Law and Digital Security 2

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Younis Badar Salim Al Kharusi 1920083
1920083
WRIT2 -FinalTerm – Draft Link- Review
GIS5007 LDS- 2023-2024 S1
Gulf College Oman
Document Details
Submission ID
trn:oid:::1:2776156894
10 Pages
Submission Date
2,031 Words
Dec 7, 2023, 5:54 PM GMT+4
13,437 Characters
Download Date
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File Name
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How much of this submission has been generated by AI?
100%
of qualifying text in this submission has been determined to be
generated by AI.
Caution: Percentage may not indicate academic misconduct. Review required.
It is essential to understand the limitations of AI detection before making decisions
about a student’s work. We encourage you to learn more about Turnitin’s AI detection
capabilities before using the tool.
Frequently Asked Questions
What does the percentage mean?
The percentage shown in the AI writing detection indicator and in the AI writing report is the amount of qualifying text within the
submission that Turnitin’s AI writing detection model determines was generated by AI.
Our testing has found that there is a higher incidence of false positives when the percentage is less than 20. In order to reduce the
likelihood of misinterpretation, the AI indicator will display an asterisk for percentages less than 20 to call attention to the fact that
the score is less reliable.
However, the final decision on whether any misconduct has occurred rests with the reviewer/instructor. They should use the
percentage as a means to start a formative conversation with their student and/or use it to examine the submitted assignment in
greater detail according to their school’s policies.
How does Turnitin’s indicator address false positives?
Our model only processes qualifying text in the form of long-form writing. Long-form writing means individual sentences contained in paragraphs that make up a
longer piece of written work, such as an essay, a dissertation, or an article, etc. Qualifying text that has been determined to be AI-generated will be highlighted blue
on the submission text.
Non-qualifying text, such as bullet points, annotated bibliographies, etc., will not be processed and can create disparity between the submission highlights and the
percentage shown.
What does ‘qualifying text’ mean?
Sometimes false positives (incorrectly flagging human-written text as AI-generated), can include lists without a lot of structural variation, text that literally repeats
itself, or text that has been paraphrased without developing new ideas. If our indicator shows a higher amount of AI writing in such text, we advise you to take that
into consideration when looking at the percentage indicated.
In a longer document with a mix of authentic writing and AI generated text, it can be difficult to exactly determine where the AI writing begins and original writing
ends, but our model should give you a reliable guide to start conversations with the submitting student.
Disclaimer
Our AI writing assessment is designed to help educators identify text that might be prepared by a generative AI tool. Our AI writing assessment may not always be accurate (it may misidentify
both human and AI-generated text) so it should not be used as the sole basis for adverse actions against a student. It takes further scrutiny and human judgment in conjunction with an
organization’s application of its specific academic policies to determine whether any academic misconduct has occurred.
Page 2 of 12 – AI Writing Overview
Submission ID trn:oid:::1:2776156894
Page 3 of 12 – AI Writing Submission
Submission ID trn:oid:::1:2776156894
In academic
affiliation with
Student ID
Younis Alkharusi 1920083
Module Title
Program Area
Law and Digital Security
information systems management
L5-B2
Level & Block
Semester and Academic
Year
Assessment
2023-24, 1st Semester
WRIT1
Weighting
50%
Date of Submission
Black
Page 3 of 12 – AI Writing Submission
2
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Page 4 of 12 – AI Writing Submission
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I. Introduction
The rapid growth of digital data, stemming from various sources such as social media, IoT
devices, sensors, and online transactions, has given rise to the era of big data analytics. This
transformative technology involves the processing and analysis of massive, complex datasets to
extract valuable insights, patterns, and trends. In the realm of cybersecurity, the application of
big data analytics has become increasingly crucial in fortifying defenses against the dynamic
landscape of cyber threats. As organizations grapple with the escalating sophistication of
cyberattacks, the integration of big data analytics offers a proactive and sophisticated approach
to identify, prevent, and respond to cyber threats effectively.
The importance of big data analytics in cybersecurity lies in its ability to harness the large
volume of network-generated data, enabling cybersecurity professionals to uncover subtle
patterns, anomalies, and potential signs of compromise. This capability empowers
organizations to move beyond traditional reactive approaches and adopt a proactive stance in
threat identification and mitigation. As highlighted by Jang-Jaccard et al. (2014), big data
analytics plays a vital role in enhancing cybersecurity by providing valuable insights that enable
swift and informed decision-making in the face of evolving cyber threats. The integration of
such analytics enables organizations to not only bolster their security measures but also to
adapt and respond rapidly to emerging threats.
Research Objectives and Scope:
The primary objective of this research is to critically evaluate the impact and significance of big
data analytics in strengthening cybersecurity measures. This includes an in-depth examination
of the effectiveness of big data analytics in threat identification, prevention, and response.
Furthermore, the research aims to explore the key concepts and challenges associated with the
integration of big data analytics in cybersecurity tools. By comparing different datasets used in
implementing cybersecurity measures, the study seeks to discern best practices and potential
areas of improvement. Additionally, the research will review existing studies in the field of big
data-driven cybersecurity to provide a comprehensive understanding of the current state of
research. Lastly, the analysis will focus on various big data analytics techniques for threat
detection and prevention, considering the crucial role of data privacy and ethical considerations
in this rapidly evolving landscape. Through these objectives, the study aspires to contribute to
the advancement of knowledge in leveraging big data analytics for robust cybersecurity
practices.
II. Literature Review
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The integration of big data analytics in cybersecurity has been a subject of extensive research,
reflecting the growing recognition of its significance in enhancing cyber defenses. This literature
review aims to provide a comprehensive overview of existing studies on the intersection of
cybersecurity and big data analytics, offering insights into key concepts, challenges, and
advancements in the field.
Description:
The literature on big data analytics in cybersecurity emphasizes its role in processing and
analyzing large datasets to uncover patterns, anomalies, and potential threats. Jang-Jaccard et
al. (2014) underscore the proactive threat identification capabilities enabled by big data
analytics, emphasizing its contribution to effective decision-making in cybersecurity.
Additionally, studies like Dumbacher (2017) delve into the specific application of big data
analytics for cybersecurity, providing a detailed exploration of its methodologies and outcomes.
Summary:
A notable study by Jones, Smith, and Williams (2018) presents a comprehensive review of
leveraging big data for cybersecurity, offering valuable insights into the current state of
research. This study emphasizes the importance of integrating big data analytics tools and
techniques in cybersecurity strategies, highlighting their potential to enhance threat detection
and response capabilities. Moreover, Curtis and Oxburgh (2022) contribute to the literature by
examining the practical implications of cybersecurity in ‘real world’ policing and law
enforcement, shedding light on the challenges and opportunities faced in operational contexts.
