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.
publisher: The Science Brigade Publishers
language: en
publication name: Cybersecurity and Network Defense Research
issn: 2456-950X
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: 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
items: Securing Multi-Tenant Cloud Systems for Insurance Platforms Through Isolation and Compliance Strategies
items: Predictive Analytics and AI-Driven Threat Intelligence for Cloud Cybersecurity
items: Implementing Zero Trust Security Architectures for Financial Services in Cloud Environments
items: Encryption Standards and Tokenization Techniques for Securing Banking Cloud Infrastructure
items: Advanced Behavioral Analytics for User and Entity Behavior Anomaly Detection in Hybrid Cloud Environments
items: AI-Enhanced Malware Analysis: Breaking Down Advanced Cyber Threats with Precision
items: Intrusion Detection System for Prediction Cyber Threats based on Machine Learning Techniques
items: Synthetic Test Data Generation Using Generative AI in Healthcare Applications: Addressing Compliance and Security Challenges
items: Optimizing B2B Pharmacy Applications with Cloud Infrastructure: A Case Study on Performance and Security
items: Large Language Models for Test Data Fabrication in Healthcare: Ensuring Data Security and Reducing Testing Costs
items: Enterprise Cloud Architecture for Data Security and Compliance: Building Privacy-Centric Cloud Solutions for Global Enterprises
items: Cloud Compliance Implementation in Healthcare: Ensuring Security, Privacy, and Data Integrity in Cloud-Based Solutions
items: AI-Enhanced Mobile Platform Optimization: Leveraging Machine Learning for Predictive Maintenance, Performance Tuning, and Security Hardening
items: AI-Driven Product Management Strategies for Enhancing Customer-Centric Mobile Product Development: Leveraging Machine Learning for Feature Prioritization and User Experience Optimization
items: Quantum-Resistant Cryptography for Automotive Cybersecurity: Implementing Post-Quantum Algorithms to Secure Next-Generation Autonomous and Connected Vehicles
items: Intrusion Detection Systems for Automotive Networks: Implementing AI-Powered Solutions to Enhance Cybersecurity in In-Vehicle Communication Protocols
items: End-to-End Cybersecurity Strategies for Autonomous Vehicles: Leveraging Multi-Layered Defence Mechanisms to Safeguard Automotive Ecosystems
items: Comprehensive Cybersecurity Framework for Connected Vehicles: Securing Vehicle-to-Everything (V2X) Communication Against Emerging Threats in the Automotive Industry
items: Blockchain-Based Cybersecurity Solutions for Automotive Industry: Protecting Over-the-Air (OTA) Software Updates in Autonomous and Connected Vehicles
items: Secure Multi-Party Computation for Privacy-Preserving Data Analytics in Cybersecurity
items: Cyber Resilience Frameworks: Exploring Cyber Resilience Frameworks and Strategies for Organizations to Recover From Cyber Attacks and Maintain Operational Continuity
items: Zero-Day Exploit Detection: Analyzing Machine Learning Approaches for Detecting Zero-Day Exploits and Previously Unseen Vulnerabilities to Enhance Proactive Threat Defense
items: Network Traffic Analysis: Studying Anomaly Detection Approaches for Network Traffic Analysis to Identify Suspicious Patterns and Behaviors Indicative of Cyber Threats

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

Securing Multi-Tenant Cloud Systems for Insurance Platforms Through Isolation and Compliance Strategies
type: item
description: Multi-tenant cloud systems have become a cornerstone of modern digital infrastructure, particularly in data-intensive industries such as insurance. These systems allow multiple tenants to share resources, reducing operational costs while increasing scalability. However, the inherent shared nature of these environments introduces unique challenges related to tenant isolation, data security, and regulatory compliance. This paper explores the application of advanced techniques and tools to secure multi-tenant cloud systems for insurance platforms, focusing on Kubernetes for robust tenant isolation, encryption strategies for safeguarding shared datasets, and sophisticated monitoring solutions to meet compliance requirements. Kubernetes, an open-source container orchestration platform, has emerged as a powerful tool for achieving granular tenant isolation in multi-tenant environments. By leveraging Kubernetes namespaces, resource quotas, and network policies, this paper examines how tenant workloads can be effectively isolated to prevent data leakage and unauthorized access. Furthermore, we delve into the use of encryption mechanisms, including data-at-rest and data-in-transit encryption, to enhance the security of shared datasets in compliance with industry standards such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). Encryption key management solutions and their integration into cloud-native architectures are discussed, emphasizing their role in ensuring robust data protection. To address the multifaceted compliance challenges faced by insurance platforms, we propose the adoption of real-time monitoring and auditing solutions. These solutions leverage advanced logging mechanisms, anomaly detection algorithms, and policy-based alerts to track and enforce compliance. The paper also evaluates open-source and commercial tools such as Prometheus, Grafana, and cloud-native security platforms that provide …
subject: Articles
  topic: section
subject: multi-tenant cloud systems
  topic: keywords
subject: Kubernetes
  topic: keywords
creator: Debabrata Das
creator: Akhil Reddy Bairi
creator: Muthuraman Saminathan
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-01-07
publication date: 2022-01-07
starting page: 71
ending page: 112

