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ASSIGNMENT 1 FRONT SHEET
Qualification TEC Level 5 HND Diploma in Computing Unit number and title Unit 06: Planning a computing project Submission date October 29 , 2023 Date Received 1st submission Re-submission Date Date Received 2nd submission Student Name Nguyen Minh Anh Student ID BH Class SE06203 Assessor name Nguyen Van Toan Student declaration I certify that the assignment submission is entirely my own work and I fully understand the consequences of plagiarism. I understand that making a false declaration is a form of malpractice. Student’s signature Anh Grading grid P1 P2 P3 P4 M1 M2 D
❒ Summative Feedback: ❒ Resubmission Feedback:
Grade: Assessor Signature: Date: Signature & Date:
- A. Introduction
- B. Content
- 1.1 Project Management Plan
- 1.2 Project Charter
- 1.3 Project Objectives
- 1.4 Project Scope
- 1.4.1 Research methods
- 1.4.1.1 What is quantitative/ Qualitative research?
- 1.4.1.2 What is Cyber security risks
- 1.4.1.3 What is Big Data
- 1.4.1.4 What is cloud-based Big Data storage systems...........................................................................................................................................
- 1.4.1.5 Quantitative Analysis of Cyber security risks involved in local and/or cloud-based Big Data storage systems.
- 1.4.1.6 Qualitative Analysis of Cyber security risks involved in local and/or cloud-based Big Data storage systems.
- 1.4.1.7 Methodology for Combining Qualitative and Quantitative Research to Investigate a Specific Theme
- 1.4.2 Primary Reseach
- 1.4.2.1 Define Primary Reseach
- 1.4.2.2 Primary Research on the Evolution of these risks over time and future risks
- 1.4.3 Secondary reseach
- 1.4.3.1 Define Secondary Reseach
- 1.4.3.2 Exploring the Challenges and Opportunities of Cyber security risks involved in local and/or cloud-based Big Data storage systems
- 1.4.4 Comprehensive Report................................................................................................................................................................................
- 1.4.5 Evaluation of Project Management Process and Research Methodologies
- 1.4.6 Project Logbook...........................................................................................................................................................................................
- 1.4.7 Project Team
- and/or cloud-based Big Data storage systems from Cyber security risks 1.4.8 Features of a data centre business and cloud providers(Google - GCP, Amazon - AWS, Microsoft - Azure) to prevent and protect local
- protect local and/or cloud-based Big Data storage systems from Cyber security risks 1.4.9 Operational areas of a data centre business and cloud providers(Google - GCP, Amazon - AWS, Microsoft - Azure ) to prevent &
- 1.4.10 What are Stakeholders
- 1.4.11 Role of stakeholders in Cyber security risks involved in local and/or cloud-based Big Data storage systems...........................................
- 1.4.12 How stakeholders impact the business in Cyber security risks involved in local and/or cloud-based Big Data storage systems
- C. Conclusion
- D. Reference
- Figure 1 Gantt Chart Table of Figure
- Figure 2 Quantitative research
- Figure 3 Quantitative research methods.......................................................................................................................................................................
- Figure 4 Big Data
- Figure 5 3V of Big Data
- Figure 6 Cloud-based big data
- Figure 7 The most cyber secure countries in the world
- Figure 8 Quantitative analysis of cyber security risks
- Figure 9 Primary research..............................................................................................................................................................................................
- Figure 10 Project Diary
- Figure 11 Cloud Computing
- Figure 12 Google Cloud
- Figure 13 Amazon Web Services
- Figure 14 Azure
- Figure 15 Stakeholders
- Figure 16 Roles of Stakeholders
- Figure 17 Cyber security
A. Introduction
As a member of the Research and Development department, I was assigned a small project to conduct research on this topic. The report's primary research includes both qualitative and quantitative research. Qualitative research will help me better understand the unmeasured elements of the environmental impact of digital transformation. This includes gathering information, observing and analyzing factors such as changes in the natural environment and effects on resources. Quantitative research, meanwhile, will help me measure and evaluate measurable environmental factors, such as emissions, energy consumption and the amount of waste produced during the conversion process. change number. By using data analysis methods and tools, I will be able to collect data and come up with concrete numbers on the environmental impact of digital transformation. Secondary research will provide me with a base of information and related studies that have been previously done on the topic. I will explore studies, reports and documents to review the results, analyze and assess the environmental impact of digital transformation identified. This helps me have a more comprehensive view of the problem and provide more in-depth analysis in my report. After completing primary research and secondary research, I will proceed to write a report for my research. The report will include a description of the research problem, objectives, research methods, results and conclusions. I will try to present information clearly, logically, and based on the data and results I have collected. Big Data storage systems offer serious cybersecurity threats, jeopardizing essential data security and harming companies and individuals. Personal data misuse and complicated network assaults are on the rise, demonstrating the complexities of cybersecurity threats. Big Data storage and processing are becoming increasingly important for business performance as information technology progresses.This paper examines changes in attack methodologies and exploited vulnerabilities as they relate to the growth of network security concerns over time. It also investigates future hazards as a result of technological advancement, emphasizing the importance of protecting key data and systems from potential threats.This report aims to provide readers with an overview of the importance of cybersecurity and the significance of improving security capabilities in managing and storing Big Data by evaluating the evolution of cybersecurity risks associated with Big Data storage systems and providing insights intofuture prospects.