Evaluation:
While the literature generally acknowledges the potential benefits of big data analytics in
cybersecurity, there exists a need for more empirical studies assessing the actual effectiveness
of these technologies in diverse organizational settings. Bossler and Berenblum (2019) address
this gap by introducing new directions in cybercrime research, emphasizing the importance of
empirical evidence to inform cybersecurity practices. The evaluation of existing research
highlights the evolving nature of cyber threats and the necessity for adaptive and scalable big
data analytics solutions.
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Comparison:
Comparing studies reveals variations in methodologies, focus areas, and outcomes. For
instance, the study by De Silva (2023) explores the relationship between cybersecurity culture
and cyber-crime prevention, introducing a socio-cultural perspective to the discourse. This
approach stands in contrast to the more technical emphasis of other studies, illustrating the
multidimensional nature of the relationship between big data analytics and cybersecurity.
Table:
The following table provides a comparative overview of selected studies in the field of big data
analytics and cybersecurity:
Author(s)
Jang-Jaccard et al.
(2014)
Focus
Proactive threat
identification
Methodology
Data analysis and
pattern recognition
Jones, Smith, &
Williams (2018)
Integration of big
data in cybersecurity
Literature review and
analysis
Curtis & Oxburgh
(2022)
Real-world
implications of
cybersecurity
New directions in
cybercrime research
Case studies and
qualitative analysis
Cybersecurity culture
and prevention
Systematic review
Bossler & Berenblum
(2019)
De Silva (2023)
Empirical research
and data analysis
Key Findings
Swift and effective
response to cyber
threats
Enhanced threat
detection and
response capabilities
Challenges and
opportunities in
operational contexts
Emphasis on
empirical evidence in
cybercrime research
Socio-cultural factors
influencing cybercrime prevention
III. Analysis / Discussion
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In the realm of cybersecurity, the analysis of big data is instrumental in fortifying defenses
against an ever-evolving landscape of threats. This section delves into the various big data
analytics techniques employed for threat detection and prevention, highlighting their strengths
and potential challenges. Additionally, the discussion extends to the crucial aspects of data
privacy and ethical considerations, emphasizing the need for a balanced and responsible
approach in the deployment of big data-driven cybersecurity measures.
Big Data Analytics Techniques for Threat Detection and Prevention:
Machine Learning Algorithms: Machine learning algorithms play a pivotal role in analyzing
large datasets to identify patterns and anomalies indicative of potential cyber threats.
Supervised learning models, such as support vector machines and neural networks, are
effective in training on historical data to predict and classify threats accurately (Dumbacher,
2017). Unsupervised learning models, such as clustering algorithms, contribute to anomaly
detection by identifying deviations from normal behavior (Jones et al., 2018).
Behavioral Analytics: Behavioral analytics leverages big data to establish baselines of normal
user behavior and detect deviations that may indicate malicious activity. By analyzing patterns
in user activities, behavioral analytics tools can identify anomalies and potential threats in realtime, enhancing the ability to respond promptly to suspicious behavior (Wu et al., 2023).
Predictive Analytics: Predictive analytics involves the use of statistical algorithms and machine
learning techniques to forecast potential cyber threats based on historical data and trends. This
forward-looking approach enables organizations to proactively address vulnerabilities and
anticipate potential attack vectors (Dumbacher, 2017).
Role of Data Privacy and Ethical Considerations:
While big data analytics offers unparalleled capabilities in enhancing cybersecurity, the ethical
and privacy implications of these technologies are paramount. The vast amount of data
processed for threat detection may include sensitive personal information, raising concerns
about user privacy and consent. Organizations must implement robust data anonymization and
encryption measures to safeguard individual privacy (De Silva, 2023).
Privacy-Preserving Techniques:
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Homomorphic Encryption: This technique allows computations to be performed on encrypted
data without decrypting it, preserving privacy during analysis (Dumbacher, 2017).
Differential Privacy: By adding noise to individual data points, differential privacy ensures that
the output of analytics algorithms does not reveal specific details about any individual’s data,
striking a balance between utility and privacy (Jones et al., 2018).
Ethical Considerations
Transparency and Accountability: Organizations should adopt transparent practices in
communicating how big data analytics is used for cybersecurity. Providing users with clear
information about data collection, processing, and storage fosters accountability (De Silva,
2023).
Bias Mitigation: Ethical deployment of big data analytics involves addressing biases that may
exist in the data. Algorithms should be regularly audited and adjusted to mitigate any
unintentional biases that could impact decision-making (Wu et al., 2023).
IV. Conclusions
The integration of big data analytics into cybersecurity strategies presents a paradigm shift in
addressing the dynamic and sophisticated nature of cyber threats. This section summarizes the
key findings derived from the analysis and discussion, emphasizing the transformative potential
of big data analytics. Additionally, recommendations are provided for leveraging these
technologies to enhance the overall effectiveness of cybersecurity measures.
Summary of Key Findings:
Effectiveness of Big Data Analytics in Cybersecurity: The analysis has underscored the
effectiveness of big data analytics in cybersecurity, particularly in proactive threat identification
and response. Machine learning algorithms, behavioral analytics, and predictive analytics
emerge as powerful tools for identifying patterns, anomalies, and potential cyber threats in
real-time (Jang-Jaccard et al., 2014; Wu et al., 2023; Dumbacher, 2017).
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Challenges and Considerations: While big data analytics holds immense promise, challenges
related to data privacy and ethical considerations are critical. The responsible deployment of
these technologies requires robust privacy-preserving techniques, transparency, and mitigation
of biases to address ethical concerns (De Silva, 2023; Jones et al., 2018).
Multidimensional Approach: The literature review and analysis highlight the multidimensional
nature of big data analytics in cybersecurity. Different studies emphasize diverse aspects,
including the technical application of analytics tools, real-world implications in law
enforcement, and socio-cultural factors influencing cybercrime prevention (Curtis & Oxburgh,
2022; Silveira, 2013).
Recommendations for Enhancing Cybersecurity Effectiveness
Investment in Education and Training: Organizations should prioritize educating cybersecurity
professionals on the latest advancements in big data analytics. Training programs should
encompass the application of machine learning algorithms, behavioral analytics, and predictive
analytics to empower professionals in effectively utilizing these tools (Dumbacher, 2017).
Continuous Evaluation and Adaptation: Given the evolving nature of cyber threats,
organizations should adopt a proactive stance by continuously evaluating and adapting their big
data analytics strategies. Regular audits of algorithms, threat models, and data sources are
essential to ensure the relevance and effectiveness of cybersecurity measures (Jones et al.,
2018).
Privacy-Preserving Technologies: Integrating privacy-preserving technologies, such as
homomorphic encryption and differential privacy, into big data analytics frameworks is crucial.
This ensures that the analysis remains effective while respecting individual privacy rights (De
Silva, 2023).
Promotion of Ethical Practices: Organizations should prioritize ethical considerations in the
deployment of big data analytics for cybersecurity. Transparent communication, accountability,
and bias mitigation strategies should be integral components of cybersecurity practices,
fostering trust and ethicality in the use of these technologies (Wu et al., 2023).