Predictive Analytics and AI-Driven Threat Intelligence for Cloud Cybersecurity
type: item
description: Cloud computing has revolutionized the IT landscape, offering scalable, on-demand resources and flexibility for businesses. However, this evolution has also introduced new security challenges, particularly in the context of cyber threats that target cloud infrastructures. With the increasing sophistication of cyberattacks, there is an urgent need for advanced, proactive measures to safeguard cloud environments. Predictive analytics, particularly through the application of machine learning (ML), has emerged as a crucial tool in cloud cybersecurity. This research explores how predictive analytics and AI-driven threat intelligence can be leveraged to identify potential attack vectors, detect anomalies, and implement more effective defense strategies. Specifically, the paper delves into the integration of predictive machine learning models with historical attack pattern analysis and real-time external threat feed data to anticipate, identify, and mitigate cloud security threats. This paper begins by addressing the fundamental concepts of predictive analytics and AI in the cybersecurity context. Predictive analytics refers to the use of historical data, machine learning algorithms, and statistical models to forecast potential future events, including cyberattacks. In the domain of cloud security, this methodology facilitates early detection of emerging threats, enabling organizations to proactively defend against attacks rather than relying solely on reactive measures. Machine learning techniques, particularly supervised and unsupervised learning, have been integral to the development of predictive models that learn from past data and adapt to evolving cyber threats. These models can identify patterns indicative of potential attacks, such as unusual access patterns, abnormal data transfers, or the presence of malware. Furthermore, this research explores the integration of real-time external threat feeds with cloud security systems, a critical aspect of enhancing the effe…
subject: Articles
  topic: section
subject: predictive analytics
  topic: keywords
subject: machine learning
  topic: keywords
subject: AI-driven threat intelligence
  topic: keywords
creator: Sayantan Bhattacharyya
creator: Vincent Kanka
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-11-15
publication date: 2022-11-15
starting page: 165
ending page: 209

Implementing Zero Trust Security Architectures for Financial Services in Cloud Environments
type: item
description: The dynamic evolution of cloud computing has necessitated a paradigm shift in cybersecurity frameworks, particularly in sectors such as financial services where the confidentiality, integrity, and availability of data are paramount. Traditional perimeter-based security models are increasingly inadequate in addressing sophisticated threats, distributed systems, and the high-value data inherent to financial institutions. This paper explores the implementation of Zero Trust Security Architectures (ZTSA) tailored to financial services operating in cloud environments, offering a robust approach to safeguarding sensitive workloads. Zero trust, premised on the principle of "never trust, always verify," challenges conventional security assumptions by continuously authenticating and authorizing users, devices, and applications irrespective of their location within or outside traditional network perimeters. In this study, we analyze key architectural components, including micro-segmentation, identity and access management (IAM) policies, and advanced anomaly detection mechanisms, which collectively ensure granular control and real-time monitoring of data flows. Micro-segmentation, as a cornerstone of zero trust, involves dividing cloud workloads into discrete segments to enforce least-privilege access and isolate potential breaches. By leveraging cloud-native tools and third-party solutions, financial institutions can ensure that sensitive data remains insulated, even in the event of a compromise. This paper delves into practical strategies for implementing micro-segmentation in heterogeneous cloud environments, emphasizing its role in reducing the attack surface while maintaining operational agility. IAM policies are another critical component of ZTSA, underpinning secure access to resources based on dynamic contextual factors such as user roles, device health, and behavioral patterns. Through policy-based access controls, integration with multifactor authentication (MFA), a…
subject: Articles
  topic: section
subject: zero trust security architecture
  topic: keywords
subject: financial services
  topic: keywords
subject: cloud environments
  topic: keywords
creator: Sayantan Bhattacharyya
creator: Debabrata Das
creator: Muthuraman Saminathan
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2021-10-20
publication date: 2021-10-20
starting page: 21
ending page: 63

Encryption Standards and Tokenization Techniques for Securing Banking Cloud Infrastructure
type: item
description: The growing reliance of the banking sector on cloud infrastructure necessitates robust security frameworks to protect sensitive customer information and comply with regulatory standards. This paper investigates the implementation of encryption standards and tokenization techniques tailored for securing banking cloud infrastructures. Emphasis is placed on advanced encryption methods, their suitability for various banking operations, and their compliance with established standards, including Payment Card Industry Data Security Standards (PCI DSS) and Federal Financial Institutions Examination Council (FFIEC) guidelines. The study evaluates symmetric encryption algorithms such as Advanced Encryption Standard (AES) for data-at-rest and Transport Layer Security (TLS) for data-in-transit, discussing their respective strengths and vulnerabilities in cloud environments. Furthermore, the analysis extends to emerging encryption techniques, including homomorphic encryption and quantum-resistant algorithms, highlighting their potential to address evolving cybersecurity threats. In addition to encryption mechanisms, the paper explores tokenization as a complementary approach to enhance data security by replacing sensitive information with non-sensitive tokens. The effectiveness of tokenization in mitigating risks associated with data breaches, ensuring compliance with industry standards, and supporting secure payment processing is critically analyzed. Different tokenization architectures, including format-preserving and vaultless tokenization, are examined with a focus on their scalability, performance implications, and compatibility with cloud-native applications. Case studies demonstrate the practical application of these techniques in real-world banking scenarios, showcasing their ability to meet stringent security and performance requirements. Regulatory compliance remains a cornerstone of banking security, and this paper delves into the integration of encryption and tokeniz…
subject: Articles
  topic: section
subject: encryption standards
  topic: keywords
subject: tokenization techniques
  topic: keywords
subject: cloud infrastructure security
  topic: keywords
creator: Debabrata Das
creator: Akhil Reddy Bairi
creator: Amsa Selvaraj
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2021-11-05
publication date: 2021-11-05
starting page: 64
ending page: 106