B. Content
Figure 1 Gantt Chart 1.2 Project Charter In this section, we will clarify the purposes of the project, the scope, and the charter to help teams quickly understand the objectives, tasks, processes, and stakeholders.
- Purpose: The project's goal is to investigate and assess the cybersecurity threats associated with local and/or cloud- based Big Data storage systems. Create plans and ideas to mitigate identified hazards. Improve the security of enterprises that use Big Data storage solutions.
- Range:
- The research will analyze cybersecurity vulnerabilities connected with Big Data storage technologies in particular.
- Both on-premises and cloud storage will be evaluated.
- Risk will be assessed from three perspectives: technical, operational, and organizational.
- The project will offer recommendations and best practices for reducing the identified hazards in the Big Data storage infrastructure.
- Principles and regulations:
- Compliance with applicable cybersecurity, data protection, and privacy norms and regulations.
- Information and data gathered and used in the study process are secure.
- Ensure the confidentiality and security of the project's systems, tools, and resources.
- Only share information and study findings with those who have access to it.
- Moral principles:
- Respect for research ethics and professional integrity.
- Maintain transparency and integrity in data gathering, analysis, and reporting outcomes. • Protect people' and organizations' privacy and sensitive information.
- Do not harm or violate any party's system, resource, or data.
- Project Manager: The project's manager is Nguyen Minh Anh will manage and supervise the project. Risk management, planning, and resource coordination must all be implemented. Manage project progress, report on progress, and assess project effectiveness. Ensure that project stakeholders collaborate and communicate effectively.
- Distribution and responsibility:
- Because the project has six people, it is vital to identify the roles, duties, and work assignments for the project participants.
- Ascertain that members have the necessary skills and resources. Encourage collaboration and teamwork among project participants.
- Change Manager: Changes in project needs must be evaluated and managed. Make certain that any modifications are evaluated and authorized in accordance with the change management procedure. Examine the impact of changes on project plans, resources, and timelines.
- Quality management:
- Ensure the project's quality via testing, assessment, and analysis.
- Ensure that the project's procedures, methodologies, and standards are followed at all times.
- Establish goals and quality standards for products and project outcomes.
- Ensure feedback and improvement based on stakeholder feedback and test findings.
- Conflict Resolution:
- Managing and resolving issues throughout the project process.
- Identify and put in place ways to settle disagreements in a fair and effective manner.
- Ensure that dispute resolution does not impede the project's development and effectiveness.
- Evaluation and completion of the project:
- Conduct a final project evaluation to confirm that the objectives are satisfied.
- Organize presentations and convey project achievements to stakeholders.
- Identify and document the project's lessons learnt for future efforts.
- Compliance:
- Compliance with cybersecurity, privacy, and data protection regulations, rules, policies, and procedures.
- Respect for research ethics and professional integrity.
- Compliance with legal and privacy standards governing information collection, usage, and security.
These project objectives provide a platform for organizations to effectively manage and mitigate cybersecurity risks in large local and/or cloud-based data storage systems. It is important to tailor these targets to specific needs and priorities to ensure a comprehensive and effective cybersecurity strategy. 1.4 Project Scope The project scope includes the following:
- Security Risk and Vulnerability Assessment: ❖ Conduct security audits of local and cloud data storage systems, analyze possible risks and identify cybersecurity vulnerabilities. ❖ Determine the sensitivity of your data and identify areas at highest risk.