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In conclusion, the synthesis of findings emphasizes the transformative potential of big data
analytics in cybersecurity. By addressing challenges related to data privacy and ethical
considerations and implementing the recommended strategies, organizations can harness the
full power of big data analytics to fortify their defenses and effectively respond to the everevolving landscape of cyber threats. This multidimensional approach not only enhances
cybersecurity effectiveness but also ensures a responsible and ethical deployment of big data
analytics in safeguarding sensitive information and digital infrastructures.
V. Reference List
Bossler, A. M., & Berenblum, T. (2019). Introduction: New directions in cybercrime research.
Journal of Crime and Justice, 42(5), 495-499. https://doi.org/10.1080/0735648X.2019.1692426
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Curtis, J., & Oxburgh, G. (2022). Understanding cybercrime in ‘real world’ policing and law
enforcement. The Police Journal. https://doi.org/10.1177/0032258X221107584
De Silva, B. (2023). Exploring the Relationship Between Cybersecurity Culture and Cyber-Crime
Prevention: A Systematic Review. International Journal of Information Security and Cybercrime,
12(1), 23-29. https://doi.org/10.19107/ijisc.2023.01.03
Dumbacher, P. (2017). Big Data Analytics for Cybersecurity. International Journal of Network
Security & Its Applications, 9(6), 57-70. https://doi.org/10.5121/ijnsa.2017.9605
Jones, A., Smith, B., & Williams, C. (2018). Leveraging Big Data for Cybersecurity: A Review.
Journal of Cybersecurity and Mobility, 7(1), 1-21. https://doi.org/10.13052/jcsm2245-1439.714
Jang-Jaccard, J., Nepal, S., & Chen, S. (2014). Big data analytics for cyber security. In Proceedings
of the 2014 IEEE International Congress on Big Data (pp. 94-101). IEEE.
https://doi.org/10.1109/BigData.Congress.2014.18
Radoniewicz, F. (2021). Cybercrime in Selected European Countries. Cybersecurity in Poland,
419–439. https://doi.org/10.1007/978-3-030-78551-2_25
Silveira, A. d. (2013). Aaron Swartz and the Battles for Freedom of Knowledge. SSRN Electronic
Journal. https://doi.org/10.2139/ssrn.2399578
Wu, L., Peng, Q., & Lembke, M. (2023). Research Trends in Cybercrime and Cybersecurity: A
Review Based on Web of Science Core Collection Database. International Journal of
Cybersecurity Intelligence and Cybercrime. https://doi.org/10.52306/ozmb2721
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Page 1 of 12 – Cover Page
Submission ID trn:oid:::1:2776156894
Younis Badar Salim Al Kharusi 1920083
1920083
WRIT2 -FinalTerm – Draft Link- Review
GIS5007 LDS- 2023-2024 S1
Gulf College Oman
Document Details
Submission ID
trn:oid:::1:2776156894
10 Pages
Submission Date
2,031 Words
Dec 7, 2023, 5:54 PM GMT+4
13,437 Characters
Download Date
Dec 9, 2023, 11:16 AM GMT+4
File Name
Law_and_Digital_Security_22.docx
File Size
307.0 KB
Page 1 of 12 – Cover Page
Submission ID trn:oid:::1:2776156894
Page 2 of 12 – AI Writing Overview
Submission ID trn:oid:::1:2776156894
How much of this submission has been generated by AI?
100%
of qualifying text in this submission has been determined to be
generated by AI.
Caution: Percentage may not indicate academic misconduct. Review required.
It is essential to understand the limitations of AI detection before making decisions
about a student’s work. We encourage you to learn more about Turnitin’s AI detection
capabilities before using the tool.
Frequently Asked Questions
What does the percentage mean?
The percentage shown in the AI writing detection indicator and in the AI writing report is the amount of qualifying text within the
submission that Turnitin’s AI writing detection model determines was generated by AI.
Our testing has found that there is a higher incidence of false positives when the percentage is less than 20. In order to reduce the
likelihood of misinterpretation, the AI indicator will display an asterisk for percentages less than 20 to call attention to the fact that
the score is less reliable.
However, the final decision on whether any misconduct has occurred rests with the reviewer/instructor. They should use the
percentage as a means to start a formative conversation with their student and/or use it to examine the submitted assignment in
greater detail according to their school’s policies.
How does Turnitin’s indicator address false positives?
Our model only processes qualifying text in the form of long-form writing. Long-form writing means individual sentences contained in paragraphs that make up a
longer piece of written work, such as an essay, a dissertation, or an article, etc. Qualifying text that has been determined to be AI-generated will be highlighted blue
on the submission text.
Non-qualifying text, such as bullet points, annotated bibliographies, etc., will not be processed and can create disparity between the submission highlights and the
percentage shown.
What does ‘qualifying text’ mean?
Sometimes false positives (incorrectly flagging human-written text as AI-generated), can include lists without a lot of structural variation, text that literally repeats
itself, or text that has been paraphrased without developing new ideas. If our indicator shows a higher amount of AI writing in such text, we advise you to take that
into consideration when looking at the percentage indicated.
In a longer document with a mix of authentic writing and AI generated text, it can be difficult to exactly determine where the AI writing begins and original writing
ends, but our model should give you a reliable guide to start conversations with the submitting student.
Disclaimer
Our AI writing assessment is designed to help educators identify text that might be prepared by a generative AI tool. Our AI writing assessment may not always be accurate (it may misidentify
both human and AI-generated text) so it should not be used as the sole basis for adverse actions against a student. It takes further scrutiny and human judgment in conjunction with an
organization’s application of its specific academic policies to determine whether any academic misconduct has occurred.
Page 2 of 12 – AI Writing Overview
Submission ID trn:oid:::1:2776156894
Page 3 of 12 – AI Writing Submission
Submission ID trn:oid:::1:2776156894
In academic
affiliation with
Student ID
Younis Alkharusi 1920083
Module Title
Program Area
Law and Digital Security
information systems management
L5-B2
Level & Block
Semester and Academic
Year
Assessment
2023-24, 1st Semester
WRIT1
Weighting
50%
Date of Submission
Black
Page 3 of 12 – AI Writing Submission
2
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Submission ID trn:oid:::1:2776156894
I. Introduction
The rapid growth of digital data, stemming from various sources such as social media, IoT
devices, sensors, and online transactions, has given rise to the era of big data analytics. This
transformative technology involves the processing and analysis of massive, complex datasets to
extract valuable insights, patterns, and trends. In the realm of cybersecurity, the application of
big data analytics has become increasingly crucial in fortifying defenses against the dynamic
landscape of cyber threats. As organizations grapple with the escalating sophistication of
cyberattacks, the integration of big data analytics offers a proactive and sophisticated approach
to identify, prevent, and respond to cyber threats effectively.
