly increasing worldwide. The intent of an insurance fraudster can be to either falsely obtain payment or avoid incurring a cost, whether that is through accident staging in the hope of a public liability settlement or filing life insurance claims when the individual is still very much alive. Such crimes go beyond the financial implications and affect general public safety, hike premiums, and eat into profits.
subject: Articles
  topic: section
subject: network topology analysis
  topic: keywords
subject: claim linkage intelligence
  topic: keywords
subject: machine learning approaches to enhanced fraud detection
  topic: keywords
subject: insurance operations
  topic: keywords
creator: Ebru Topal
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 46
ending page: 57

Pharmacokinetic-Pharmacodynamic Modelling and Adaptive Dosing Intelligence: AI-Driven Platforms for Patient-Specific Drug Dosing Optimisation
type: item
description: Modern healthcare is moving towards individualized or personalized medical interventions in many areas, and personalized drug dosing is no exception. Personalized drug dosing is the concept of administering a dosage that is specifically tailored to the pharmacokinetics of an individual and further adjusted to reach a personalized target. Personalized dosages can optimize the benefit of the treatment while minimizing adverse drug reactions.
subject: Articles
  topic: section
subject: pharmacokinetic-pharmacodynamic modelling
  topic: keywords
subject: adaptive dosing intelligence
  topic: keywords
subject: platforms
  topic: keywords
subject: patient-specific drug dosing optimisation
  topic: keywords
subject: machine learning
  topic: keywords
creator: Esteban Botaro
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 1
ending page: 10

Policyholder Behavioural Profiling and Dynamic Product Customisation: Machine Learning Frameworks for Personalised Insurance Offerings
type: item
description: In today's world, it is unimaginable to think of personalization only in terms of music streaming platforms that automatically generate playlists. In contrast, customer requirements have drastically changed over the last few years. Consumers are increasingly seeking tailored solutions, whether in the e-commerce, lifestyle, or subscription industries. This also applies to insurance products. Artificial intelligence is fundamentally changing and shaping conventional insurance products.
subject: Articles
  topic: section
subject: policyholder behavioural profiling
  topic: keywords
subject: dynamic product customisation
  topic: keywords
subject: machine learning frameworks
  topic: keywords
subject: personalised insurance offerings
  topic: keywords
creator: Ricardo Garzón
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 11
ending page: 21

Predictive Physicochemical Property Modelling and Process Parameter Optimisation: AI-Driven Platforms for Pharmaceutical Formulation Development
type: item
description: Artificial intelligence (AI) is an advanced computerized tool that makes it possible to understand and process information. It is widely used in pharmaceutical sciences, especially in reviewing and analyzing extensive databases in a reasonable time. Although there is a reliable system for monitoring information via these stored databases, the AI-based methodologies for advanced drug formulation applications are limited.
subject: Articles
  topic: section
subject: predictive physicochemical property modelling
  topic: keywords
subject: process parameter optimisation
  topic: keywords
subject: platforms
  topic: keywords
subject: pharmaceutical formulation development
  topic: keywords
subject: machine learning
  topic: keywords
creator: Soo-Yeon Oh
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 22
ending page: 29

Probabilistic Demand Signal Decomposition in Multi-Echelon Networks: Machine Learning Architectures for Supply Chain Forecasting Accuracy
type: item
description: Forecasting is challenging in the face of high demand uncertainty; precisely integrating these market demands into decisions on sourcing, manufacturing, and logistics directly impacts the operational efficiencies and effectiveness of many industrial concerns. With these interrelated uncertainties, many studies on supply chain planning and operations rely on the use of forecasting techniques.
subject: Articles
  topic: section
subject: probabilistic demand signal decomposition
  topic: keywords
subject: multi-echelon networks
  topic: keywords
subject: machine learning architectures
  topic: keywords
subject: supply chain forecasting accuracy
  topic: keywords
creator: Alexandre Vieira
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 30
ending page: 41

Proteome-Wide Binding Site Identification Through Graph Convolutional Networks: AI-Enhanced Computational Methods for Drug Target Prediction
type: item
description: Accurate drug target prediction is the uppermost priority for enlightening novel therapeutic opportunities in the pharmaceutical sector. The simplicity and cost-effectiveness of the computational methods initiated the transformation from conventional to in-silico drug identification approaches in the last few decades. Recent advancements in high-throughput biological properties of chemical attributes have led to a massive amount of data.
subject: Articles
  topic: section
subject: proteome-wide binding site identification
  topic: keywords
subject: graph convolutional networks
  topic: keywords
subject: ai-enhanced computational methods
  topic: keywords
subject: drug target prediction
  topic: keywords
subject: machine learning
  topic: keywords
creator: José Barreto
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 42
ending page: 49