Advanced Behavioral Analytics for User and Entity Behavior Anomaly Detection in Hybrid Cloud Environments
type: item
description: The increasing adoption of hybrid cloud environments in enterprises has necessitated advanced mechanisms to ensure robust security and operational integrity. This research delves into the application of advanced behavioral analytics for detecting user and entity behavior anomalies in hybrid cloud environments, focusing on artificial intelligence (AI) and machine learning (ML) models to address the inherent complexity and dynamic nature of these infrastructures. Hybrid cloud environments, characterized by their interconnected public and private cloud systems, create unique challenges for security monitoring due to diverse user activities, heterogeneous workloads, and evolving threat landscapes. Establishing baseline behavior profiles for users and entities is a critical first step in addressing these challenges. This study explores supervised and unsupervised ML approaches, including clustering algorithms, such as k-means and DBSCAN, and outlier detection techniques, such as Isolation Forests and Local Outlier Factor (LOF), for modeling normal behavior patterns. The paper also examines the challenges associated with constructing reliable baselines in hybrid cloud settings, such as the variability of workloads, the diversity of user roles, and the continuous adaptation of cloud environments. Additionally, the integration of these models with Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) platforms is evaluated. Such integration enables automated incident detection and response, reducing the time to identify and mitigate threats. Key considerations include the harmonization of data ingestion from multi-cloud sources, real-time anomaly detection capabilities, and the orchestration of automated workflows for incident handling. By leveraging anomaly detection mechanisms, this research demonstrates how hybrid cloud environments can achieve enhanced situational awareness and improved threat response. Through case…
subject: Articles
  topic: section
subject: hybrid cloud security
  topic: keywords
subject: behavioral analytics
  topic: keywords
subject: anomaly detection
  topic: keywords
creator: Abdul Samad Mohammed
creator: Vincent Kanka
creator: Amsa Selvaraj
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-10-09
publication date: 2022-10-09
starting page: 127
ending page: 164

AI-Enhanced Malware Analysis: Breaking Down Advanced Cyber Threats with Precision
type: item
description: The rise in global cyber-attacks highlights the need for more sophisticated malware analysis tools and methodologies. As attackers use more advanced techniques, static signatures and heuristic rules are not adequate to detect attacks. The rise of artificial intelligence (AI), including machine learning (ML), deep learning (DL), and anomaly detection, has radically changed the way malware is detected, allowing it to have more adaptive and powerful protection. The paper provides an overview of AI-powered malware analysis — from evolving threats, through basic static and dynamic analysis, to anomaly detection for real-time threat monitoring. As a comparative analysis of AI’s effectiveness at detecting polymorphic, metamorphic and zero-day attacks shows, AI technologies are more effective than traditional signature-based approaches. In addition, issues of adversarial machine learning, model interpretability, and data-based retraining pipelines are discussed, mirroring current debates in industry and academia. It ends by identifying the importance of proactive AI systems in contemporary cybersecurity, and suggests research avenues such as federated learning, explainable AI, and aligning regulatory expectations with cutting-edge security.
subject: Articles
  topic: section
subject: cyber-attacks
  topic: keywords
subject: Malware
  topic: keywords
creator: Ahmed Elgalb
creator: Mahmoud Mohamed
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2021-12-09
publication date: 2021-12-09
starting page: 1
ending page: 20

Intrusion Detection System for Prediction Cyber Threats based on Machine Learning Techniques
type: item
description: The increasing development of cyber threats has urged the establishment of efficient Intrusion Detection Systems that can predict and mitigate these attacks. With the ability to predict future threats and implement mechanisms, machine learning techniques offer excellent solutions in making IDS more intelligent, autonomous, and adaptive for future threats. This paper elaborates the application of multiple ML-based techniques to detect and predict cyber threats within IDS systems, analyzing the effectiveness and performance as well as discussing the limitation areas. Comparisons among supervised, unsupervised, and hybrid models were presented to show each's capability in enhancing accuracy with an IDS and reduction of response times.
subject: Articles
  topic: section
subject: Intrusion Detection System (IDS)
  topic: keywords
subject: Cyber Threat Prediction
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Supervised Learning
  topic: keywords
subject: Unsupervised Learning
  topic: keywords
subject: Hybrid Models
  topic: keywords
creator: Iqra Naseer
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-04-15
publication date: 2022-04-15
starting page: 57
ending page: 70

Synthetic Test Data Generation Using Generative AI in Healthcare Applications: Addressing Compliance and Security Challenges
type: item
description: The increasing adoption of artificial intelligence (AI) in healthcare has led to a significant demand for robust and diverse datasets to train, test, and validate machine learning models. However, the sensitive nature of healthcare data, governed by strict regulations like HIPAA and GDPR, poses considerable challenges in data accessibility, security, and compliance. In this context, the generation of synthetic test data using generative AI models has emerged as a viable solution, offering a way to produce realistic and representative datasets without compromising patient privacy. This paper delves into the potential of generative AI, specifically models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), for the creation of synthetic healthcare data. The focus is on addressing the critical issues surrounding data security, privacy compliance, and the adequacy of synthetic data for performance testing in healthcare applications. Generative AI has demonstrated a remarkable ability to learn from real data distributions and produce high-quality synthetic data that mimics the statistical properties of real-world datasets. This capability is particularly important in healthcare, where the quality and representativeness of data directly influence the effectiveness of AI-driven solutions for diagnostics, treatment planning, and patient care. Synthetic test data generation offers a promising alternative to the traditional use of anonymized or de-identified data, which often suffers from potential re-identification risks and data quality degradation. However, while synthetic data generation mitigates some privacy risks, it introduces a new set of compliance and security challenges that must be carefully considered to ensure regulatory adherence. This paper systematically explores how generative AI models can be leveraged to generate synthetic test data while addressing compliance and security issues in healthcare. The discussion includes an in-dep…
subject: Articles
  topic: section
subject: generative AI
  topic: keywords
subject: synthetic data generation
  topic: keywords
creator: Lakshmi Durga Panguluri
creator: Subhan Baba Mohammed
creator: Thirunavukkarasu Pichaimani
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-11-13
publication date: 2023-11-13
starting page: 280
ending page: 319

