I want to write a 5,500-word dissertation  on my Threat Management Archive for IoT Environments in Healthcare that i developed. The following structure is what i am using.
1. Abstract (300-400 words)
- Provide a concise overview of the objectives, methodology, features, and significance of the ArchiveIoT Threat Management System, focusing on its impact on enhancing IoT security in healthcare settings.

2. Table of Contents
- Automatically generated to reflect the document’s structure and pagination.

3. Chapter 1: Introduction (800-1,000 words)
- 1.1 Background and Context
- Discuss the increasing reliance on IoT devices in healthcare and the critical need for secure threat management.
- 1.2 Problem Statement
- Define the specific security challenges addressed by the ArchiveIoT System.
- 1.3 Aims and Objectives
- Outline the goals and specific objectives of the ArchiveIoT development project.
- 1.4 Novelty and Contribution
- Highlight the unique features and expected impacts of the ArchiveIoT System on healthcare security.
- 1.5 Dissertation Structure Overview
- Summarize the organization of the document.

4. Chapter 2: Literature Review (1,200-1,500 words)
- 2.1 Introduction
- Brief statement of the literature review’s purpose and scope.
- 2.2 Current State of IoT Security
- Analyze existing literature on IoT security measures within healthcare.
- 2.3 Role of Threat Management Systems
- Evaluate technologies and methodologies in current threat management solutions.
- 2.4 Challenges and Limitations
- Discuss prevalent challenges in securing IoT systems in healthcare settings.
- 2.5 Integration Frameworks
- Explore frameworks for implementing new threat management solutions.
- 2.6 Ethical Considerations
- Address ethical concerns related to data use and patient privacy.
- 2.7 Emerging Trends and Future Directions
- Identify future trends and potential research areas.
- 2.8 Conclusion of Literature Review
- Summarize key insights and highlight research gaps.

5. Chapter 3: Methodology (1,000-1,200 words)
- 3.1 Introduction
- Overview of the purpose and structure of the methodology section.
- 3.2 Development Process
- Detail the strategic approach and methods used to develop the ArchiveIoT system.
- 3.3 Data Collection and Sources
- Specify how data was gathered and utilized in system development.
- 3.4 System Design
- Discuss the architectural design and framework of the system.
- 3.4.1 Architectural Framework
- 3.4.2 User Requirements and Use Cases
- 3.5 Implementation
- Elaborate on the realization and construction of the system design.
- 3.6 Evaluation
- Outline strategies for testing and evaluating the system.
- 3.7 Ethical Considerations
- Include privacy and ethical considerations related to system deployment.
- 3.8 Conclusion of Methodology
- Summarize how the methodology supports reaching the project’s objectives.

6. Chapter 4: System Design and Implementation (1,200-1,400 words)
- 4.1 Introduction
- Explain the importance of robust system design for the ArchiveIoT System.
- 4.2 Architectural Framework
- Detail the structural components and layers of the system.
- 4.2.1 Three-Tier Architecture
- 4.2.2 API Integration
- 4.3 Security Features and Data Management
- Discuss integrated security measures and strategies for managing data.
- 4.4 User Interface Design
- Describe user interaction design principles for system usability.
- 4.5 Implementation Details
- Provide insights into the development process and key implementation phases.
- 4.6 Requirements
- Enumerate the system’s functional and non-functional requirements.
- 4.7 Conclusion of System Design
- Reflect on design choices and their implications on system effectiveness.

7. Chapter 5: Results and Analysis (800-1,000 words)
- 5.1 Introduction
- Summarize the purpose and scope of the results section.
- 5.2 System Performance
- Discuss performance metrics and outcomes derived from testing.
- 5.3 User Interaction and Usability
- Present usability testing results and user feedback.
- 5.4 Limitations and Future Improvements
- Acknowledge limitations and suggest potential enhancements.
- 5.5 Conclusion of Results
- Summarize the key findings and their significance.

8. Chapter 6: Discussion and Conclusion (600-800 words)
- 6.1 Discussion
- Interpret the significance of the results in the context of IoT security in healthcare.
- 6.2 Conclusion
- Recap the project’s accomplishments and contributions to healthcare IoT security.
- 6.3 Contributions to the Field
- Highlight the system’s impact and implications for future research and practice.
- 6.4 Future Work
- Suggest potential directions for future research and development of the ArchiveIoT system.