RFID-Enabled Inventory State Estimation and Automated Replenishment: A Real-Time Machine Learning Framework for Retail Inventory Tracking and Management
type: item
description: An agile, lean, and responsive inventory is an inevitable aspect of enabling efficient and effective consumer service and satisfaction under the contemporary turbulent, competitive, and uncertain business environment. Due to its numerous dependencies on the internal as well as external complex business environment of an organization, inventory management is always a matter of focal concern for practitioners and researchers.
subject: Articles
  topic: section
subject: rfid-enabled inventory state estimation
  topic: keywords
subject: automated replenishment
  topic: keywords
subject: real-time machine learning framework
  topic: keywords
subject: retail inventory tracking
  topic: keywords
creator: Michael Cooney
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 1
ending page: 11

High-Frequency Point-of-Sale Signal Processing and Inventory Trigger Intelligence: AI-Powered Demand Sensing Architectures for Retail Supply Chain Responsiveness
type: item
description: Demand forecasting, or demand sensing, is one of the critical tasks in supply chain management that significantly contributes to its efficiency. However, consumer behavior volatility, driven by preference changes, external influencing factors such as media and social networks, or uncertainty, significantly impacts the forecasting practice of retail supply chains, which increasingly requires understanding consumer behavioral traits and mining respective data using adaptive retail demand sensing s
subject: Articles
  topic: section
subject: high-frequency point-of-sale signal processing
  topic: keywords
subject: inventory trigger intelligence
  topic: keywords
subject: demand sensing architectures
  topic: keywords
subject: retail supply chain responsiveness
  topic: keywords
subject: machine learning
  topic: keywords
creator: Tomohiro Naraoka
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 1
ending page: 8

Graph-Based Anomaly Propagation Analysis in Point-of-Sale Networks: AI-Enhanced Fraud Detection in Retail Transaction Ecosystems
type: item
description: The advent of digital transformation has ushered in an era of hyper-connected consumers, allowing for seamless transactions across multiple platforms. However, this rapid migration online has provided new opportunities for fraud, resulting in a 33% increase in fraudulent activities over the years since 2016. We chose retail transactions as our focus, as reports indicate that losses incurred in the retail industry doubled from 2019 onwards due to the pandemic and migratory fraud.
subject: Articles
  topic: section
subject: graph-based anomaly propagation analysis
  topic: keywords
subject: point-of-sale networks
  topic: keywords
subject: ai-enhanced fraud detection
  topic: keywords
subject: retail transaction ecosystems
  topic: keywords
subject: machine learning
  topic: keywords
creator: Małgorzata Pioro-Mianowska
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
date: 2024-12-31
publication date: 2024-12-31
starting page: 41
ending page: 48

Multi-Modal Clinical Data Harmonisation and Feature Extraction: AI-Enhanced Systems for Automated Biomedical Data Analysis and Interpretation
type: item
description: Biomedical data are increasing in both size and complexity; however, the capabilities of biomedical data analysis using conventional methods are limited. Artificial intelligence has reshaped the process of biomedical data analysis.
subject: Articles
  topic: section
subject: multi-modal clinical data harmonisation
  topic: keywords
subject: feature extraction
  topic: keywords
subject: ai-enhanced systems
  topic: keywords
subject: automated biomedical data analysis
  topic: keywords
subject: machine learning
  topic: keywords
creator: Christos Papachristou
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 9
ending page: 19

Dynamic Risk Classification and Premium Calibration Through Streaming Analytics: A Real-Time Machine Learning Framework for Insurance Underwriting Optimisation
type: item
description: Underwriting processes have moved from the company's headquarters to the cloud. At the same time, financial and insurance markets are ardently adopting artificial intelligence-based solutions, with those technologies and processes enabling superhuman performance in many applications. The data-driven approaches, and thus the AI, are gradually yet significantly overthrowing the old deductive manner, which may have seemed to be quite economically justified in the recent past.
subject: Articles
  topic: section
subject: dynamic risk classification
  topic: keywords
subject: premium calibration
  topic: keywords
subject: streaming analytics
  topic: keywords
subject: real-time machine learning framework
  topic: keywords
subject: insurance underwriting optimisation
  topic: keywords
creator: Bengi Gönen
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
date: 2024-12-31
publication date: 2024-12-31
starting page: 12
ending page: 19