Optimizing B2B Pharmacy Applications with Cloud Infrastructure: A Case Study on Performance and Security
type: item
description: This research paper explores the optimization of B2B pharmacy applications through the deployment of cloud infrastructure, focusing on a comprehensive case study that demonstrates improvements in security, performance, and customer service. As digital transformation accelerates in the pharmaceutical industry, B2B applications play a critical role in enhancing operational efficiency, supply chain management, and customer interactions. However, traditional on-premise solutions present limitations in scalability, performance optimization, and security, which necessitates the exploration of cloud-based alternatives. This study aims to provide an in-depth analysis of the integration of cloud infrastructure into B2B pharmacy platforms, emphasizing how cloud solutions address these challenges by leveraging their flexibility, scalability, and security features. The paper delves into the technical intricacies of cloud infrastructure, including cloud computing models, service delivery methods, and security protocols, to evaluate their effectiveness in transforming pharmacy operations. The paper begins with a detailed overview of the pharmaceutical industry's reliance on B2B applications, focusing on their importance for real-time inventory management, order processing, and supply chain coordination. The case study examines a B2B pharmacy platform that migrated from an on-premise system to a cloud-based infrastructure. This migration involved adopting a hybrid cloud model, combining the benefits of private cloud for secure data management and public cloud for scalable processing power. The research presents a thorough evaluation of the pre- and post-migration performance metrics, including system response time, transaction throughput, and latency, highlighting significant improvements in operational efficiency and customer service delivery. These performance gains are attributed to the inherent elasticity of cloud computing, which allows for dynamic resource allocation, thereb…
subject: Articles
  topic: section
subject: cloud infrastructure
  topic: keywords
subject: B2B pharmacy
  topic: keywords
creator: Anil Kumar Ratnala
creator: Naveen Pakalapati
creator: Bhavani Krothapalli
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-10-04
publication date: 2022-10-04
starting page: 45
ending page: 81

Large Language Models for Test Data Fabrication in Healthcare: Ensuring Data Security and Reducing Testing Costs
type: item
description: The advent of large language models (LLMs) presents a promising frontier in addressing significant challenges in healthcare data management, specifically in the domain of test data fabrication. As healthcare systems become increasingly reliant on data-driven methodologies, the need for comprehensive testing environments grows in parallel. However, the use of real patient data for testing raises concerns related to data privacy, security, and compliance with stringent regulatory frameworks such as HIPAA and GDPR. Moreover, the utilization of actual patient information in non-production environments creates ethical and legal risks, further complicating the process of ensuring robust and secure healthcare systems. This study investigates the potential of LLMs to generate synthetic test data as a solution to these challenges, providing a framework that ensures both the security of sensitive patient information and the reduction of associated costs linked to testing procedures. LLMs, powered by deep learning architectures, are capable of generating vast amounts of human-like text, which can be leveraged to produce highly realistic, domain-specific test data. In the context of healthcare, this entails the generation of synthetic patient records, clinical notes, diagnostic reports, and other medical documentation that mimic the characteristics of real data but do not compromise patient confidentiality. The use of synthetic data enables healthcare organizations to conduct comprehensive system testing, stress-testing of databases, and the validation of machine learning models in environments that closely resemble real-world conditions, without exposing actual patient information. This paper delves into the mechanisms through which LLMs can be trained to generate such data, exploring the model architectures, training processes, and the ethical implications of using fabricated data in critical healthcare systems. One of the key advantages of employing LLMs in this context is t…
subject: Articles
  topic: section
subject: large language models
  topic: keywords
subject: test data fabrication
  topic: keywords
creator: Ravi Kumar Burila
creator: Thirunavukkarasu Pichaimani
creator: Sahana Ramesh
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-09-11
publication date: 2023-09-11
starting page: 237
ending page: 279

Enterprise Cloud Architecture for Data Security and Compliance: Building Privacy-Centric Cloud Solutions for Global Enterprises
type: item
description: The increasing demand for cloud-based solutions has fundamentally reshaped how global enterprises approach data storage, processing, and accessibility. Yet, as cloud adoption accelerates, these organizations face unprecedented challenges in safeguarding data privacy and maintaining regulatory compliance, particularly in a landscape marked by intricate, region-specific data protection laws. This research delves into enterprise cloud architecture, with an emphasis on creating privacy-centric and compliant infrastructures tailored to meet the stringent demands of large, multinational corporations. The study underscores the complexities of integrating data security within cloud environments, where data flow across borders and compliance requirements vary, necessitating a robust architecture that addresses both data protection and legal obligations. Through an examination of cloud architecture components, such as data encryption techniques, identity and access management (IAM), and advanced monitoring and auditing systems, the paper offers a structured approach to designing enterprise cloud infrastructures that align with global data security mandates. A critical aspect of this research is the exploration of privacy-centric design principles within enterprise cloud frameworks. With data residency and sovereignty requirements becoming increasingly significant, cloud architectures must incorporate solutions that enable data localization and implement jurisdiction-specific controls. This paper discusses the deployment of multi-region cloud storage and processing mechanisms, as well as the role of geo-fencing capabilities to limit data movement in compliance with regional laws. Furthermore, the research addresses the importance of encryption, both at rest and in transit, alongside robust key management systems that ensure data confidentiality and integrity within distributed cloud environments. By examining end-to-end encryption mechanisms, secure enclaves, and homomorphic e…
subject: Articles
  topic: section
subject: enterprise cloud architecture
  topic: keywords
subject: data security
  topic: keywords
creator: Yeswanth Surampudi
creator: Manish Tomar
creator: Rama Krishna Inampudi
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-09-13
publication date: 2022-09-13
starting page: 1
ending page: 44