The importance of big data analytics in cybersecurity lies in its ability to harness the large
volume of network-generated data, enabling cybersecurity professionals to uncover subtle
patterns, anomalies, and potential signs of compromise. This capability empowers
organizations to move beyond traditional reactive approaches and adopt a proactive stance in
threat identification and mitigation. As highlighted by Jang-Jaccard et al. (2014), big data
analytics plays a vital role in enhancing cybersecurity by providing valuable insights that enable
swift and informed decision-making in the face of evolving cyber threats. The integration of
such analytics enables organizations to not only bolster their security measures but also to
adapt and respond rapidly to emerging threats.
Research Objectives and Scope:
The primary objective of this research is to critically evaluate the impact and significance of big
data analytics in strengthening cybersecurity measures. This includes an in-depth examination
of the effectiveness of big data analytics in threat identification, prevention, and response.
Furthermore, the research aims to explore the key concepts and challenges associated with the
integration of big data analytics in cybersecurity tools. By comparing different datasets used in
implementing cybersecurity measures, the study seeks to discern best practices and potential
areas of improvement. Additionally, the research will review existing studies in the field of big
data-driven cybersecurity to provide a comprehensive understanding of the current state of
research. Lastly, the analysis will focus on various big data analytics techniques for threat
detection and prevention, considering the crucial role of data privacy and ethical considerations
in this rapidly evolving landscape. Through these objectives, the study aspires to contribute to
the advancement of knowledge in leveraging big data analytics for robust cybersecurity
practices.
II. Literature Review
Page 4 of 12 – AI Writing Submission
Submission ID trn:oid:::1:2776156894
Page 5 of 12 – AI Writing Submission
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The integration of big data analytics in cybersecurity has been a subject of extensive research,
reflecting the growing recognition of its significance in enhancing cyber defenses. This literature
review aims to provide a comprehensive overview of existing studies on the intersection of
cybersecurity and big data analytics, offering insights into key concepts, challenges, and
advancements in the field.
Description:
The literature on big data analytics in cybersecurity emphasizes its role in processing and
analyzing large datasets to uncover patterns, anomalies, and potential threats. Jang-Jaccard et
al. (2014) underscore the proactive threat identification capabilities enabled by big data
analytics, emphasizing its contribution to effective decision-making in cybersecurity.
Additionally, studies like Dumbacher (2017) delve into the specific application of big data
analytics for cybersecurity, providing a detailed exploration of its methodologies and outcomes.
Summary:
A notable study by Jones, Smith, and Williams (2018) presents a comprehensive review of
leveraging big data for cybersecurity, offering valuable insights into the current state of
research. This study emphasizes the importance of integrating big data analytics tools and
techniques in cybersecurity strategies, highlighting their potential to enhance threat detection
and response capabilities. Moreover, Curtis and Oxburgh (2022) contribute to the literature by
examining the practical implications of cybersecurity in ‘real world’ policing and law
enforcement, shedding light on the challenges and opportunities faced in operational contexts.
Evaluation:
While the literature generally acknowledges the potential benefits of big data analytics in
cybersecurity, there exists a need for more empirical studies assessing the actual effectiveness
of these technologies in diverse organizational settings. Bossler and Berenblum (2019) address
this gap by introducing new directions in cybercrime research, emphasizing the importance of
empirical evidence to inform cybersecurity practices. The evaluation of existing research
highlights the evolving nature of cyber threats and the necessity for adaptive and scalable big
data analytics solutions.
Page 5 of 12 – AI Writing Submission
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Comparison:
Comparing studies reveals variations in methodologies, focus areas, and outcomes. For
instance, the study by De Silva (2023) explores the relationship between cybersecurity culture
and cyber-crime prevention, introducing a socio-cultural perspective to the discourse. This
approach stands in contrast to the more technical emphasis of other studies, illustrating the
multidimensional nature of the relationship between big data analytics and cybersecurity.
Table:
The following table provides a comparative overview of selected studies in the field of big data
analytics and cybersecurity:
Author(s)
Jang-Jaccard et al.
(2014)
Focus
Proactive threat
identification
Methodology
Data analysis and
pattern recognition
Jones, Smith, &
Williams (2018)
Integration of big
data in cybersecurity
Literature review and
analysis
Curtis & Oxburgh
(2022)
Real-world
implications of
cybersecurity
New directions in
cybercrime research
Case studies and
qualitative analysis
Cybersecurity culture
and prevention
Systematic review
Bossler & Berenblum
(2019)
De Silva (2023)
Empirical research
and data analysis
Key Findings
Swift and effective
response to cyber
threats
Enhanced threat
detection and
response capabilities
Challenges and
opportunities in
operational contexts
Emphasis on
empirical evidence in
cybercrime research
Socio-cultural factors
influencing cybercrime prevention
III. Analysis / Discussion
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In the realm of cybersecurity, the analysis of big data is instrumental in fortifying defenses
against an ever-evolving landscape of threats. This section delves into the various big data
analytics techniques employed for threat detection and prevention, highlighting their strengths
and potential challenges. Additionally, the discussion extends to the crucial aspects of data
privacy and ethical considerations, emphasizing the need for a balanced and responsible
approach in the deployment of big data-driven cybersecurity measures.
Big Data Analytics Techniques for Threat Detection and Prevention:
Machine Learning Algorithms: Machine learning algorithms play a pivotal role in analyzing
large datasets to identify patterns and anomalies indicative of potential cyber threats.
Supervised learning models, such as support vector machines and neural networks, are
effective in training on historical data to predict and classify threats accurately (Dumbacher,
2017). Unsupervised learning models, such as clustering algorithms, contribute to anomaly
detection by identifying deviations from normal behavior (Jones et al., 2018).
Behavioral Analytics: Behavioral analytics leverages big data to establish baselines of normal
user behavior and detect deviations that may indicate malicious activity. By analyzing patterns
in user activities, behavioral analytics tools can identify anomalies and potential threats in realtime, enhancing the ability to respond promptly to suspicious behavior (Wu et al., 2023).
Predictive Analytics: Predictive analytics involves the use of statistical algorithms and machine
learning techniques to forecast potential cyber threats based on historical data and trends. This
forward-looking approach enables organizations to proactively address vulnerabilities and
anticipate potential attack vectors (Dumbacher, 2017).
Role of Data Privacy and Ethical Considerations:
While big data analytics offers unparalleled capabilities in enhancing cybersecurity, the ethical
and privacy implications of these technologies are paramount. The vast amount of data
processed for threat detection may include sensitive personal information, raising concerns
about user privacy and consent. Organizations must implement robust data anonymization and
encryption measures to safeguard individual privacy (De Silva, 2023).
Privacy-Preserving Techniques:
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Homomorphic Encryption: This technique allows computations to be performed on encrypted
data without decrypting it, preserving privacy during analysis (Dumbacher, 2017).
Differential Privacy: By adding noise to individual data points, differential privacy ensures that
the output of analytics algorithms does not reveal specific details about any individual’s data,
striking a balance between utility and privacy (Jones et al., 2018).
Ethical Considerations
Transparency and Accountability: Organizations should adopt transparent practices in
communicating how big data analytics is used for cybersecurity. Providing users with clear
information about data collection, processing, and storage fosters accountability (De Silva,
2023).