End-to-End Tracking Intelligence and Predictive Exception Management: AI-Enhanced Frameworks for Real-Time Supply Chain Visibility
type: item
description: In today's global market, the influx of goods and siloed sources of information have made supply chains more complex than ever. Enterprises are now in need of monitoring and managing their supply chain processes in real time. The field of AI is now one of the most transformative technologies to drive manufacturers and distributors into Industry 4.0.
subject: Articles
  topic: section
subject: end-to-end tracking intelligence
  topic: keywords
subject: predictive exception management
  topic: keywords
subject: ai-enhanced frameworks
  topic: keywords
subject: real-time supply chain visibility
  topic: keywords
subject: machine learning
  topic: keywords
creator: Fillia Makedon
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
date: 2024-12-31
publication date: 2024-12-31
starting page: 20
ending page: 30

Ensemble Demand Forecasting with Exogenous Signal Integration: AI-Driven Approaches to Enhanced Retail Supply Chain Planning Accuracy
type: item
description: Introduction Retail supply chain forecasting is crucial for effectively operating the retail supply chain and goes a long way in determining the efficiency of the operation. Efficient forecasting can tremendously influence the inventory level in the supply chain, which also impacts the profits of each partner in the retail network. Accurate supply chain forecasting also helps to enhance customer satisfaction, which further improves the profitability of the organization.
subject: Articles
  topic: section
subject: ensemble demand forecasting
  topic: keywords
subject: exogenous signal integration
  topic: keywords
subject: approaches to enhanced retail supply chain planning accuracy
  topic: keywords
subject: machine learning
  topic: keywords
creator: Agnieszka Chęć
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
date: 2024-12-31
publication date: 2024-12-31
starting page: 31
ending page: 40

Atomic-Level Binding Affinity Estimation via Physics-Informed Neural Networks: AI-Enhanced Computational Methods for Protein-Ligand Interaction Prediction
type: item
description: Predicting the binding affinity, preferred orientation, and stereochemistry of the complex between a protein receptor and a small-molecule ligand is a critical step in structure-driven drug discovery and development. Traditionally, this has been achieved using computational methods that explicitly treat the electronic structure of the atoms in the model. A variety of challenges have been encountered in the process.
subject: Articles
  topic: section
subject: atomic-level binding affinity estimation
  topic: keywords
subject: physics-informed neural networks
  topic: keywords
subject: ai-enhanced computational methods
  topic: keywords
subject: protein-ligand interaction prediction
  topic: keywords
subject: machine learning
  topic: keywords
creator: Matej Rojc
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/
date: 2021-12-31
publication date: 2021-12-31
starting page: 107
ending page: 119

Automated Claims Adjudication and Resolution Pathway Intelligence: Machine Learning Frameworks for Insurance Settlement Process Optimisation
type: item
description: Today, insurance is a rapidly developing area that is constantly adapting to technological innovations and the changing requirements of end users. Relevant changes are also associated with significant development in the field of AI. This is why the subject of this publication is a presentation of the possibilities of applying AI solutions to the optimization of operations connected with the insurance process. The insurance sector deals with a significant number of customers.
subject: Articles
  topic: section
subject: automated claims adjudication
  topic: keywords
subject: resolution pathway intelligence
  topic: keywords
subject: machine learning frameworks
  topic: keywords
subject: insurance settlement process optimisation
  topic: keywords
creator: Ayşe Erçil Başaran
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/
date: 2021-12-31
publication date: 2021-12-31
starting page: 120
ending page: 129

Bioprocess Parameter Optimisation and Cell Culture Intelligence: Machine Learning Approaches to Enhanced Biopharmaceutical Development and Manufacturing
type: item
description: The pharmaceutical industry is key to the healthcare ecosystem; within this, biopharmaceuticals or biological drugs are a significant domain. They are valued for their importance in treating patients with malignant diseases, genetic disorders, or producing vaccines against diseases. This field, compared to conventional pharmaceutical products, deals with drugs derived from living organisms that are far more complicated and more difficult for quality assurance.
subject: Articles
  topic: section
subject: bioprocess parameter optimisation
  topic: keywords
subject: cell culture intelligence
  topic: keywords
subject: machine learning approaches to enhanced biopharmaceutical development
  topic: keywords
subject: manufacturing
  topic: keywords
creator: Byung-Woo Kim
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
date: 2024-06-30
publication date: 2024-06-30
starting page: 89
ending page: 97