9. References
- Include a list of all scholarly and technical references cited throughout the document.

10. Appendix: Runbook
- Provide the detailed runbook as an appendix. It should include instructions for installation, configuration, usage, maintenance, and troubleshooting, as initially provided. This keeps the operational details separate from the main report content.


Below is my runbook.
ArchiveIoT Threat Management System Run Book
Introduction:
The ArchiveIoT Threat Management System is a web-based application designed to manage and archive security threats associated with IoT devices in a healthcare setting. This system helps monitor, detect, and respond to threats to ensure the safety and integrity of healthcare IoT devices and data.
This platform allows for users to engage and learn from threat report in the past.
Prerequisites:
Web server (XAMPP 8.*.*)
PHP (>= version 7)
MySQL database server
Web browser (Chrome, Firefox, etc.)
Installation:
1.Clone or download the ArchiveIoT repository from [repository URL].
2.Place the ArchiveIoT files in the root directory of your web server that has assignment/archiveiot or edit the PHP path variable in the files to match project root directory.
3.Ensure that the necessary permissions are set for files and directories, particularly for write access to folders where data will be stored (e.g., for logs, uploads).
4.Import the provided database schema (archiveiot_db.sql file) into your MySQL database.
5.Configure the database connection settings in the appropriate PHP files (server/config/MysqlDBH.php).

Configuration:
1.Open the server/config/MysqlDBH.php file and update the following settings as needed:
Database host
Database username
Database password
Database name as dbname

2.Review other configuration options available in the file and adjust them according to your requirements.

Starting the Application:
1.Ensure that your web server and MySQL database server are running.
2.Open a web browser and navigate to the URL where the archiveIoT application is hosted.
3.You should be greeted with the home page of the archiveIoT system.
4.Click at the Get Started to create an account and login with the created credentials to gain access to the user dashboard.

Usage:
1.Upon logging in, you will be presented with the dashboard, where you can navigate to different modules of the archiveIoT system.
2.Explore the various functionalities provided by the archiveIoT system, including:
Threat management
Threat search scheduling
Follower management
Likes Management
Reporting
In-App Notification System

Maintenance:
1.Regularly backup the archiveIoT database to prevent data loss.
2.Keep the archiveIoT application and its dependencies (PHP, MySQL, etc.) up to date with the latest security patches and updates.
3.Monitor server logs and application logs for any errors or issues and take appropriate action to resolve them.
4.Periodically review and optimize database queries and application code for better performance.


Troubleshooting:
1.If you encounter any errors or issues while using the archiveIoT system, refer to the application logs for error messages and stack traces.
2.Check the server logs for any relevant error messages that may indicate server-side issues.
3.Consult the documentation or seek assistance from the development team or online forums for troubleshooting assistance.

Conclusion:
The ArchiveIoT Threat Management System is now set up and ready to use. Follow this runbook for installation, configuration, and maintenance to effectively manage and archive IoT threats in your healthcare environment.


Below is the list of requirements
Functional Requirements
1.User Registration and Authentication
oThe system shall allow users to register with an email address and password.
oThe system shall support login functionality with email and password.
oThe system shall ensure that no duplicate of email in the creating of users account.
2.User Roles and Permissions
oThe system shall support multiple user roles, including Contributor (reporter), and Viewer.
oContributors shall have permissions to manage threats, submit solutions, and comment and view engagement on posts.
oViewers shall have permission to view threats, solutions, comment like and follow.
3.Threat Management
oThe system shall allow authenticated users to report new IoT threats through a submission form.
oThe system shall store each threat with a title, description, category, severity, date reported, and attachments.
oThe system shall allow users to view a list of reported threats and click on any threat to see detailed information and also engage each threat report by liking, following and commenting.
4.Solution Management
oThe system shall allow authenticated users to submit solutions to reported threats.
oEach solution shall be linked to its corresponding threat called mitigation steps
oThe system shall display solutions associated with each threat for users to view.
5.Commenting System
oThe system shall allow authenticated users to comment on threats and solutions.
oUsers shall be able to view all comments associated with a threat or solution in a threaded format.
6.Search and Filtering
oThe system shall provide a search functionality to allow users to find threats and solutions using keywords.
oThe system shall provide search options by category, severity, date reported, and other relevant criteria.
7.Administration and Moderation
oReporter shall have the ability to view, edit, or delete any of their content (threats, solutions) that violates community guidelines.
oViewer can be able to unlike, unfollow and delete their comment
8.Notifications
oThe system shall send in-app notifications to users for account-related actions, such as registration confirmation and password changes.
oThe system shall provide real-time in-app alerts to follows of a threat, when such threat is updated.