Cloud Compliance Implementation in Healthcare: Ensuring Security, Privacy, and Data Integrity in Cloud-Based Solutions
type: item
description: This research paper investigates the intricacies of implementing cloud compliance in healthcare systems, with a particular focus on maintaining security, privacy, and data integrity in cloud-based environments. As healthcare organizations increasingly adopt cloud computing solutions to enhance operational efficiency, reduce costs, and improve patient care, ensuring compliance with a complex array of regulatory frameworks becomes critical. The paper delves into the primary regulations governing healthcare data in cloud environments, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States, the General Data Protection Regulation (GDPR) in Europe, and other region-specific compliance mandates. Through a comprehensive analysis of these regulatory frameworks, the paper outlines how cloud providers and healthcare organizations can align their systems and processes to meet stringent compliance requirements. A critical aspect of cloud compliance implementation is the preservation of data security, privacy, and integrity—core principles that directly impact patient safety and trust in healthcare systems. This paper explores technical measures for safeguarding healthcare data, including encryption methodologies, identity and access management (IAM) solutions, multi-factor authentication, and secure data transmission protocols. These measures are essential for mitigating risks associated with unauthorized access, data breaches, and potential insider threats. Additionally, the paper discusses the role of cloud service providers (CSPs) in sharing compliance responsibilities with healthcare organizations, detailing the legal and technical obligations of CSPs in maintaining compliance, such as offering audit trails, data encryption services, and incident response plans. One of the central challenges in healthcare cloud compliance is ensuring that data privacy and security are maintained in multi-tenant environments, where multiple organizations …
subject: Articles
  topic: section
subject: cloud compliance
  topic: keywords
subject: healthcare systems
  topic: keywords
creator: Subhan Baba Mohammed
creator: Srinivasan Ramalingam
creator: Praveen Sivathapandi
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-11-07
publication date: 2022-11-07
starting page: 82
ending page: 127

AI-Enhanced Mobile Platform Optimization: Leveraging Machine Learning for Predictive Maintenance, Performance Tuning, and Security Hardening
type: item
description: In recent years, the integration of artificial intelligence (AI) and machine learning (ML) into mobile platforms has emerged as a transformative approach to enhancing operational efficiency, user experience, and security. This research paper examines the multifaceted applications of AI-driven techniques in optimizing mobile platform performance, focusing specifically on three critical domains: predictive maintenance, performance tuning, and security hardening. Through a comprehensive analysis of existing literature and contemporary case studies, this study elucidates how machine learning algorithms can be leveraged to anticipate hardware and software failures, dynamically adjust resource allocation, and fortify security protocols against emerging threats. The first section of the paper delves into predictive maintenance, where AI models are employed to analyze historical performance data and identify patterns indicative of potential malfunctions. By employing techniques such as supervised learning and anomaly detection, mobile platforms can proactively address maintenance needs, thereby minimizing downtime and extending the lifespan of devices. The integration of IoT (Internet of Things) sensors and data analytics further enhances the efficacy of predictive maintenance, providing real-time insights that facilitate timely interventions. In the realm of performance tuning, the research highlights how machine learning can optimize resource management by dynamically adjusting parameters based on user behavior and system load. Techniques such as reinforcement learning are discussed, wherein algorithms learn from historical data to make real-time adjustments that enhance application responsiveness and resource utilization. The paper also explores the role of AI in load balancing and power management, illustrating how intelligent systems can improve overall system efficiency while simultaneously reducing energy consumption. Security hardening is another pivotal aspect addr…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Mobile Platforms
  topic: keywords
subject: Predictive Maintenance
  topic: keywords
creator: Seema Kumari
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-08-08
publication date: 2024-08-08
starting page: 29
ending page: 49

AI-Driven Product Management Strategies for Enhancing Customer-Centric Mobile Product Development: Leveraging Machine Learning for Feature Prioritization and User Experience Optimization
type: item
description: In the rapidly evolving landscape of mobile product development, product managers face increasing complexity in aligning product features with ever-changing user preferences and technological advancements. The traditional methods of feature prioritization and user experience (UX) optimization, often guided by intuition and limited data analysis, struggle to keep pace with the vast and dynamic streams of user behavior data generated by mobile applications. This paper investigates how artificial intelligence (AI), specifically machine learning (ML), can transform product management strategies to make them more data-driven, customer-centric, and responsive to real-time user needs in mobile product development. By leveraging AI-driven analytics, product managers can unlock deeper insights into user behavior patterns, preferences, and pain points, enabling more informed and agile decision-making. The paper begins by reviewing the existing approaches to product management in mobile applications and the growing demand for enhanced customer-centricity in the design and development processes. It highlights the limitations of traditional methods, which often fail to adequately capture and process the scale of user data required for optimal decision-making. In contrast, AI-driven product management systems can ingest and analyze vast amounts of real-time data, providing actionable insights for improving product features and user satisfaction. Next, the paper focuses on machine learning's specific role in feature prioritization, a critical aspect of mobile product development. Traditional feature prioritization methods, such as the MoSCoW (Must have, Should have, Could have, and Won’t have) method, Kano model, and cost-benefit analysis, though effective to some extent, often rely on static inputs and do not dynamically adapt to evolving user needs. This paper demonstrates how machine learning algorithms, including classification models, clustering techniques, and predictive ana…
subject: Articles
  topic: section
subject: AI-driven product management
  topic: keywords
subject: machine learning
  topic: keywords
subject: feature prioritization
  topic: keywords
subject: user experience optimization
  topic: keywords
creator: Seema Kumari
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-11-14
publication date: 2023-11-14
starting page: 218
ending page: 236