Bias Mitigation: Ethical deployment of big data analytics involves addressing biases that may
exist in the data. Algorithms should be regularly audited and adjusted to mitigate any
unintentional biases that could impact decision-making (Wu et al., 2023).
IV. Conclusions
The integration of big data analytics into cybersecurity strategies presents a paradigm shift in
addressing the dynamic and sophisticated nature of cyber threats. This section summarizes the
key findings derived from the analysis and discussion, emphasizing the transformative potential
of big data analytics. Additionally, recommendations are provided for leveraging these
technologies to enhance the overall effectiveness of cybersecurity measures.
Summary of Key Findings:
Effectiveness of Big Data Analytics in Cybersecurity: The analysis has underscored the
effectiveness of big data analytics in cybersecurity, particularly in proactive threat identification
and response. Machine learning algorithms, behavioral analytics, and predictive analytics
emerge as powerful tools for identifying patterns, anomalies, and potential cyber threats in
real-time (Jang-Jaccard et al., 2014; Wu et al., 2023; Dumbacher, 2017).
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Challenges and Considerations: While big data analytics holds immense promise, challenges
related to data privacy and ethical considerations are critical. The responsible deployment of
these technologies requires robust privacy-preserving techniques, transparency, and mitigation
of biases to address ethical concerns (De Silva, 2023; Jones et al., 2018).
Multidimensional Approach: The literature review and analysis highlight the multidimensional
nature of big data analytics in cybersecurity. Different studies emphasize diverse aspects,
including the technical application of analytics tools, real-world implications in law
enforcement, and socio-cultural factors influencing cybercrime prevention (Curtis & Oxburgh,
2022; Silveira, 2013).
Recommendations for Enhancing Cybersecurity Effectiveness
Investment in Education and Training: Organizations should prioritize educating cybersecurity
professionals on the latest advancements in big data analytics. Training programs should
encompass the application of machine learning algorithms, behavioral analytics, and predictive
analytics to empower professionals in effectively utilizing these tools (Dumbacher, 2017).
Continuous Evaluation and Adaptation: Given the evolving nature of cyber threats,
organizations should adopt a proactive stance by continuously evaluating and adapting their big
data analytics strategies. Regular audits of algorithms, threat models, and data sources are
essential to ensure the relevance and effectiveness of cybersecurity measures (Jones et al.,
2018).
Privacy-Preserving Technologies: Integrating privacy-preserving technologies, such as
homomorphic encryption and differential privacy, into big data analytics frameworks is crucial.
This ensures that the analysis remains effective while respecting individual privacy rights (De
Silva, 2023).
Promotion of Ethical Practices: Organizations should prioritize ethical considerations in the
deployment of big data analytics for cybersecurity. Transparent communication, accountability,
and bias mitigation strategies should be integral components of cybersecurity practices,
fostering trust and ethicality in the use of these technologies (Wu et al., 2023).
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In conclusion, the synthesis of findings emphasizes the transformative potential of big data
analytics in cybersecurity. By addressing challenges related to data privacy and ethical
considerations and implementing the recommended strategies, organizations can harness the
full power of big data analytics to fortify their defenses and effectively respond to the everevolving landscape of cyber threats. This multidimensional approach not only enhances
cybersecurity effectiveness but also ensures a responsible and ethical deployment of big data
analytics in safeguarding sensitive information and digital infrastructures.
V. Reference List
Bossler, A. M., & Berenblum, T. (2019). Introduction: New directions in cybercrime research.
Journal of Crime and Justice, 42(5), 495-499. https://doi.org/10.1080/0735648X.2019.1692426
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Curtis, J., & Oxburgh, G. (2022). Understanding cybercrime in ‘real world’ policing and law
enforcement. The Police Journal. https://doi.org/10.1177/0032258X221107584
De Silva, B. (2023). Exploring the Relationship Between Cybersecurity Culture and Cyber-Crime
Prevention: A Systematic Review. International Journal of Information Security and Cybercrime,
12(1), 23-29. https://doi.org/10.19107/ijisc.2023.01.03
Dumbacher, P. (2017). Big Data Analytics for Cybersecurity. International Journal of Network
Security & Its Applications, 9(6), 57-70. https://doi.org/10.5121/ijnsa.2017.9605
Jones, A., Smith, B., & Williams, C. (2018). Leveraging Big Data for Cybersecurity: A Review.
Journal of Cybersecurity and Mobility, 7(1), 1-21. https://doi.org/10.13052/jcsm2245-1439.714
Jang-Jaccard, J., Nepal, S., & Chen, S. (2014). Big data analytics for cyber security. In Proceedings
of the 2014 IEEE International Congress on Big Data (pp. 94-101). IEEE.
https://doi.org/10.1109/BigData.Congress.2014.18
Radoniewicz, F. (2021). Cybercrime in Selected European Countries. Cybersecurity in Poland,
419–439. https://doi.org/10.1007/978-3-030-78551-2_25
Silveira, A. d. (2013). Aaron Swartz and the Battles for Freedom of Knowledge. SSRN Electronic
Journal. https://doi.org/10.2139/ssrn.2399578
Wu, L., Peng, Q., & Lembke, M. (2023). Research Trends in Cybercrime and Cybersecurity: A
Review Based on Web of Science Core Collection Database. International Journal of
Cybersecurity Intelligence and Cybercrime. https://doi.org/10.52306/ozmb2721
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Module Code
Module Title
Module Credits
GIS5007
Law and Digital Security
20
Academic Year and Semester Examination Board
Level & Block
2023-24, 1st Semester
L5-B2
January 2024
Method of Assessment
Term
Weighting
WRIT2
End-term
50%
Module Leader
Module Leader email
Ms. Marya AL Amri
marya@gulfcollege.edu.om
Additional Information (if any)
This coursework is to be completed individually.
Equivalent to 2000 words.
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Page 1 of 14
Contents
CONTENTS ……………………………………………………………………………………………………. 2
ASSESSMENT DETAILS …………………………………………………………………………………….. 3
SUBMISSION DETAILS ……………………………………………………………………………………… 5
ASSESSMENT CRITERIA ……………………………………………………………………………………. 5
FURTHER INFORMATION ……………………………………………………………………………….. 11
Who can answer questions about my assessment? …………………………………………………….. 12
Referencing and independent learning (Not applicable for Examination) ………………………… 12
Technical submission problems (Not applicable for Examination) ………………………………….. 12
Mitigating circumstances ……………………………………………………………………………………….. 12
Unfair academic practice ……………………………………………………………………………………….. 12
How is my work graded? ………………………………………………………………………………………… 13
IV FORMS……………………………………………………………. Error! Bookmark not defined.
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Assessment Details
Assessment title
Abr.
Weighting
Reflective Assignment
WRIT2
50%
Pass marks for undergraduate work is 40%, unless stated otherwise.