Non-Functional Requirements
1.Performance
oThe system shall handle up to 1,000 concurrent users without performance degradation.
oThe system shall return search results within 2 seconds for typical queries.
2.Scalability
oThe system shall be designed to scale horizontally to accommodate increasing numbers of users and data.
3.Security
oThe system shall encrypt all sensitive data, such as user passwords and personal information, using industry-standard encryption algorithms.
oThe system shall implement measures to prevent common security threats, such as SQL injection, cross-site scripting (XSS), and cross-site request forgery (CSRF).
4.Availability
oThe system shall have an uptime of 99.9% to ensure high availability for users.
oThe system shall implement automatic failover mechanisms in case of server failure.
5.Usability
oThe system shall have an intuitive and user-friendly interface that is easy to navigate for users of all technical levels.
oThe system shall be accessible through a web browser and optimized for use on various devices, including desktops, tablets, and smartphones.
6.Maintainability
oThe system codebase shall be well-documented to facilitate maintenance and future development.
oThe system shall be designed using modular architecture to allow for easy updates and feature enhancements.



Below is the list of academic literatures i want to use throughout this dissertation.
Aboukadri, S., Ouaddah, A. and Mezrioui, A. (2024) Machine Learning in Identity and Access Management Systems: Survey and Deep Dive. Computers & Security [online]. 139, p. 103729. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0167404824000300.
AboulEla, S., Ibrahim, N., Shehmir, S., Yadav, A. and Kashef, R. (2024) Navigating the Cyber Threat Landscape: An In-Depth Analysis of Attack Detection within IoT Ecosystems. AI [online]. 5 (2), pp. 704–732. Available from: https://www.mdpi.com/2673-2688/5/2/37.
Akinsanya, N.M.O., Ekechi, N.C.C. and Okeke, N.C.D. (2024) SECURITY PARADIGMS FOR IOT IN TELECOM NETWORKS: CONCEPTUAL CHALLENGES AND SOLUTION PATHWAYS. Engineering Science & Technology Journal [online]. 5 (4), pp. 1431–1451. Available from: https://www.fepbl.com/index.php/estj/article/view/1075.
Amoo, N.O.O., Osasona, N.F., Atadoga, N.A., Ayinla, N.B.S., Farayola, N.O.A. and Abrahams, N.T.O. (2024) Cybersecurity threats in the age of IoT: A review of protective measures. International Journal of Science and Research Archive [online]. 11 (1), pp. 1304–1310. Available from: https://ijsra.net/content/cybersecurity-threats-age-iot-review-protective-measures.
Boateng, F., Aroba, O.J. and Patel, S.S. (2024) Developing an IoT Adoption Framework for Library Management for Public Tertiary Institutions in Ghana. In: Advances in library and information science (ALIS) book series [online]. pp. 233–257.
Cassisi, C., Aliotta, M., Cannata, A., Pistagna, F., Prestifilippo, M., Torrisi, M. and Montalto, P. (2024) TSDSystem: a framework to collect, archive and share time series data at volcanological observatories. Bulletin of Volcanology [online]. 86 (8). Available from: https://doi.org/10.1007/s00445-024-01757-1.
Cîmpean, D.A., Vochin, M.-C., Crăciunescu, R.-E., Drăgulinescu, A.-M.-C. and Boicescu, L. (2024) Security Assessment of an Internet of Things Device. In: Lecture notes in networks and systems [online]. pp. 284–294.
Du, S., Liu, S., Xu, P. and Zhang, J. (2023) Engineering archive management model based on big data analysis and deep learning model. Applied Mathematics and Nonlinear Sciences [online]. 9 (1). Available from: https://doi.org/10.2478/amns.2023.1.00212.
Ghadge, N.N. (2024) Enhancing threat detection in Identity and Access Management (IAM) systems. International Journal of Science and Research Archive [online]. 11 (2), pp. 2050–2057. Available from: https://ijsra.net/content/enhancing-threat-detection-identity-and-access-management-iam-systems.
Guo, P., Xiao, K., Wang, X. and Li, D. (2024) Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things. Global Energy Interconnection [online]. 7 (1), pp. 94–105. Available from: https://www.sciencedirect.com/science/article/pii/S2096511724000094.
H. Duan, Y. Zheng, C. Wang and X. Yuan, "Treasure Collection on Foggy Islands: Building Secure Network Archives for Internet of Things," in IEEE Internet of Things Journal, vol. 6, no. 2, pp. 2637-2650, April 2019, doi: 10.1109/JIOT.2018.2872461.
Hasan, M.M., Ali, S., Mahmood, F.W., Hossain, S., Rahman, M.M. and Barid, M. (2024) Observations on big data concerning storage and security management. AIP Conference Proceedings [online]. Available from: https://pubs.aip.org/aip/acp/article-abstract/2898/1/030006/3262525/Observations-on-big-data-concerning-storage-and.
