Assessment cover STUDENTS, PLEASE COPY THIS PAGE AND USE AS THE COVER PAGE FOR YOUR SUBMISSION Module No: COMP7024 Module title: Operating Systems Security and Development Assessment title : Coursework Due date and time: Estimated total time to be spent on assignment: 35 hours per student LEARNING OUTCOMES On successful completion of this module, students will be able to achieve the module following learning outcomes (LOs): LO numbers and text copied and pasted from the module descriptor LO 1: Demonstrate a thorough understanding of the fundamentals of OS design, including process/thread, file, 10 and memory management. LO 2: Create system-level software that modifies and extends existing operating systems. Conduct experiments designed to evaluate the performance, security and reliability of their modifications and additions. LO 3: Critically evaluate the security, reliability and protection in a given OS configuration. Use the results of the evaluation to produce recommendations for hardening the system. LO 4: Demonstrate a thorough understanding of multi-threaded/process systems through the design and implementation of communicating, multi-threaded systems software. Engineering Council AHEP4 LOs assessed (from S1 2022-23) LOS copied and pasted from the AHEP4 matrix STUDENT NAMES (ONLY IF GROUP ASSIGNMENT, OTHERWISE ANONYMOUS) Student No: 1. Student Name: Group Name and Number: Statement of Compliance (please tick to sign) I declare that the work submitted is my own and that the work I submit is fully in accordance with the University regulations regarding assessments (www.brookes.ac.uk/uniregulations/current) COMP7024- Operating Systems Security and Development Coursework- Part 2: OS Security Improvement Semester 2- 2023-24 Part 2: Implementation, Testing, and Presenting the Results (50%) Learning outcome LO 1: Demonstrate a thorough understanding of the fundamentals of OS design, including process/thread, file, IO and memory management. LO 2: Create system-level software that modifies and extends existing operating systems. Conduct experiments designed to evaluate the performance, security and reliability of their modifications and additions. LO 3: Critically evaluate the security, reliability and protection in a given OS configuration. Use the results of the evaluation to produce recommendations for hardening the system. LO 4: Demonstrate a thorough understanding of multi-threaded/process systems through the design and implementation of communicating, multi-threaded systems software. Task Following coursework 1, in this part of the coursework you will 1. Implement the identified method (12.5% of the overall module mark), 2. Test and evaluate the identified method, that includes a description of the method or experiment for evaluating the result of the work (15% of the overall module mark), 3. Discuss the achieved result and show that the implementation could improve the gaps or areas of improvement (10% of the overall module mark), 4. Present a conclusion that summarise the work and includes some limitation in conducting the work and possible areas for future works (10% of the overall module mark), and 5. References (using Harvard or Numerical style of referencing) and proper citation (2.5% of the overall module mark). Deliverable, word limit, and deadline This exercise is worth 50% of the total marks for the module. Your report should be structured as listed in the task section; the mark for each section listed there. Submission Your report must be 1500 words (excluding references, tables, figures, and individual sesion). The reports longer than 20% of the word limit will be penalised; the extra words will not be marked. Marks and feedback will be available on Moodle 3 weeks after submission. This coursework is an individual piece of work. The University rules concerning plagiarism, syndication and cheating apply. Using Al tools is allowed but your report must be your own writing. Copying and pasting form Al tools affect your mark and can also be considered as plagiarism as it's someone elses's work. Version Control: You'll need to use a version control platform that records your report development history i.e., Google Doc or GitHub. Your report will include the link your to repository. If you use any other repository, you must justify this in your report Appendices. Reports without a valid version control history will not be acceptable. Please do Read the marking rubric to better understand what you need to include in your report and how you should do it. Weight Marking rubric Section 0 1 to <50 12.5% Implementation: Not Implement the presented identified method. 15% Validation: Test and evaluate the identified method, that includes 10% 10% a description of the method or experiment for evaluating the result of the work Discussion: Discuss the achieved result and show that the implementation could improve the gaps or areas of improvement Conclusion: Present a conclusion that Inadequately addressed: Poor quality content; incomplete /irrelevant. 50 to <60 Good: Good implementation, addressing most of the required features as explained in the first part of the coursework. Good: Good validation section with a clear test plan, covering most of the implemented features with a presentation of the results. Good: Good discussion section with some explanation of improvements achieved from conducting the work and presenting some analysis for the achieved results. Good: Good conclusion section that somehow summarises the conducted work, achieved Mark distribution 60 to <70 Very good: A very good implementation, addressing almost all the required features as explained in the first part of the coursework. Vary good: A very good validation section with a clear and concise test plan, covering almost all the implemented features with a clear presentation of the results. Very good: A very good discussion section with a clear explanation of improvements achieved from conducting the work and presenting some comparison of the results with some similar past work/system before applying the suggested changes. Very good: A very good conclusion section that clearly and almost fully summarises 70 to <100 Excellent: Excellent implementation, complete and concise following all the required features as explained in the first part of the coursework. Excellent: Excellent validation section with a clear and concise test plan, covering all the implemented features with a clear and complete presentation of the results. Excellent: Excellent discussion section with a clear and concise explanation of improvements achieved from conducting the work focusing on the results and comparing them with some similar past work/system before applying the suggested changes. Excellent: Excellent conclusion section that clearly, concisely, and fully summarises the conducted work, ' 100 Perfect: Complete, precise, clear, recent, well-structur ed, well-written, parsimonious and covers all listed in the previous column. No evidence of using Al in completing the work/report. 2.5% summarise the work and includes some limitation in conducting the work and possible areas for future works. References (using Harvard or Numerical style of referencing). result, the importance of the work, potential market and benefit, limitations faced, along with some potential improvement that can be made to complete the work as potential future work. Acceptable: An acceptable list of some related valid references provided, which some cited inside the report. the conducted work, achieved result, the importance of the work, potential market and benefit, limitations faced, along with some potential improvement that can be made to complete the work as potential future work. Very Good: A very good list of some related valid references is provided, which some cited inside the report. achieved result, the importance of the work, potential market and benefit, limitations faced, along with some potential improvement that can be made to complete the work as potential future work. Excellent: An excellent well-structured reference list containing valid resources/scholars, which all were cited inside the report./nAims and Objectives To develop an ML-based IoT authentication system that adapts the dynamic environmental conditions and user behaviors. To evaluate the system's performances in detecting anomalies and effectively effectively adjusting real-time authentication parameters. To test the effectiveness of the method in different IoT contexts. To carry out a comparative analysis between traditional techniques and the ML-based authentication system to identify advancements in dynamic adaptability. Potential Solution A method crucial for gaps in IoT authentication is a machine learning-driven adaptive authentication model (Al- Ghuwairi et al., 2023). It is a continuous learning process that analyzes device interactions, user's behaviours and user's environmental conditions to adjust authentication levels (Al- Naji & Zagrouba, 2020). Bharati & Podder (2022) suggests that the algorithms in the machines such as behaviour analysis and anomaly plays a crucial role in monitoring user behaviours to develop accurate profile and enhance the system's aptitude to adapt to evolving circumstances. Justification The method will be effective because it improves the effectiveness of authentication mechanisms in the face of emerging and changing threats and user patterns. Besides, it will be significant due to its ability to offer real-time insights into authentication contexts, enabling for proactive alterations (Ge et al., 2021). Therefore, the approach will provide a context-aware and resilient authentication solution.

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