Quantum-Resistant Cryptography for Automotive Cybersecurity: Implementing Post-Quantum Algorithms to Secure Next-Generation Autonomous and Connected Vehicles
type: item
description: As the automotive industry advances towards next-generation autonomous and connected vehicles, cybersecurity emerges as a critical concern due to the increasing reliance on digital communication networks and control systems. With the rapid development of quantum computing, traditional cryptographic methods, such as RSA, ECC, and AES, face potential vulnerabilities that could compromise the security of automotive networks. This research paper explores the implementation of quantum-resistant cryptographic algorithms, commonly referred to as post-quantum cryptography (PQC), specifically within the domain of automotive cybersecurity. The primary focus of this study is to address the imminent threats posed by quantum computing to conventional encryption standards and propose robust PQC frameworks tailored for securing vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and in-vehicle communication systems. The paper provides a comprehensive analysis of various quantum-resistant cryptographic schemes, such as lattice-based cryptography, hash-based cryptography, code-based cryptography, multivariate polynomial cryptography, and isogeny-based cryptography. It evaluates their applicability, performance, and feasibility for integration into automotive cybersecurity architectures. Each algorithm's strengths and limitations are assessed in the context of automotive systems' unique requirements, such as low latency, real-time processing, limited computational resources, and the need for long-term security. Furthermore, the research highlights the challenges of transitioning from current cryptographic protocols to post-quantum algorithms, including computational overhead, backward compatibility, and the complexities of managing hybrid cryptographic environments where both classical and post-quantum schemes coexist. The study also delves into the impact of implementing PQC on critical automotive cybersecurity components, such as secure boot, firmware updates, secure communi…
subject: Articles
  topic: section
subject: Quantum-resistant cryptography
  topic: keywords
subject: secure communication protocols
  topic: keywords
creator: Rajalakshmi Soundarapandiyan
creator: Praveen Sivathapandi
creator: Akila Selvaraj
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-11-28
publication date: 2023-11-28
starting page: 177
ending page: 218

Intrusion Detection Systems for Automotive Networks: Implementing AI-Powered Solutions to Enhance Cybersecurity in In-Vehicle Communication Protocols
type: item
description: As the automotive industry evolves towards increased autonomy and connectivity, the cybersecurity of in-vehicle communication networks has become a critical concern. Modern vehicles incorporate multiple interconnected electronic control units (ECUs) that communicate through various protocols such as the Controller Area Network (CAN), FlexRay, and Ethernet, forming complex networks that are vulnerable to cyber-attacks. Intrusion Detection Systems (IDS) are pivotal in safeguarding these networks by identifying and mitigating malicious activities. This paper explores the implementation of Artificial Intelligence (AI)-powered IDS for automotive networks, specifically focusing on in-vehicle communication protocols like CAN and Automotive Ethernet. Traditional IDS methods have proven insufficient due to the evolving nature of attack vectors targeting vehicular networks, necessitating more advanced, adaptive, and scalable solutions. AI-powered IDS, leveraging machine learning (ML) and deep learning (DL) algorithms, have shown significant promise in detecting zero-day attacks and sophisticated intrusion attempts that bypass conventional rule-based detection systems. This research provides a comprehensive analysis of the types of IDS applicable to automotive networks, including Signature-based, Anomaly-based, and Hybrid IDS, and emphasizes the growing preference for Anomaly-based IDS due to their adaptability and effectiveness against unknown threats. It discusses the architecture and operational principles of AI-based IDS, highlighting the role of supervised, unsupervised, and reinforcement learning algorithms in detecting anomalies within vehicular communication protocols. Techniques such as Support Vector Machines (SVM), Random Forests, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Autoencoders are examined for their effectiveness in identifying deviations from normal traffic patterns, signaling potential intrusions. The paper evaluates …
subject: Articles
  topic: section
subject: automotive networks
  topic: keywords
subject: intrusion detection systems
  topic: keywords
creator: Rajalakshmi Soundarapandiyan
creator: Yeswanth Surampudi
creator: Akila Selvaraj
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-08-29
publication date: 2023-08-29
starting page: 41
ending page: 86