Hisseine, M.A., Chen, D., Xiao, Y. and Alimo, P.K. (2024) A review of digital object architecture and handle system: Development, current applications and prospective. Internet of Things [online]. 26, p. 101230. Available from: https://www.sciencedirect.com/science/article/abs/pii/S2542660524001719.
I. Makhdoom, M. Abolhasan, J. Lipman, R. P. Liu and W. Ni, "Anatomy of Threats to the Internet of Things," in IEEE Communications Surveys & Tutorials, vol. 21, no. 2, pp. 1636-1675, Secondquarter 2019, doi: 10.1109/COMST.2018.2874978.
K. Boisrond, P. M. Tardif and F. Jaafar, "Ensuring the Integrity, Confidentiality, and Availability of IoT Data in Industry 5.0: A Systematic Mapping Study," in IEEE Access, vol. 12, pp. 107017-107045, 2024, doi: 10.1109/ACCESS.2024.3434618.
keywords: {Cryptography;Indexes;Cloud computing;Edge computing;Internet of Things;Forensics;Internet of Things;edge computing;network security;information security},
keywords: {Protocols;Internet of Things;Privacy;Malware;Tutorials;Computer crime;Threats to the IoT;Internet of Things;malware attacks on the Internet of Things;attack methodology;security and privacy;IoT security framework;security guidelines},
keywords: {Security;DevOps;Fifth Industrial Revolution;Software;Industrial Internet of Things;Codes;Fourth Industrial Revolution;DevOps;secure DevOps;DevSecOps;industry 5.0;Industrial Internet of Things (IIoT);Internet of Things (IoT);continuous integration;continuous deployment;continuous delivery;integrity;confidentiality;availability;artificial intelligence;cyber security},
Lee, C., Kim, J., Ko, H. and Yoo, B. (2024) Addressing IoT storage constraints: A hybrid architecture for decentralized data storage and centralized management. Internet of Things [online]. 25, p. 101014. Available from: https://www.sciencedirect.com/science/article/pii/S2542660523003372.
Mahardika, D.P. (2024) Project design and implementation of electronic archiving pt multi garmenjaya. April 5, 2024 [online]. Available from: https://journal.ebizmark.id/index.php/rmsj/article/view/93.
Naderi, I., Bazargan, S. and Yarahmadi, H. (2024) No Title. [online]. 03 (02). Available from: https://doi.org/10.69557/ujrra.v3i2.79.
Nazir, A., He, J., Zhu, N., Wajahat, A., Ullah, F., Qureshi, S., Ma, X. and Pathan, M.S. (2024) Collaborative threat intelligence: Enhancing IoT security through blockchain and machine learning integration. Journal of King Saud University - Computer and Information Sciences [online]. 36 (2), p. 101939. Available from: https://www.sciencedirect.com/science/article/pii/S1319157824000284.
Noor, J., Ratul, R.H., Basher, Md.S., Soumik, J.A., Sadman, S., Rozario, N.J., Reaz, R., Chellappan, S. and Islam, A.B.M.A.A. (2024) Secure Processing-aware Media Storage and Archival (SPMSA). Future Generation Computer Systems [online]. 159, pp. 290–306. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0167739X24002279.
Okpor, N.M.D., Anazia, N.K.E. and Ukpenusiowho, N.D. (2024) A novel hybrid database security management technique. International Journal of Science and Research Archive [online]. 11 (2), pp. 1555–1565. Available from: https://ijsra.net/content/novel-hybrid-database-security-management-technique.
Ożadowicz, A. (2024) Generic IoT for Smart Buildings and Field-Level Automation—Challenges, Threats, Approaches, and Solutions. Computers [online]. 13 (2), p. 45. Available from: https://www.mdpi.com/2073-431X/13/2/45.
Saurabh, K., Sharma, V., Singh, U., Khondoker, R., Vyas, R. and Vyas, O.P. (2024) HMS-IDS: Threat Intelligence Integration for Zero-Day Exploits and Advanced Persistent Threats in IIoT. Arabian Journal for Science and Engineering [online]. Available from: https://doi.org/10.1007/s13369-024-08935-5.
Smail, B., Meenakshi, N., Arias‐Gonzáles, J.L., Jawarneh, M., Prasad, P.V.H. and Pallathadka, H. (2024) Conversational AI Threat Identification at Industrial Internet of Things. [online]. pp. 513–532. Available from: https://doi.org/10.1002/9781394200801.ch30.
Velmurugan, S., Shanthi, G., Raja, L. and Subitha, D. (2024) Threat Analysis and Security Measures for the Internet of Medical Things (IoMT): A Study. In: CRC Press eBooks [online]. pp. 229–242.
Zhang, Z., Zhou, N., Sun, B., Banerjee, S. and Mou, J. (2024) Multimedia healthcare cloud personal archives security system based on compressed sensing and multi-image encryption. Journal of the Franklin Institute [online]. 361 (8), p. 106844. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0016003224002655.