Carbon Footprint Modelling and Circular Economy Optimisation: AI-Driven Frameworks for Environmental Sustainability in Supply Chain Management
type: item
description: In the backdrop of the global climate debate, the world is looking to achieve socio-economic objectives through a change in the existing way businesses are managed. Sustainable supply chain practices that address the socio-economic and environmental objectives of businesses have become central. There is a strong call for sustainability from consumers, regulators, stakeholders, and activist shareholders.
subject: Articles
  topic: section
subject: carbon footprint modelling
  topic: keywords
subject: circular economy optimisation
  topic: keywords
subject: frameworks
  topic: keywords
subject: environmental sustainability
  topic: keywords
subject: machine learning
  topic: keywords
creator: Michael Hitchens
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
date: 2024-12-31
publication date: 2024-12-31
starting page: 1
ending page: 11

Zero-Trust Architecture for Securing Multi-Cloud Environments
type: item
description: The proliferation of multi-cloud environments has rendered traditional perimeter-based security models obsolete, necessitating the adoption of a Zero-Trust Architecture (ZTA) to mitigate evolving cyber threats. This paper explores the implementation of ZTA in multi-cloud infrastructures, emphasizing the principles of strict identity verification, granular access control, and continuous monitoring. It examines security challenges such as lateral movement, unauthorized access, and cloud-native attack vectors, demonstrating how ZTA enforces least-privilege access and micro-segmentation to fortify cloud workloads. Furthermore, it evaluates policy enforcement mechanisms, identity and access management (IAM), and the role of artificial intelligence in adaptive threat detection. Case studies illustrate successful ZTA deployments in securing multi-cloud ecosystems, highlighting their effectiveness in reducing attack surfaces. The study concludes with an analysis of performance trade-offs and best practices for enterprises transitioning to a zero-trust security paradigm.
subject: Articles
  topic: section
subject: zero-trust
  topic: keywords
subject: multi-cloud security
  topic: keywords
subject: network defense
  topic: keywords
subject: cloud computing
  topic: keywords
creator: Hassan Rehan
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-09-15
publication date: 2022-09-15
starting page: 236
ending page: 273

Data Encryption and IAM Policies: Best Practices for AWS Ecosystems
type: item
description: Securing sensitive data in AWS ecosystem remains a critical concern for enterprises as cloud adoption increases. The aim of this paper is to explore the best practises for data encryption Identity and Access Management (IAM) policies in AWS To ensure robust security postures against new evolving threats. As we provide an in-depth analysis of AWS-native encryption mechanism which includes AWS Key Management Service (KMS), envelope encryption, and hardware security modules (HSMs), and also examines the effectiveness in securing data confidentiality and integrity.
subject: Articles
  topic: section
subject: AWS security
  topic: keywords
subject: data encryption
  topic: keywords
subject: IAM policies
  topic: keywords
creator: Venkata Ramana Gudelli
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-08-16
publication date: 2022-08-16
starting page: 210
ending page: 235

Securing Continuous Integration/Deployment Pipelines with Single Sign-On (SSO) in Cloud DevOps
type: item
description: Rapid adaptation of cloud-based DevOps practises makes it necessary to protect Continuous Integration/Continuous Deployment (CI/CD) pipelines more robust using different security mechanisms to protect unauthorised access and misuse of credentials. Single Sign-On (SSO) is emerging as a crucial security control which enables seamless authentication at the same time mitigating risk associated with credential leak and identity fragmentation. The objective of this paper is to explore the integration of SSO in CI/CD pipeline which enhance the security policies, streamline identity management, and enforce access control policy across cloud development environments.
subject: Articles
  topic: section
subject: Single Sign-On
  topic: keywords
subject: CI/CD security
  topic: keywords
subject: cloud DevOps
  topic: keywords
creator: Vivek Sheetal Dhaduvai
creator: Raghuvaran Kendyala
creator: Sandeep Batchu
creator: Kendyala Srinivasulu Harshavardhan
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-03-14
publication date: 2024-03-14
starting page: 50
ending page: 88

Advancements in Passwordless Authentication: The Future of SSO in Cloud IAM
type: item
description: The evolution of passwordless authentication in cloud-based identity and access management system shows the paradigm shift in enterprise security. Traditional password-based authentication systems are becoming vulnerable to phishing attacks, credential thefts, brute force attacks, these attacks results in adaptation of advanced authentication models. The aim of this paper is to explore contemporary password less authentication technologies such as biometric authentication, hardware security keys, and cryptographic techniques like WebAuthn and FIDO2.
subject: Articles
  topic: section
subject: passwordless authentication
  topic: keywords
subject: cloud IAM
  topic: keywords
subject: Single Sign-On
  topic: keywords
creator: Vivek Sheetal Dhaduvai
creator: Raghuvaran Kendyala
creator: Sandeep Batchu
creator: Kendyala Srinivasulu Harshavardhan
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-06-17
publication date: 2023-06-17
starting page: 95
ending page: 136