End-to-End Cybersecurity Strategies for Autonomous Vehicles: Leveraging Multi-Layered Defence Mechanisms to Safeguard Automotive Ecosystems
type: item
description: The rise of autonomous vehicles (AVs) represents a paradigm shift in the automotive industry, promising enhanced safety, convenience, and efficiency. However, as AVs become more integrated into our daily lives, they also present a novel and substantial cybersecurity challenge due to their reliance on complex interdependent systems and extensive connectivity. This research paper presents a comprehensive examination of end-to-end cybersecurity strategies for autonomous vehicles, emphasizing multi-layered defense mechanisms to safeguard the entire automotive ecosystem. Autonomous vehicles are equipped with numerous sensors, communication modules, and computing systems that facilitate real-time decision-making, navigation, and interaction with external environments, rendering them susceptible to a myriad of cyber threats. Consequently, robust and holistic cybersecurity frameworks are paramount to ensuring their safe and reliable operation. This study aims to address the critical need for securing AVs through a multi-layered defense approach that encompasses various layers, including secure boot processes, encrypted communication channels, secure cloud integration, and advanced threat detection systems. The concept of a secure boot process is foundational to protecting the AV ecosystem from unauthorized software and firmware updates, ensuring that only legitimate and verified code is executed on vehicle systems. By establishing a root of trust, secure boot mechanisms prevent adversaries from injecting malicious code during system startup, which could compromise the vehicle's core functions. This paper delves into the architecture and implementation of secure boot processes, discussing their efficacy in thwarting a wide range of attacks, from firmware tampering to rootkit installations. Moreover, the integration of hardware-based security modules, such as Trusted Platform Modules (TPMs), is explored to further reinforce the integrity of the boot sequence and enhance overa…
subject: Articles
  topic: section
subject: cybersecurity
  topic: keywords
subject: autonomous vehicles
  topic: keywords
creator: Mahadu Vinayak Kurkute
creator: Akila Selvaraj
creator: Amsa Selvaraj
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-10-09
publication date: 2023-10-09
starting page: 134
ending page: 177

Comprehensive Cybersecurity Framework for Connected Vehicles: Securing Vehicle-to-Everything (V2X) Communication Against Emerging Threats in the Automotive Industry
type: item
description: The rapid development of connected vehicles and Vehicle-to-Everything (V2X) communication has transformed the automotive industry, paving the way for intelligent transportation systems that enhance road safety, traffic management, and driving efficiency. However, this technological advancement also introduces a wide array of cybersecurity challenges that need to be addressed to ensure the safety, privacy, and reliability of these systems. The integration of V2X communication technologies, which include Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Pedestrian (V2P), and Vehicle-to-Network (V2N) interactions, has made vehicles increasingly vulnerable to sophisticated cyber-attacks. These attacks, ranging from remote hacking and data breaches to signal spoofing and denial-of-service (DoS) attacks, pose significant risks to vehicle safety and user privacy, potentially resulting in severe financial, operational, and reputational damage to automotive manufacturers and service providers. This paper proposes a comprehensive cybersecurity framework specifically tailored for connected vehicles, focusing on securing V2X communication against these emerging threats in the automotive industry. The proposed cybersecurity framework incorporates a multi-layered defense strategy that encompasses robust encryption, authentication, anomaly detection, and intrusion prevention mechanisms. The framework's first layer focuses on cryptographic techniques that safeguard V2X communication channels from unauthorized access and data manipulation. Advanced encryption algorithms, such as Elliptic Curve Cryptography (ECC) and Quantum Key Distribution (QKD), are examined to provide high levels of data confidentiality and integrity. The second layer involves authentication protocols designed to verify the legitimacy of communicating entities within the V2X network, preventing impersonation attacks and unauthorized access. Various public key infrastructure (PKI)-based methods…
subject: Articles
  topic: section
subject: Vehicle-to-Everything (V2X)
  topic: keywords
subject: connected vehicles
  topic: keywords
creator: Rajalakshmi Soundarapandiyan
creator: Priya Ranjan Parida
creator: Yeswanth Surampudi
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-07-18
publication date: 2023-07-18
starting page: 1
ending page: 41

Blockchain-Based Cybersecurity Solutions for Automotive Industry: Protecting Over-the-Air (OTA) Software Updates in Autonomous and Connected Vehicles
type: item
description: The rise of autonomous and connected vehicles (ACVs) has revolutionized the automotive industry, promising enhanced safety, efficiency, and convenience. However, the growing reliance on software for vehicle control and communication has introduced new cybersecurity vulnerabilities, particularly in Over-the-Air (OTA) software update mechanisms. These OTA updates, essential for maintaining and enhancing vehicle performance, are susceptible to various cyber threats, such as unauthorized modifications, data tampering, and malicious code injections. To address these challenges, this paper investigates the application of blockchain technology as a cybersecurity solution to protect OTA software updates in ACVs. Blockchain technology, known for its decentralized, secure, and immutable ledger, offers a promising approach to ensuring the integrity, authenticity, and transparency of OTA updates. The study begins by outlining the current cybersecurity challenges in the automotive industry, focusing on the vulnerabilities associated with OTA software updates. It highlights how traditional security mechanisms, such as cryptographic signatures and centralized certificate authorities, may not suffice against sophisticated cyber-attacks targeting connected vehicles. The paper then explores the unique properties of blockchain technology that make it suitable for addressing these challenges. By leveraging blockchain’s decentralized architecture, the risk of a single point of failure is mitigated, enhancing the robustness of OTA update processes. Additionally, the immutability and transparency of blockchain records ensure that any modification attempt is recorded and visible to all network participants, thereby preventing unauthorized changes. The core of this research is dedicated to examining blockchain-based frameworks and protocols specifically designed for OTA software update protection. Various blockchain models, such as public, private, and consortium blockchains, are evaluated …
subject: Articles
  topic: section
subject: Blockchain technology
  topic: keywords
subject: Over-the-Air (OTA) software updates
  topic: keywords
creator: Akila Selvaraj
creator: Praveen Sivathapandi
creator: Rajalakshmi Soundarapandiyan
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-09-17
publication date: 2023-09-17
starting page: 86
ending page: 134