i Have written the following


Chapter 1: Introduction

#### 1.1 Background and Context

The rapid advancement of Internet of Things (IoT) technology has revolutionized various sectors, notably healthcare, by introducing innovative applications such as remote patient monitoring, smart medical devices, and real-time health data analytics (AboulEla et al., 2024). This transformation has enhanced patient care and operational efficiency but also introduced new security challenges. Healthcare environments now face an increased risk of cyber threats due to the proliferation of interconnected IoT devices susceptible to vulnerabilities (Cîmpean et al., 2024). Consequently, robust IoT threat management solutions are imperative to protect sensitive health information and ensure patient safety. The ArchiveIoT Threat Management System is designed to meet these security demands by offering a comprehensive platform for monitoring, detecting, and responding to threats in healthcare IoT ecosystems.

#### 1.2 Problem Statement

Despite the promising benefits of IoT in healthcare, the sector remains a prime target for cybercriminals seeking to exploit system vulnerabilities for malicious purposes (Akinsanya et al., 2024). Traditional security measures have proven inadequate for IoT environments due to their unique challenges, including vast device heterogeneity, limited device resources, and varied communication protocols (Duan et al., 2019). This complexity hinders the timely detection and mitigation of threats, posing significant risks to patient data integrity and system availability (Velmurugan et al., 2024). The ArchiveIoT System addresses these challenges by providing a specialized threat management solution tailored to the intricate dynamics of healthcare IoT networks.

#### 1.3 Aims and Objectives

The primary aim of the ArchiveIoT project is to develop a sophisticated threat management system that enhances security for IoT devices in healthcare settings. Key objectives include:

1. Designing a scalable and adaptable system architecture capable of integrating with diverse IoT devices.
2. Implementing advanced threat detection and mitigation capabilities using state-of-the-art security paradigms.
3. Ensuring compliance with healthcare data privacy standards, thereby safeguarding patient information.
4. Facilitating intuitive user interaction with the system through a user-friendly interface and automated alert mechanisms.