Task/assessment brief:
Assignment Overview:
Utilising the Power of Big Data Analytics to Strengthen Cybersecurity
Big data refers to massive, complicated datasets from various sources, such as social media, IoT devices, sensors, and
online transactions. Due to its ability to deliver valuable insights and enhance decision-making processes in various
fields, the analysis of large datasets has gained prominence in recent years. In cybersecurity, big data analytics is vital
in bolstering defences against changing threats and safeguarding sensitive data.
Organisations can improve their capacity to identify, prevent, and respond to cyberattacks by employing big data
analytics in cybersecurity. The large volume of network-generated data can be utilised to uncover patterns,
abnormalities, and potential compromise signs. This enables proactive threat identification and allows cybersecurity
professionals to respond effectively and quickly. Jang-Jaccard, J., Nepal, S., & Chen, S. (2014). Big data analytics for cyber
security. In Proceedings of the
10.1109/BigData.Congress.2014.18
2014
IEEE
International
Congress
on
Big
Data
(pp.
94-101).
IEEE.
doi:
Tasks:
Critical review, summarize and evaluate the topic on the following outline:
1. Evaluation of Big data analytics and how its strengthen cybersecurity.
2. Key concepts and challenges in big data analytics for cybersecurity tools.
3. Compare two data sets which are used to implement cybersecurity tools.
4. Review of existing research in big data-driven cybersecurity.
5. Perform analysis of big data analytics techniques for threat detection and prevention. Discuss the
role of data privacy and ethical considerations in big data-driven cybersecurity.
Report Format and Content Requirement
I.
II.
Introduction. In this section, write an introduction about Big data analytics and its importance in
cybersecurity. Include research objective(s) and scope. Your introduction should be at least two
paragraphs long (about 300 words), refer to task 1. Also, properly paraphrase and/or write at least two
in-text citations in this section.
Literature Review. This section is an evaluative report of information based on printed and/ or online
sources. The review should contain a description, summary, evaluation, and comparison of the study
with previous research on cybersecurity and big data. Support this section with properly referenced
citations. If you present this section in a tabular format, precede the table with a short introductory
paragraph, and refer to tasks 2-4.
III.
Analysis / Discussion. In this section, performs an analysis of big data analytics techniques for threat
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IV.
V.
detection and prevention. Discuss the role of data privacy and ethical considerations in big data-driven
cybersecurity, and refer to task 5.
Conclusion. This section of the report should include a summary of key findings from the analysis and
discussion. Recommendations for leveraging big data analytics to enhance cybersecurity effectiveness.
Reference List. List down all the references you cited in the report using the Harvard style of referencing.
Make sure that the references you listed match the citations you made in the report.
Additional instructions:
• Your student identification number must be clearly stated at the top of each page of your work.
• Where appropriate, a contents page, a list of tables/figures, and a list of abbreviations should precede your
work.
• Each page must be numbered.
• Please use Calibri font
o size 14, bold for main titles
o size 12, bold for subtitles
o size 11, regular for the body of each section
o size 9, and italics for the image, chart or graph captions or labels
• All referencing must adhere to Institutional requirements (Harvard Referencing Style).
• A word count must be stated at the end of your work.
• All tables and figures (if there are any) must be correctly numbered and labelled.
• Upload your partial outputs to MS Teams for formative feedback.
• Your final report must be uploaded to Turnitin for plagiarism checking; college rules on plagiarism apply.
*************
Word count (or equivalent):
2000 words
This is a reflection of the effort required for the assessment. Word counts will normally include any text, tables,
calculations, figures, subtitles, and citations. Reference lists and contents of appendices are excluded from the word
count. Contents of appendices are not usually considered when determining your final assessment grade.
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Submission Details
Submission
Deadline:
Submission
Time:
END-TERM:
7th of December 2023
Estimated Feedback
Return Date
After the result
announcement (10 working
days) – January 2024 EB
9:00 PM
Turnitin:
Any assessments submitted after the deadline will not be marked and will be recorded as a
non-attempt unless you have had an extension request agreed upon or have approved
mitigating circumstances. See the Gulf College website for more information on submission
details and mitigating circumstances.
File Format:
The assessment must be submitted as a Word document and submitted through the Turnitin
submission point.
Your assessment should be titled with your:
Student ID number, Module code and Assessment ID,
e.g. 1610200 GIS5007 WRIT2
Feedback
Feedback for the assessment will be provided electronically via Turnitin / MS Teams / Face to
Face. Feedback will be provided with comments on your strengths and the areas in which you
can improve. Module tutors give students two types of assessment feedback: formative, which
is given when the student is working on the completion of an assignment or coursework, and
summative, which is given upon completion of the module. Comprehensive assessment
feedback on your performance will be given after the announcement of the results. (10
Working Days)
Assessment Criteria
Learning outcomes assessed
On successful completion of the module, a student should be able to:
•
•
•
Demonstrate understanding of the management of data from a legal and ethical context.
Evaluate aspects of security and the forensic analysis of data.
Synthesise the wider application of cloud computing and big data analysis.
In addition, the assessment will test the following learning outcome:
• Evaluate aspects of security and the forensic analysis of data.
• Synthesise the wider application of cloud computing and big data analysis.
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Marking Scheme
Max.
Marks
Item
Criteria
10
Introduction
Big Data analysis introduction and its importance in
cybersecurity.
Research objective and scope.
5
Literature
Review
15
Key concepts and challenges in big data analytics for the
cybersecurity tools.
Compare two data sets which is used to implement cybersecurity
tools.
Review of existing research in big data-driven cybersecurity.
Analysis/
Discussion
Report
Structure
and
Formatting
10
10
Analysis of big data analytics techniques for threat detection and
prevention.
Discuss the role of data privacy and ethical considerations in big
data-driven cybersecurity.
15
30
15
10
Recommendations for leveraging big data analytics to enhance
cybersecurity effectiveness.
The report should be well-formatted, with consistent headings,
subheadings, and numbering. Fonts, spacing, and margins should
be consistent and professional-looking, including Harvard
referencing style.
20
10
5
Total Marks
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30
10
The summary of key findings from the analysis and discussion.
Conclusion
Total
5
100
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Marking Criteria
Grade
% Mark
0
1–9
10 – 19
F
(Fail)
20 – 29
30 – 39
D
(Third)
40 – 49
C
(Lower
Second)
50 – 59
Requirements
No answer has been attempted or evidence of unfair practice.
The work presented for assessment may be incomplete and/or irrelevant and demonstrates a
serious lack of comprehension and/or engagement with the set task. Attainment of the learning
outcomes is minimal and assessment criteria are not addressed.
Misunderstanding or misinterpretation of the set task, providing a short and/or largely irrelevant
response. Consequently, no learning outcomes are met in full although there may be minimal
attainment of about one or two.
Minimal understanding of the set task and will partially have met some of the learning outcomes.
