Cybersecurity and Network Defense Research
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: Worldwide Language: English Editorial Contact - editor@thesciencebrigade.com
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.
Translational Data Fusion Architectures for Efficacy Signal Propagation: AI-Enhanced Systems for Clinical-Preclinical Data Integration in Drug Development
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.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
Total Cost of Ownership Modelling and Procurement Intelligence: AI-Driven Approaches to Retail Supply Chain Cost Optimisation
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
approaches to retail supply chain cost optimisation
Structure-Based Molecular Optimisation Through Deep Generative Networks: AI-Enhanced Computational Strategies for Rational Drug Design
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
Tumour Heterogeneity Profiling and Molecular Subtype Classification: AI-Enhanced Systems for Precision Oncology Drug Development Personalisation
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.
precision oncology drug development personalisation
Multi-Omics Data Fusion Through Attention-Based Neural Networks: AI-Enhanced Systems for Integrated Genomic, Proteomic, and Metabolomic Analysis in Drug Discovery
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.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
Multi-Target Neuroprotection Lead Identification Through Deep Learning: AI-Driven Platforms for Enhanced Drug Discovery in Neurodegenerative Disease
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.
multi-target neuroprotection lead identification
Network Topology Analysis and Claim Linkage Intelligence: Machine Learning Approaches to Enhanced Fraud Detection in Insurance Operations
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.
machine learning approaches to enhanced fraud detection
Pharmacokinetic-Pharmacodynamic Modelling and Adaptive Dosing Intelligence: AI-Driven Platforms for Patient-Specific Drug Dosing Optimisation
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.
pharmacokinetic-pharmacodynamic modelling
patient-specific drug dosing optimisation
Policyholder Behavioural Profiling and Dynamic Product Customisation: Machine Learning Frameworks for Personalised Insurance Offerings
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.
Predictive Physicochemical Property Modelling and Process Parameter Optimisation: AI-Driven Platforms for Pharmaceutical Formulation Development
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.
predictive physicochemical property modelling
Probabilistic Demand Signal Decomposition in Multi-Echelon Networks: Machine Learning Architectures for Supply Chain Forecasting Accuracy
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.
probabilistic demand signal decomposition
Proteome-Wide Binding Site Identification Through Graph Convolutional Networks: AI-Enhanced Computational Methods for Drug Target Prediction
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.
proteome-wide binding site identification
RFID-Enabled Inventory State Estimation and Automated Replenishment: A Real-Time Machine Learning Framework for Retail Inventory Tracking and Management
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.
High-Frequency Point-of-Sale Signal Processing and Inventory Trigger Intelligence: AI-Powered Demand Sensing Architectures for Retail Supply Chain Responsiveness
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
high-frequency point-of-sale signal processing
Graph-Based Anomaly Propagation Analysis in Point-of-Sale Networks: AI-Enhanced Fraud Detection in Retail Transaction Ecosystems
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.
graph-based anomaly propagation analysis
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
Multi-Modal Clinical Data Harmonisation and Feature Extraction: AI-Enhanced Systems for Automated Biomedical Data Analysis and Interpretation
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.
Dynamic Risk Classification and Premium Calibration Through Streaming Analytics: A Real-Time Machine Learning Framework for Insurance Underwriting Optimisation
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.
End-to-End Tracking Intelligence and Predictive Exception Management: AI-Enhanced Frameworks for Real-Time Supply Chain Visibility
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.
Ensemble Demand Forecasting with Exogenous Signal Integration: AI-Driven Approaches to Enhanced Retail Supply Chain Planning Accuracy
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.
approaches to enhanced retail supply chain planning accuracy
Atomic-Level Binding Affinity Estimation via Physics-Informed Neural Networks: AI-Enhanced Computational Methods for Protein-Ligand Interaction Prediction
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.
atomic-level binding affinity estimation
Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/
Automated Claims Adjudication and Resolution Pathway Intelligence: Machine Learning Frameworks for Insurance Settlement Process Optimisation
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.
insurance settlement process optimisation
Bioprocess Parameter Optimisation and Cell Culture Intelligence: Machine Learning Approaches to Enhanced Biopharmaceutical Development and Manufacturing
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.
machine learning approaches to enhanced biopharmaceutical development
Carbon Footprint Modelling and Circular Economy Optimisation: AI-Driven Frameworks for Environmental Sustainability in Supply Chain Management
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.
Zero-Trust Architecture for Securing Multi-Cloud Environments
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.
Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
Data Encryption and IAM Policies: Best Practices for AWS Ecosystems
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.
Securing Continuous Integration/Deployment Pipelines with Single Sign-On (SSO) in Cloud DevOps
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.
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
Advancements in Passwordless Authentication: The Future of SSO in Cloud IAM
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.
Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
Enhancing Cloud DevOps Pipelines with SSO for CI/CD Security
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.
Identity Federation for Cross-Cloud Workflows: Challenges and Best Practices
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.
Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
Security-First Frameworks for Multi-Tenant PaaS Platforms: Challenges and Solutions
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 environments, specifically focusing on the management of encryption keys and the isolation of data between tenants. One of the primary concerns is the management of encryption keys in a way that allows tenants to retain control over their data while also ensuring that platform administrators can manage the security of the underlying infrastructure. The paper proposes an approach for tenant-specific encryption key management that balances control and usability, offering practical solutions to prevent unauthorized access or data leaks between tenants. Another significant challenge in multi-tenant PaaS platforms is the implementation of an effective IAM system for managing tenant-specific authentication and authorization. Given that each tenant may have unique authentication requirements, ranging from traditional username-password schemes to more advanced multi-factor authentication (MFA) mechanisms, a comprehensive IAM system is necessary to support a variety of authentication methods. This paper examines existing IAM frameworks and identifies gaps in their applicability to multi-tenant environments. The paper proposes a modular IAM architecture capable of supporting flexible tenant-specific authentication protocols, ensuring that tenants can customize their authentication mechanisms based on their security requirements without compromising the security posture of the entire platform. Moreover, the paper outlines the use of federated identity management, which allows seamless integration with external identity providers, ensuring that tenants can maintain consistency in their identity management systems while taking advantage of platform capabilities. In this research, the proposed framework is evaluated with respect to its scalability, performance, and flexibility. The paper includes several use cases and case studies to demonstrate the practicality of the framework in addressing the security concerns of multi-tenant PaaS platforms. Specifically, these use cases highlight how the proposed solutions can be applied to real-world platforms, including challenges such as handling varying levels of tenant resource consumption, ensuring proper isolation in shared database systems, and meeting compliance requirements in regulated industries. Additionally, the paper discusses the trade-offs between security and performance, particularly in relation to encryption and IAM systems, providing insights into how to optimize the proposed framework for different deployment scenarios.