- Determine Security Strategy: ❖ Classify data according to sensitivity level and determine appropriate security measures for each type of data. ❖ Develop a detailed plan for implementing security measures, including software updates, data encryption, and access management.
- Implement Security Measures: ❖ Deploy and configure new security solutions to prevent and prevent cyber attacks. ❖ Build a network security monitoring system to detect potential threats early and handle them.
- Training and Awareness: ❖ Organize periodic training and coaching sessions to increase cybersecurity awareness and create a culture of information security. ❖ Provide guidance and information to employees on how to prevent and respond to cyber security threats.
- Develop an Emergency and Recovery Plan: ❖ Develop a quick response and recovery plan after a cyber security incident to minimize damage when an incident occurs. ❖ Create and organize post-incident network recovery plan drills and tests. The project "Addressing Cyber Security Risks in Data Storage Systems" aims to enhance the security of data storage systems, protect organizational data and meet security requirements. network for increasingly complex digital environments. We hope that the project will ensure that organizations can manage and operate their data storage systems securely and efficiently. P1: Demonstrate qualitative and quantitative research methods to generate relevant primary data for an identified theme. 1.4.1 Research methods
- Determine Objective and Scope:
- Clearly define the specific goals of the project, including specific cybersecurity issues that the project wants to solve.
- Determine the scope of the project, including what will actually be researched and implemented within the scope of the project.
- Data Collection:
- Identify sources of information and data needed to assess cybersecurity risks in data storage systems. This may include internal documents, security reports, as well as information from data storage providers or external sources that may provide information about security risks. popular network.
- Risk Analysis:
- Use methods such as SWOT analysis (Strengths, Weaknesses, Opportunities, and Threats) or Risk and Control Analysis to understand potential cybersecurity risks in storage systems store data
- Identifying Security Measures:
- Based on the results of the risk analysis process, identify specific and effective security measures to minimize risks. These measures may include software updates, enhanced access controls and data encryption.
- Implementation and Evaluation:
- Implement selected security measures and monitor their effectiveness by evaluating the improvement of network security and the ability to respond to identified risks.
- Training and Awareness:
- Train employees on new security measures and cybersecurity processes, as well as raise awareness of the impact of effective security on the organization.
- Prepare a Prevention and Response Plan:
- Develop a plan to prevent and respond to cyber security incidents, including potential risks and necessary measures to respond when incidents occur.
- Compliance and Legal:
- Ensure that all security measures comply with cybersecurity and data protection legislation, and adopt appropriate steps to ensure compliance.
- Measure and Evaluate Results:
Quantitative research is a method of collecting and analyzing numerical data, enabling the identification of patterns, making predictions, testing causal relationships, and generalizing results to broader populations, unlike qualitative research which focuses on non-numerical data. Quantitative research methods are used for descriptive, correlational, and experimental studies. Descriptive research summarizes study variables, correlational research investigates relationships, and experimental research systematically examines cause-and-effect relationships. These methods can test hypotheses using statistics and generalize results to broader populations (Bhandari, 2020). Quantitative research methods: Figure 3 Quantitative research methods Quantitative research methods are systematic approaches to collecting and analyzing data that are primarily focused on producing numerical or quantitative data. These methods are commonly used in various fields, including social sciences, natural sciences, business, and health sciences. Quantitative research aims to provide a clear understanding of patterns, relationships, and cause-and-effect associations within a given dataset. Here are some common quantitative research methods:
1. Surveys and Questionnaires: Surveys involve collecting data through structured questions asked to a sample of respondents. Questionnaires are a common tool for surveys. The responses are often in the form of closedended questions with predetermined answer choices, which can be analyzed quantitatively. 2. Experiments: Experiments involve manipulating one or more variables to study their effects on one or more dependent variables. Quantitative data is collected to assess the impact of the manipulated variables. Experimental designs often use control groups for comparison.