#### 1.4 Novelty and Contribution

The ArchiveIoT System introduces a novel approach to IoT threat management by integrating blockchain technology and machine learning algorithms to enhance security measures (Nazir et al., 2024; Ghadge, 2024). Unlike existing solutions, which often focus solely on threat detection, ArchiveIoT emphasizes threat archival and real-time collaboration for continuous security improvement (Duan et al., 2023; Hisseine et al., 2024). This system not only detects and responds to threats but also archives past threat reports to educate users and aid in preventing future incidents. By doing so, it bridges the gap between instantaneous threat management and strategic long-term security planning (Boisrond et al., 2024).

#### 1.5 Dissertation Structure Overview

This dissertation is organized into several chapters to systematically explore the development, implementation, and impact of the ArchiveIoT Threat Management System. Chapter 2 offers a comprehensive literature review, examining current IoT security measures and identifying gaps that the ArchiveIoT System aims to fill. Chapter 3 outlines the methodology used during the system’s development, detailing the design process, data collection, and evaluation strategies. Chapter 4 delves into the system design and implementation, providing insights into the architectural framework and security features. Chapter 5 presents the results and analysis, highlighting the system’s performance and user feedback. Finally, Chapter 6 discusses the implications of these findings for IoT security in healthcare and suggests future research directions. Each chapter builds upon the previous one, ensuring a coherent narrative that underscores the significance of the ArchiveIoT System in enhancing healthcare IoT security.

By addressing the pressing need for effective threat management in IoT ecosystems, the ArchiveIoT System represents a significant advancement in healthcare security, offering a robust framework to protect sensitive data and maintain patient trust in digital health innovations. Through this dissertation, we aim to contribute to the existing body of knowledge, providing valuable insights for researchers and practitioners in the field of IoT security.


Chapter 2: Literature Review

#### 2.1 Introduction

The integration of Internet of Things (IoT) technologies in healthcare has ushered in a new era of medical innovation, facilitating advanced patient monitoring, data-driven healthcare decisions, and enriched patient experiences (Lee et al., 2024). Yet, this technological evolution is not devoid of challenges. The increasing deployment of these devices elevates the risk of cybersecurity threats, which necessitates robust and effective threat management systems (Amoo et al., 2024). This literature review aims to critically examine the current state of IoT security in healthcare, the role and limitations of existing threat management systems, integration frameworks, ethical considerations, and emerging trends, all converging to underline the necessity and potential benefits of the ArchiveIoT Threat Management System.

#### 2.2 Current State of IoT Security in Healthcare

The healthcare sector’s adoption of IoT has outpaced the development of security measures capable of defending the intricate web of connected devices (Velmurugan et al., 2024). Current security frameworks often fall short due to the unique and complex nature of IoT infrastructure in healthcare (Cîmpean et al., 2024). These systems are typically designed for general IT environments and neglect the specific requirements and challenges of healthcare IoT, including the management of sensitive patient data and the need for uninterrupted device functionality (Akinsanya et al., 2024). The literature thus highlights a critical need for tailored security solutions that can address these domain-specific challenges effectively.

#### 2.3 Role of Threat Management Systems

Threat management systems play a crucial role in defending against IoT vulnerabilities by providing mechanisms for threat detection, analysis, and response (Nezi et al., 2024). Advances in AI and machine learning have considerably enhanced these systems’ capabilities, allowing for more efficient anomaly detection and automated response strategies (AboulEla et al., 2024). However, despite these advancements, the dynamic and evolving nature of threats, particularly in IoT-rich environments, poses significant challenges. Duan et al. (2019) argue that many existing systems lack the adaptability required to address real-time threats effectively, pointing to a gap the ArchiveIoT System seeks to bridge by integrating sophisticated, adaptive threat management strategies.

#### 2.4 Challenges and Limitations

IoT systems in healthcare face distinct challenges, including device heterogeneity, resource constraints, and varying communication protocols, which complicate the deployment of effective security measures (Hisseine et al., 2024). The limited processing power and memory capacity of many IoT devices restrict the implementation of complex security algorithms (Ożadowicz, 2024). Additionally, the vast and varied landscape of IoT devices results in interoperability issues, complicating comprehensive threat management (Ghadge, 2024). These constraints necessitate the development of lightweight, scalable security solutions that can adapt to the unique demands of healthcare IoT environments.