Enhancing Cloud DevOps Pipelines with SSO for CI/CD Security
type: item
description: Integration of Sign-On (SSO) in cloud based DevOps environment is marked as a crucial development in enhancing security for Continuous Integration and Continuous Deployment (CI/CD) pipeline. As DevOps speedup software delivery through automation the expansion of access point and user credentials introduces various security vulnerabilities such as credential sprawl, unauthorized access, and compliance risks. Through the implementation of SSO organisations can centralize authentication, enforce stringent access controls, and reduces identity-based threats mean while improving developer productivity. This research paper aims to provide a technical analysis of SSO integration in CI/CD workflow and the impact on authentication mechanisms, role-based access control (RBAC), and secure secrets management.
subject: Articles
  topic: section
subject: Single Sign-On
  topic: keywords
subject: DevOps security
  topic: keywords
creator: Vivek Sheetal Dhaduvai
creator: Raghuvaran Kendyala
creator: Sandeep Batchu
creator: Kendyala Srinivasulu Harshavardhan
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-04-15
publication date: 2022-04-15
starting page: 113
ending page: 152

Identity Federation for Cross-Cloud Workflows: Challenges and Best Practices
type: item
description: Identity Federation is a crucial supporter for secure and seamless authentication across multiple cloud environments which makes interoperability and access management in cross cloud workflows. But achieving robust identity federation in heterogeneous cloud ecosystem creates number of technical and operation challenges such as protocol standardization, trust establishment, security vulnerabilities, and performance overhead. This paper aims to explore the in-depth challenges, analysing authentication mechanism such as Security Assertion Markup Language (SAML), OpenID Connect (OIDC), and OAuth 2.0.
subject: Articles
  topic: section
subject: identity federation
  topic: keywords
subject: cross-cloud workflows
  topic: keywords
creator: Vivek Sheetal Dhaduvai
creator: Raghuvaran Kendyala
creator: Sandeep Batchu
creator: Kendyala Srinivasulu Harshavardhan
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2021-04-14
publication date: 2021-04-14
starting page: 85
ending page: 123

Security-First Frameworks for Multi-Tenant PaaS Platforms: Challenges and Solutions
type: item
description: The proliferation of cloud computing platforms has led to a significant adoption of Platform-as-a-Service (PaaS) offerings in multi-tenant environments, where multiple customers (tenants) share the same infrastructure while maintaining logical isolation. However, this multi-tenancy paradigm poses unique security challenges, primarily due to the shared nature of the underlying infrastructure, which requires effective mechanisms for ensuring tenant-specific confidentiality, integrity, and access control. This paper proposes a security-first framework designed to address key security concerns in multi-tenant PaaS platforms, specifically focusing on Tenant-Aware Role-Based Access Control (RBAC), encryption challenges, and Identity and Access Management (IAM) systems for robust tenant-specific authentication and authorization. A fundamental aspect of multi-tenant PaaS environments is the proper enforcement of access control mechanisms that prevent unauthorized access to resources and data belonging to other tenants. This paper introduces a Tenant-Aware RBAC model that allows administrators to define roles and permissions in a tenant-specific context, ensuring that users within each tenant have appropriate access to their resources while preventing cross-tenant data leakage. The proposed RBAC model takes into account dynamic environments where tenants can have differing security requirements and access patterns. The paper discusses the inherent challenges in defining, managing, and enforcing RBAC policies in such contexts, particularly considering tenant-specific policies that must be both scalable and flexible to accommodate growing and varying tenant needs. In addition to access control, encryption is another critical aspect of security in multi-tenant PaaS platforms. The shared infrastructure often necessitates the use of encryption to safeguard tenant data both at rest and in transit. This paper explores the challenges associated with implementing encryption in such e…
subject: Articles
  topic: section
subject: multi-tenant PaaS
  topic: keywords
subject: security-first framework
  topic: keywords
subject: Tenant-Aware RBAC
  topic: keywords
creator: Debabrata Das
creator: Muthuraman Saminathan
creator: Aarthi Anbalagan
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2021-03-01
publication date: 2021-03-01
starting page: 39
ending page: 84