Little knowledge and understanding of the field of study relevant to the task. The limited ability is
shown to communicate simple concepts and/or information. Significant difficulties in the report’s
structure and organisation detract from the clarity and meaning overall. Evidence of individual
reading and investigation is negligible, and the limited referencing of literature and other sources is
frequently inaccurate. Demonstrates some ability to describe and report but very little evidence is
available to indicate an ability to engage in critical evaluation and reflection.
Partial understanding of the set task and some of the associated learning outcomes met at a basic
level. Factual inaccuracies, errors, and misconceptions are evident in important areas and elements
of the assessed work may be irrelevant to the task. If attempted, the presentation of arguments and
more complex ideas may be confused and clumsily expressed. Some enquiry and analysis relevant
to the task attempted but outcomes may be naïve, simplistic, and/or unconvincing. Demonstrates
limited knowledge of current research/scholarship in the discipline. A restricted range of sources is
used but overall, there is an over-reliance on program materials with little evidence of individual
reading and investigation. There are frequent errors in the referencing of literature and other
sources. The work is largely descriptive and arguments, if attempted, are rarely substantiated.
Demonstrates a basic understanding of the set task and an ability to have met the associated
learning outcomes and addresses the assessment criteria at a threshold level. Displays a basic
knowledge and understanding of many aspects of the field of study relevant to the task.
Reproduction of information received from elsewhere (e.g., program materials). Errors and
misconceptions will be evident, but these are outweighed by the degree of knowledge and
understanding demonstrated overall. More success is achieved in describing and reporting
information rather than communicating complex ideas. Generally, the work is appropriately
structured although key points may not be logically sequenced. Some limited analysis and enquiry
relevant to the task/discipline included and has intermittent success in presenting and commenting
on outcomes. A limited ability to critically evaluate and reflect. Although some critical reflection is
evident, the balance within the work is likely to be in favour of description and factual presentation.
A secure understanding of the set task and an ability to have met the associated learning outcomes
and address the assessment criteria at a satisfactory level. Displays a sound knowledge and
understanding of most key aspects of the field of study relevant to the task and there is some
evidence of an ability to apply such knowledge. Some evidence of independent thinking beyond
programme notes. Overall, the structure and format of the work are appropriate. Occasional faults
in the presentation of work, but overall, these do not detract from the clarity of expression.
Examples of research/scholarship referred to in the work demonstrate individual reading and
investigative ability to critically evaluate and reflect although there may be some over-reliance on
description and factual presentation. Arguments are usually substantiated.
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B
(Upper
Second)
60-69
70 – 79
A
(First)
80 – 89
90 – 100
Demonstrates a full understanding of the set task and an ability to have met the learning outcomes
and address the assessment criteria at a good level. Detailed knowledge and thorough
understanding of the key aspects of the field of study relevant to the task are shown. There is clear
evidence of an ability to apply such knowledge and, in some contexts, to extend and transform it.
Discussion of complex concepts is often tackled successfully and there is evidence of independent
thinking. Displays an ability to communicate information, ideas, and concepts clearly and succinctly.
The work is well presented and the format appropriate. Key points are appropriately organised, the
writing style is fluent, and the arguments are well articulated. Detailed analysis and critical enquiry
relevant to the task/discipline is undertaken by making use of appropriate techniques and has
considerable success in presenting and commenting on outcomes. There is some linkage between
theory and practice. Examples referred to indicate a breadth and depth of individual reading and
investigation that extend beyond the sources provided. The referencing of literature and other
sources is almost always accurate. Arguments are considered and substantiated and there is
evidence of an ability to make appropriate judgements and to suggest solutions to problems.
Demonstrates a full and detailed understanding of the set task and an ability to have met the
learning outcomes and address the assessment criteria at a very good level. Detailed knowledge
and systematic understanding of key aspects of the field of study relevant to the task are evident.
There is strong evidence of an ability to extend, transform, and apply such knowledge. The student
also demonstrates an ability to engage in a confident discussion of complex concepts and to
recognise the limitations and ambiguity of disciplinary knowledge. Independent thinking and
original insights are also present in the report. The ability is shown in communicating information,
complex ideas, and concepts coherently and succinctly. The standard of presentation is high and the
format appropriate. Key points are logically organised and in written work, the style is lucid and
mature. Detailed and thorough knowledge of current research/advanced scholarship in the
discipline. The use of scholarly reviews/primary sources is confident and a breadth and depth of
individual reading and investigation, extending beyond the sources provided, is apparent. The
referencing of literature and other sources is accurate and in line with academic conventions. An
ability to engage in critical evaluation of concepts/arguments/data and to make appropriate and
informed judgements is shown. Arguments are well developed, sustained, and substantiated.
Where relevant, assumptions are challenged and there is a clear recognition of the complexities of
academic debate. Appropriate and sometimes innovative solutions are offered to problems.
Beyond the above, a full and detailed understanding of the set task and an ability to have met the
learning outcomes and address the assessment criteria at an excellent level is displayed.
Beyond the above, demonstrates a full and detailed understanding of the set task and an ability to
have met the learning outcomes and address the assessment criteria at an out level. Work is of a
standard deemed to be worthy of publication Reference citations extend significantly beyond the
main body of reading normally expected in the discipline/field of study.
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AY: Click or tap here to enter text. / 1st Semester
Marking Criteria/Rubrics
Criteria
Introduction
Literature
Review
Analysis and
Discussion
Not Attempted/
Irrelevant (1)
Needs
Improvement
(1)
Satisfactory (2)
Good (3)
Very good (4)
Excellent (5)
Not Attempted/
Extremely shown
with significant
errors
Limited
knowledge, with
many errors’
misconceptions,
and gaps.
Detailed, accurate, and relevant.
Key points highlighted.
Demonstrates systematic
understanding of all key aspects
of the topic and excellent
breadth and depth of knowledge.
Appreciating any ambiguities in
the area of legal study. Strong
ability to apply legal knowledge
to the key issues of the task legal
study.
No evidence that
any reading of the
subject matter/
around the
subject matter
was undertaken.
No referencing is
used at all or is
frequently
inaccurate.
Sound knowledge and
understanding of key topics.
May be a tendency to
reproduce information
received from elsewhere
(e.g. programme materials).
A few errors or
misconceptions may be
present, but not in
important areas. Some
evidence of ability to apply
core legal principles.
Tendency to rely on core
materials and information
provided by tutors although
evidence of some individual
reading. Minor
inconsistencies and
inaccuracies in referencing
using the Harvard system.
Detailed, accurate,
relevant. Shows a
thorough understanding
of key aspects of the
topic. Discussion of more
complex legal issues uses
often tackled successfully.
Not Attempted /
Irrelevant sources
Material from a variety of
sources is used extending
beyond those sources
provided, demonstrating
some synthesis of
information. Referencing
relevant and most
accurate using the
Harvard system.
A wide variety of sources used
extends well beyond programme
material, showing a strong ability
to synthesise. Academic and
textbook referencing is clear,
relevant and consistently
accurate using the Harvard
system.
Not Attempted/
Irrelevant sources
Little or no
evidence of being
able to undertake
analysis. Fails to
identify or
evaluate different
perspectives or
arguments.