#### 2.5 Integration Frameworks for Threat Management

Effective threat management relies heavily on integrating security measures across diverse and complex IoT networks. Boisrond et al. (2024) advocate for the use of modular architectures that can seamlessly incorporate various security protocols. Modular designs provide the flexibility required for integrating both existing and emerging technologies (Nazir et al., 2024). Blockchain technology has emerged as a promising solution, offering enhanced data integrity and transparency, which are critical to secure IoT ecosystems (AboulEla et al., 2024). By leveraging such integration frameworks, the ArchiveIoT System can provide a robust mechanism for real-time threat management and mitigation.

#### 2.6 Ethical Considerations

With the proliferation of IoT devices in healthcare, ethical considerations related to data privacy, consent, and patient autonomy have gained prominence (Cassisi et al., 2024). Healthcare IoT systems must comply with stringent data protection regulations, such as the GDPR, to maintain patient trust and confidentiality (I. Makhdoom et al., 2019). Ensuring privacy while promoting technological innovation presents a significant ethical dilemma. The ArchiveIoT Threat Management System is designed to align with these ethical guidelines, ensuring that patient data is securely managed and processed, thus achieving a balance between technological advancement and ethical responsibility.

#### 2.7 Emerging Trends and Future Directions

Emerging trends in IoT security emphasize the importance of collaborative threat intelligence and the integration of advanced AI-driven analytics (Nazir et al., 2024; Saurabh et al., 2024). Collaborative platforms that enable the sharing of threat information across institutions enhance the overall efficacy of threat management systems by broadening the understanding and anticipation of cyber threats (Mahardika, 2024). Additionally, AI and machine learning are playing increasingly critical roles in predicting and neutralizing sophisticated attacks, suggesting a shift towards more proactive and resilient security frameworks (Nazareth et al., 2024). These advancements align with the objectives of the ArchiveIoT System, which aims to integrate such technologies for more effective threat management.

#### 2.8 Conclusion of Literature Review

The review underscores significant gaps in existing IoT security frameworks within healthcare, particularly their inability to adapt to the specific demands of IoT environments. Current measures often fall short in providing comprehensive, real-time threat detection and mitigation, highlighting a critical need for more sophisticated and adaptable systems (Duan et al., 2019). The ArchiveIoT System is designed to address these limitations by incorporating blockchain and machine learning innovations, aiming to enhance the security of healthcare IoT infrastructures comprehensively. Beyond technological improvements, ethical considerations remain paramount, with the system ensuring compliance with data protection standards to maintain trust and security in IoT applications.

The insights derived from this literature review will guide the continued development and refinement of the ArchiveIoT System, ensuring it meets the complex challenges inherent in healthcare IoT environments. By leveraging advanced technologies and prioritizing ethical considerations, the ArchiveIoT System aspires to set a new benchmark for IoT security, addressing existing vulnerabilities and ensuring the safe, efficient, and ethical deployment of IoT technologies in healthcare.


Chapter 3: Methodology

#### 3.1 Introduction

The methodology chapter outlines the strategic approach and processes involved in the development of the ArchiveIoT Threat Management System. This section provides a detailed account of the development process, data collection methods, system design architecture, implementation steps, and evaluation strategies. By adhering to a systematic approach, the ArchiveIoT System aims to address the unique security challenges of IoT environments in healthcare settings, ensuring robustness, scalability, and ethical compliance.

#### 3.2 Development Process

The development of the ArchiveIoT System was guided by an iterative and agile methodology, allowing for flexibility and continuous improvement throughout the project lifecycle. This approach facilitated the rapid incorporation of feedback and iterative refinement of system features (Lee et al., 2024). Initially, a comprehensive needs analysis was conducted to identify critical security challenges and user requirements specific to IoT environments in healthcare. The development process was organized into distinct phases: planning, design, development, testing, and deployment. Each phase was interlinked, ensuring a cohesive transition and alignment with project objectives.