Secure Multi-Party Computation for Privacy-Preserving Data Analytics in Cybersecurity
type: item
description: Secure Multi-Party Computation (SMPC) represents a paradigm-shifting approach to privacy-preserving data analytics, particularly in the context of cybersecurity. As the field of cybersecurity grapples with ever-increasing volumes of sensitive data and sophisticated adversarial threats, the need for robust, privacy-preserving analytical techniques becomes increasingly crucial. This paper delves into the application of SMPC techniques to achieve privacy-preserving data analytics, offering a comprehensive examination of its theoretical foundations, practical implementations, and performance considerations. SMPC is a cryptographic protocol designed to enable multiple parties to collaboratively compute a function over their private inputs without revealing those inputs to each other. This fundamental capability of SMPC is especially pertinent in cybersecurity, where data privacy and integrity are paramount. The paper begins with an overview of the core principles of SMPC, including secure function evaluation, oblivious transfer, and homomorphic encryption. By laying this groundwork, it provides a clear understanding of how these protocols facilitate secure computation in a multi-party environment. The paper then explores various applications of SMPC within the realm of cybersecurity. For instance, SMPC can be leveraged for secure threat intelligence sharing, where multiple organizations collaborate to analyze and mitigate threats without exposing their proprietary data. Similarly, it can enhance privacy in federated learning models, enabling collaborative machine learning across decentralized data sources while preserving the confidentiality of the data. Case studies are presented to illustrate how SMPC has been effectively employed in these scenarios, highlighting its advantages in maintaining data privacy and enabling joint analytical efforts. However, implementing SMPC in real-world cybersecurity applications is not without its challenges. One significant issue is the…
subject: Articles
  topic: section
subject: Secure Multi-Party Computation
  topic: keywords
subject: Privacy-Preserving Analytics
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Cryptographic Protocols
  topic: keywords
subject: Secure Function Evaluation
  topic: keywords
subject: Oblivious Transfer
  topic: keywords
subject: Homomorphic Encryption
  topic: keywords
subject: Threat Intelligence Sharing
  topic: keywords
creator: Venkata Sri Manoj Bonam
creator: Sai Manoj Yellepeddi
creator: Pranadeep Katari
creator: Chetan Sasidhar Ravi
creator: Ashok Kumar Pamidi Venkata
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2021-04-19
publication date: 2021-04-19
starting page: 20
ending page: 38

Cyber Resilience Frameworks: Exploring Cyber Resilience Frameworks and Strategies for Organizations to Recover From Cyber Attacks and Maintain Operational Continuity
type: item
description: Cyber attacks have become increasingly sophisticated, posing significant challenges to organizations' cybersecurity postures. In response, organizations are turning to cyber resilience frameworks to enhance their ability to withstand, respond to, and recover from cyber attacks. This paper provides a comprehensive review of existing cyber resilience frameworks, analyzing their key components, strategies, and implementation challenges. By exploring various frameworks such as NIST Cybersecurity Framework, ISO 27001, and others, this paper aims to offer insights into best practices for developing and implementing effective cyber resilience strategies. Additionally, the paper discusses the importance of organizational culture, leadership, and collaboration in building a resilient cybersecurity posture. Practical examples and case studies are used to illustrate the application of these frameworks in real-world scenarios. The paper concludes with recommendations for organizations seeking to enhance their cyber resilience and maintain operational continuity in the face of evolving cyber threats.
subject: Articles
  topic: section
subject: Cyber resilience
  topic: keywords
subject: Cybersecurity
  topic: keywords
creator: Dr. Isabella Li
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-07-25
publication date: 2024-07-25
starting page: 1
ending page: 15

Zero-Day Exploit Detection: Analyzing Machine Learning Approaches for Detecting Zero-Day Exploits and Previously Unseen Vulnerabilities to Enhance Proactive Threat Defense
type: item
description: Zero-day exploits pose a significant threat to cybersecurity by exploiting vulnerabilities that are unknown to the software vendor and, therefore, lack a patch. Detecting these exploits before they can be weaponized is critical for proactive threat defense. This paper reviews machine learning approaches for zero-day exploit detection, focusing on their effectiveness, efficiency, and applicability. Various algorithms and techniques are discussed, highlighting their strengths and limitations. The paper also explores the challenges and future directions in this field to enhance cybersecurity defense mechanisms.
subject: Articles
  topic: section
subject: Zero-day exploits
  topic: keywords
subject: Threat detection
  topic: keywords
creator: Prof. Santiago Cruz
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-01-15
publication date: 2024-01-15
starting page: 16
ending page: 28

Network Traffic Analysis: Studying Anomaly Detection Approaches for Network Traffic Analysis to Identify Suspicious Patterns and Behaviors Indicative of Cyber Threats
type: item
description: Network traffic analysis plays a crucial role in cybersecurity by detecting and mitigating threats to network infrastructure. Anomaly detection is a fundamental technique used in network traffic analysis to identify unusual patterns or behaviors that deviate from normal traffic. This paper provides an overview of anomaly detection approaches in network traffic analysis, focusing on their principles, methodologies, and applications. We discuss traditional methods such as statistical analysis and machine learning, as well as recent advancements including deep learning and ensemble techniques. The paper also highlights challenges and future research directions in the field of anomaly detection for network traffic analysis.
subject: Articles
  topic: section
subject: Anomaly Detection
  topic: keywords
subject: Statistical Analysis
  topic: keywords
creator: Prof. Viktor Petrov
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-07-25
publication date: 2024-07-25
starting page: 13
ending page: 24