3. Observational Research: This method involves systematic observation and recording of behaviors or phenomena in a natural or controlled setting. Data can be collected using checklists, coding schemes, or numerical counts. 4. Content Analysis: Content analysis is used to quantitatively analyze the content of various forms of media, such as texts, audio, or video. Researchers count and categorize elements within the content to identify patterns and trends. 5. Secondary Data Analysis: Researchers can analyze existing datasets that were collected for different purposes. This approach is cost-effective and can provide valuable insights into various research questions. 6. Regression Analysis: Regression analysis is used to understand the relationship between one or more independent variables and a dependent variable. It helps in predicting and explaining the changes in the dependent variable based on changes in the independent variables. 7. Descriptive Statistics: Descriptive statistics, including measures like mean, median, mode, standard deviation, and range, are used to summarize and describe the characteristics of a dataset. 8. Hypothesis Testing: Statistical tests, such as t-tests, chi-square tests, and ANOVA, are used to determine if there are statistically significant differences or relationships in the data. These tests help researchers accept or reject hypotheses. 9. Sampling Techniques: Choosing the right sample is crucial in quantitative research. Techniques like random sampling, stratified sampling, and cluster sampling help ensure that the sample accurately represents the population being studied. 10. Longitudinal Studies: These involve collecting data from the same subjects over an extended period to study changes and trends over time. Quantitative research methods provide researchers with a structured and replicable way to investigate and analyze data. They are valuable in making evidence-based decisions and drawing generalizable conclusions about a population or phenomenon. However, they have limitations, including a potential lack of depth in understanding the "why" behind the data, and the need for careful design to avoid bias and errors in data collection and analysis. Descriptive statistics provide an overview of your data and contain averages and variability measurements. Graphs, scatter plots, and frequency tables can also be used to display your data and look for trends or outliers. Based on your data, you may use inferential statistics to draw predictions or generalizations. You may either test your hypothesis or estimate the population parameter using your sample data. You may also evaluate the dependability and validity of your data collecting techniques to see how regularly and accurately your procedures measured what you wanted them to measure.
b. Qualitative research Qualitative research is a research methodology that focuses on exploring and understanding the complexity of human experiences, behaviors, and social phenomena. Unlike quantitative research, which primarily deals with numerical data, qualitative research relies on non-numerical data such as words, images, and narratives. Qualitative research is used in various fields, including social sciences, anthropology, psychology, education, and healthcare. Here are key features and methods associated with qualitative research:
- Data Collection Methods:
- Interviews: Qualitative researchers conduct in-depth interviews with participants to gather their perspectives, experiences, and stories. These interviews can be structured or unstructured.
- Focus Groups: Researchers bring together a group of participants to discuss a specific topic, and then analyze the group dynamics, interactions, and discussions.
- Observation: Researchers observe and record behavior and interactions in a natural setting. This can be participant observation (where the researcher is part of the group) or non-participant observation (where the researcher remains an observer).
- Document Analysis: Researchers analyze various documents, texts, or artifacts, such as diaries, letters, newspapers, or online content.
- Visual and Audio Data: Qualitative researchers may also use visual or audio data, such as photographs, videos, or audio recordings, as part of their analysis.
- Data Analysis:
- Thematic Analysis: Researchers identify and analyze recurring themes, patterns, and meanings in the data. They may code the data to group similar responses or narratives together.
- Content Analysis: Researchers examine the content of texts, documents, or media to identify specific patterns and meanings within the data.
- Grounded Theory: Researchers use grounded theory to develop theories and concepts that emerge from the data, rather than testing preconceived hypotheses.
- Narrative Analysis: Researchers focus on the structure and content of narratives to understand how individuals construct and share their stories.
- Flexibility and Contextual Understanding:
- Qualitative research is characterized by its flexibility, allowing researchers to adapt their methods and questions as they delve deeper into the study.
- The emphasis is on understanding the context and exploring the subjective experiences of participants, which may lead to rich and nuanced findings.
- Sampling:
- Qualitative research often uses purposive or non-probability sampling methods, where participants are selected based on specific criteria related to the research focus. The goal is to obtain diverse and information-rich cases.
- Researcher Subjectivity:
- Qualitative researchers acknowledge the role of their own subjectivity and bias. Reflexivity, or selfawareness, is important in qualitative research to minimize the impact of the researcher's perspective on the findings.
- Results and Reporting:
- Qualitative research typically results in detailed, narrative descriptions and findings. Researchers may use quotes and examples from participants to illustrate key themes and insights.
- Reports or articles often include thick descriptions to provide context and a deeper understanding of the research. Qualitative research is valuable for exploring complex phenomena, gaining insights into the perspectives of individuals, and generating rich, context-specific knowledge. It is particularly useful when the research questions are open-ended and when quantitative data alone may not capture the depth and nuances of human experiences and social phenomena. It is important to note that qualitative research is susceptible to study biases such as the Hawthorne effect, observer prejudice, recollection bias, and social desirability bias. While not always completely preventable, being conscious of potential biases as you gather and evaluate data can help to keep them from negatively effecting your work. Each of the research approaches involve using one or more data collection methods. These are some of the most common qualitative methods:
- Observations taking thorough field notes on what you see, hear, or encounter.