#### 3.3 Data Collection and Sources

The data collection process was integral to the ArchiveIoT System’s development, serving to inform system design and functionality. Data was gathered from several sources, including existing IoT threat management literature, healthcare IoT security guidelines, and expert interviews (Nazir et al., 2024). Additionally, real-world threat data, anonymized to protect patient privacy, was collected from collaborations with healthcare institutions. This data was vital in crafting realistic threat scenarios and validating the system’s threat detection and response capabilities. The collected information helped define user requirements and informed the architectural design of the system.

#### 3.4 System Design

The ArchiveIoT System’s design focused on creating a scalable and secure architecture that addressed the dynamic nature of IoT environments in healthcare.

##### 3.4.1 Architectural Framework

The system was developed using a three-tier architecture, comprising presentation, application, and database layers. This architectural model facilitated separation of concerns, enabling independent scalability and enhanced security (Ożadowicz, 2024). The use of APIs allowed for seamless integration with existing healthcare IoT devices and systems, promoting interoperability (Ghadge, 2024).

##### 3.4.2 User Requirements and Use Cases

The design process was heavily influenced by user-centered design principles, ensuring the system met the specific needs of healthcare professionals and IT security experts. Use cases were developed in collaboration with these stakeholders to simulate real-world interactions with the system, guiding feature development and user interface design (Boisrond et al., 2024).

#### 3.5 Implementation

The implementation of the ArchiveIoT System involved translating design specifications into a functional application. The development environment utilized modern programming frameworks and languages, with a focus on security and performance optimization (Akinsanya et al., 2024). Key implementation steps included the development of secure APIs for data exchange, integration of machine learning models for threat detection, and implementation of blockchain technology to ensure data integrity (Nazareth et al., 2024). The system was rigorously tested throughout the implementation phase, with unit tests and integration tests employed to ensure reliability and performance.

#### 3.6 Evaluation

Evaluating the ArchiveIoT System was crucial to ensuring that it met the predefined objectives and delivered the expected security enhancements in healthcare IoT environments.

The evaluation consisted of usability testing, performance benchmarking, and security assessments. Usability testing involved real end-users interacting with the system to identify any user interface challenges and to gather feedback on system intuitiveness and accessibility (Velmurugan et al., 2024). Performance benchmarking assessed the system’s ability to handle the specified load of concurrent users and its response time to typical queries (Duan et al., 2019). Security assessments were conducted through simulated cyber-attacks and penetration testing, evaluating the system’s robustness and resilience (Hisseine et al., 2024).

#### 3.7 Ethical Considerations

In developing the ArchiveIoT System, ethical considerations were paramount, particularly concerning data privacy and user consent. The system was designed to comply with GDPR and other relevant data protection regulations, ensuring the safeguarding of sensitive health information (Cassisi et al., 2024). An ethics review board evaluated all aspects of data collection and system deployment to ensure adherence to high ethical standards and maintain patient confidentiality (Nazir et al., 2024).

#### 3.8 Conclusion of Methodology

The methodology used in the development of the ArchiveIoT System reflects a comprehensive and adaptive approach to addressing the unique security needs of healthcare IoT environments. The iterative process facilitated continuous improvement and incorporation of stakeholder feedback, ensuring the system is both user-friendly and effective in threat management (AboulEla et al., 2024). By focusing on robust system design, ethical compliance, and rigorous testing, the methodology laid a strong foundation for achieving the project’s objectives and enhancing IoT security in healthcare. As a result, the ArchiveIoT System is poised to significantly improve the management of IoT threats, offering a scalable, secure, and ethically sound solution for safeguarding healthcare environments. This strategic development approach ensures that the system not only meets current security challenges but is also adaptable to emerging threats, cementing its relevance and utility in the evolving landscape of healthcare IoT.

Put everything i mentioned above into consideration throughout this conversation. 

Now, for this dissertation, in the best way possible, generate the System Design and Implementation Section in 1000 words

Ensure to follow the following writing tips:
- Consistency and Flow: Ensure smooth transitions between sections to maintain the coherence of the narrative.
- Critical Evaluation: Always aim to critically evaluate.
- Use of References: Use correct in-text citations.
- Clarity and Precision: Keep your writing clear and focused, avoiding unnecessary jargon and complex sentences.