Inconclusive or
lacks an
Demonstrates basic
knowledge and
understanding,
reproducing
information is a
frequent feature.
Errors or
misconceptions will
be evident but
outweighed by the
overall
understanding.
Over-reliance on
materials provided
by the tutor. Little
or no evidence of
reading around the
subject. Referencing
present but contains
inconsistencies and
some inaccuracies,
overall Harvard
system used.
Fairly superficial and
generally derivative,
the balance of work
is in favour of
description and
factual
presentation. Some
evidence is
mentioned, but not
generally integrated
At times demonstrates an
ability to undertake analysis.
Evidence of findings and
conclusions are usually
grounded in appropriate
legal authority. Arguments
are usually substantiated.
Some over-reliance on
description and factual
presentation.
Able to undertake
detailed legal analysis,
good development of
arguments which are
substantiated. Most
points are illustrated with
relevant evidence. Good
evidence of evaluation
and ability to make
appropriate judgments.
Analytical and clear conclusions
are well-grounded in legal
doctrine and authority, possibly
showing the development of new
and innovative solutions to legal
problems. Key points supported
with legal authority, and
alternative perspectives are
critically evaluated. Comments
perceptively on the application of
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Criteria
Conclusion
Report
Structure and
Formatting
Not Attempted/
Irrelevant (1)
Needs
Improvement
(1)
Satisfactory (2)
appropriate
conclusion.
into the work or
evaluated, although
there may be some
limited attempts at
legal analysis and
evaluation.
Not
Attempted/Irreleva
nt
None or only one
of the main points
is summarised.
One or two main
points are
summarised but in a
manner that is
vague or too
general.
Two to three main points are
summarised with some
success. May I have one or
two issues with organisation,
but not to the point of being
a hindrance
The report has no
discernible
structure or
formatting, making
it difficult to
navigate and
comprehend.
The report lacks a
clear structure
and formatting,
making it
challenging to
follow the main
points.
The report has a
basic structure, but
the organisation and
formatting need
improvement for
better readability.
The report has a generally
appropriate structure with
headings, subheadings, and
formatting, but with some
inconsistencies or lack of
clarity.
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Good (3)
Very good (4)
Excellent (5)
legal authority to practical
problems.
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The conclusion somehow
captures the focus of the
research paper;
summarises the main
points (aspects) of the
research paper but needs
further elaboration.
The conclusion provides a
recommendation.
The conclusion includes an
ending comment that
inspires the reader to
continue thinking about
your topic.
The report has a clear
structure with appropriate
headings, subheadings,
and formatting, with
minor inconsistencies.
All the main points are
summarised with skills and
knowledge; all points are fell in
line and led up to an inevitable
conclusion
The report demonstrates a wellstructured format with
appropriate headings,
subheadings, and formatting.
Further Information
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Page 11 of 14
Who can answer questions about my assessment?
Questions about the assessment should be directed to the staff member who has set the
task/assessment brief. This will usually be the Module tutor. They will be happy to answer any queries
you have.
Referencing and independent learning (Not applicable for Examination)
Please ensure you reference a range of credible sources, with due attention to the academic literature in
the area. The time spent on research and reading from good quality sources will be reflected in the
quality of your submitted work.
Remember that what you get out of university depends on what you put in. Your teaching sessions
typically represent between 10% and 30% of the time you are expected to study for your degree. A 20credit module represents 200 hours of study time. The rest of your time should be taken up by selfdirected study.
Unless stated otherwise you must use the HARVARD referencing system. Further guidance on
referencing can be found in the on Moodle. Correct referencing is an easy way to improve your marks
and essential in achieving higher grades on most assessments.
Technical submission problems (Not applicable for Examination)
It is strongly advised that you submit your work at least 24 hours before the deadline to allow time to
resolve any last minute problems you might have. If you are having issues with IT or Turnitin you should
contact the IT Helpdesk on (+968) 92841521/ 92841217. You may require evidence of the Helpdesk call
if you are trying to demonstrate that a fault with Turnitin was the cause of a late submission.
Mitigating circumstances
Short extensions on assessment deadlines can be requested in specific circumstances. If you are
encountering particular hardship which has been affecting your studies, then you may be able to apply
for mitigating circumstances. This can give the teachers on your programme more scope to adapt the
assessment requirements to support your needs. Mitigating circumstances policies and procedures are
regularly updated. You should refer to your Academic Advisor for information on extensions and
mitigating circumstances.
Unfair academic practice
Cardiff Met takes issues of unfair practice extremely seriously. The University has procedures and
penalties for dealing with unfair academic practice. These are explained in full in the University’s Unfair
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of 14
Page 12
Practice regulations and procedures under Volume 1, Section 8 of the Academic Handbook. The Module
Leader reserves the right to interview students regarding any aspect of their work submitted for
assessment.
Types of Unfair Practice, include:
Plagiarism, which can be defined as using without acknowledgement another person’s words or ideas
and submitting them for assessment as though it were one’s own work, for instance by copying,
translating from one language to another or unacknowledged paraphrasing. Further examples include:
• Use of any quotation(s) from the published or unpublished work of other persons, whether
published in textbooks, articles, the Web, or in any other format, where quotations have not been
clearly identified as such by being placed in quotation marks and acknowledged.
• Use of another person’s words or ideas that have been slightly changed or paraphrased to make it
look different from the original.
• Summarising another person’s ideas, judgments, diagrams, figures, or computer programmes
without reference to that person in the text and the source in a bibliography/reference list.
• Use of assessment writing services, essay banks and/or any other similar agencies (NB. Students are
commonly being blackmailed after using essay mills).
• Use of unacknowledged material downloaded from the Internet.
• Re-use of one’s own material except as authorised by your degree programme.
Collusion, which can be defined as when work that that has been undertaken with others is submitted
and passed off as solely the work of one person. Modules will clearly identify where joint preparation
and joint submission are permitted, in all other cases they are not.
Fabrication of data, making false claims to have carried out experiments, observations, interviews or
other forms of data collection and analysis, or acting dishonestly in any other way.
How is my work graded?
Gulf College uses Cardiff Metropolitan University’s Generic Band Descriptors (GBD), in conjunction with
programme-specific and/or assessment-specific descriptors that are developed in accordance with the
principles underpinning the generic descriptors, as a reference in marking student work outputs. This is
to ensure that marking is consistent across all Cardiff Met students’ work, including the work outputs of
students in Gulf College.
Assessment marking undergoes a meticulous process to make sure that it is fair and truly reflects the
performance of students in their modules. Marking of work at each level of Cardiff Met degree
programmes are benchmarked against a set of general requirements set out in Cardiff Met’s Guidance
on Assessment Marking.
https://www.cardiffmet.ac.uk/registry/academichandbook/Documents/AH1_04_03.pdf
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To find out more about assessments and key academic skills that can have a significant impact on your
marks, download and read your Module Handbook from Moodle and your Programme Handbook from
the college website.
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