- Interviews are one-on-one interactions in which you individually ask them questions.
- Focus groups a group of people who are asked questions and have a conversation.
- Surveys include the distribution of questionnaires containing open-ended questions.
- Secondary research entails gathering previously collected material in the form of texts, photos, audio or video recordings, and so on (Bhandari, 2023). Because all observations, interpretations, and analyses are mediated via their own personal lens, qualitative researchers frequently regard themselves as "instruments" in research. As a result, while writing up your methodology for qualitative research, it's critical to reflect on your approach and clearly explain the decisions you took in data collection and analysis (Bhandari, 2023). Texts, images, videos, and audio may all be used to collect qualitative data. You may be working with interview transcripts, survey results, fieldnotes, or recordings from natural settings, for example:
- Prepare and arrange your information This might include interview transcription or putting up fieldnotes.
- Examine and investigate your data Examine the data for patterns or recurring concepts.
- Create a data coding system Create a collection of codes that you may use to categorize your data based on your first thoughts.
- Data should be coded In qualitative survey analysis, for example, this may entail going over each participant's replies and
the research's external validity. As a result, when extending these findings to larger situations, care should be used.
- Labor-intensive Although software may be used to organize and record enormous quantities of text, data analysis is frequently done manually. This is due to the fact that some subtleties and context in the data may necessitate human judgment and interpretation. Furthermore, manual examination enables for the detection of trends or abnormalities that automated algorithms may miss (Bhandari, 2023) 1.4.1.2 What is Cyber security risks Cybersecurity risks refer to the potential threats and vulnerabilities that can lead to the exposure, loss, or compromise of critical assets, sensitive information, or the reputation of an organization. These risks arise from various cyber attacks or breaches within an organization's network. Here are some key points about cybersecurity risks:
- Types of Cybersecurity Risks: There are various types of cybersecurity risks that organizations need to be aware of, including: ➢ Malware: Malicious software designed to disrupt, damage, or gain unauthorized access to computer systems. ➢ Phishing Attacks: Attempts to trick individuals into revealing sensitive information, such as passwords or credit card details, by posing as a trustworthy entity. ➢ Ransomware: Malware that encrypts data and demands a ransom for its release.
➢ Insider Threats: Risks posed by employees or individuals with authorized access to an organization's systems who misuse or exploit that access. ➢ Data Breaches: Unauthorized access or disclosure of sensitive data, often resulting in financial and reputational damage. ➢ Social Engineering Attacks: Manipulation of individuals to gain access to confidential information or systems. ➢ Poor Compliance Management: Failure to adhere to regulatory requirements and industry best practices, leading to vulnerabilities and potential breaches.
- Impact of Cybersecurity Risks: Cybersecurity risks can have significant consequences for organizations, including: ➢ Financial Loss: Costs associated with remediation, legal actions, and potential loss of business. ➢ Disruption of Operations: Downtime, loss of productivity, and interruption of critical services. ➢ Reputational Damage: Loss of customer trust, negative publicity, and damage to brand reputation. ➢ Non-compliance: Violation of legal and regulatory obligations, leading to penalties and legal consequences.
- Cybersecurity Risk Management: Organizations should implement a cybersecurity risk management strategy to protect against evolving cyber threats. This includes: ➢ Risk Assessment: Identifying and evaluating potential risks and vulnerabilities. ➢ Risk Mitigation: Implementing security controls, policies, and procedures to reduce the likelihood and impact of cyber attacks. ➢ Incident Response: Developing plans and procedures to respond effectively to security incidents and breaches. ➢ Employee Awareness and Training: Educating employees about cybersecurity best practices and promoting a culture of security. It's important for organizations to stay vigilant, regularly update their security measures, and adapt to emerging threats to mitigate cybersecurity risks effectively. 1.4.1.3 What is Big Data The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs. Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before. (oracle, 2023) Based on the above information, it can be concluded that big data refers to large and complex data sets that cannot be processed by traditional software. These datasets are characterized by greater diversity, increasing volume, and faster velocity. However, big data can be used to solve business problems that were previously unsolvable.
