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Cyber Security / Cloud Management
2,500 words
Cyber Security and Cloud Defence Strategy for ShieldSafe Analytics
This Level 7 Cyber Security for Business and Cloud Management portfolio examines the security challenges faced by ShieldSafe Analytics Ltd., a multinational health analytics organisation specialising in AI-enabled diagnostics and telehealth. The organisation processes high volumes of sensitive patient information, including biometric and genomic data, across hybrid-cloud environments and IoT-enabled healthcare infrastructure. Following a suspected data-exfiltration incident involving anomalous traffic from a diagnostic platform connected to third-party cloud APIs, students are required to evaluate the organisation's information environment and develop appropriate cyber-security and cloud-defence strategies. The first task focuses on information environments and the weaponisation of information. Students identify critical elements of ShieldSafe's information environment, evaluate vulnerabilities associated with the data-exfiltration incident and examine how patient data or analytical systems could be manipulated by malicious actors. Relevant real-world healthcare cyber incidents should be used to support the analysis. The second task examines offensive and defensive Information Operations. Students analyse techniques used by nation-state actors and cybercriminal organisations, including healthcare ransomware incidents such as WannaCry, and compare offensive and defensive approaches. The analysis considers how ShieldSafe can balance these approaches while protecting sensitive data and preserving trust in AI-enabled diagnostic systems. The third task applies Information Operations within legal and ethical boundaries and requires development of a secure cloud migration strategy for ShieldSafe's legacy Electronic Health Record system. The supporting student guide specifically permits students to demonstrate an implementation using Amazon AWS, including IAM users and roles, VPC configuration, security groups, web servers, EC2 instances and AWS migration services. The final task requires a comprehensive cyber-defence strategy, including implementation of Zero Trust Architecture across cloud platforms and analysis of vulnerabilities affecting cyber-physical healthcare systems such as wearable medical devices and diagnostic equipment. Students must propose controls against both remote and local attacks. Overall, the portfolio integrates information operations, healthcare cybersecurity, hybrid-cloud protection, secure migration, Zero Trust, cyber-physical security and strategic cyber defence. The work is produced as a portfolio report using PebblePad and must use Harvard referencing throughout, with appropriate citation of academic sources, images, definitions and external arguments.
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Machine Learning / Cloud Computing
3,984 words
Machine Learning on Cloud: Fraud Detection Model Design, Training and Evaluation
This postgraduate group project focuses on the design, development and critical evaluation of a cloud-based machine learning solution for financial fraud detection. The scenario involves a financial services organisation seeking to detect fraudulent transactions in order to reduce financial losses and improve customer security. Students are required to analyse an appropriate dataset, develop a machine learning solution, evaluate its effectiveness and critically consider the suitability of cloud technologies for deployment. The project begins with a cloud feasibility study, requiring critical comparison of at least two major machine learning platforms such as Microsoft Azure, Amazon Web Services and Google Cloud Platform. Evaluation criteria include performance, scalability, cost, compliance, integration and vendor lock-in, followed by a justified recommendation. Students then conduct exploratory data analysis to identify patterns, anomalies and correlations using appropriate visualisations such as heatmaps, histograms and boxplots. A substantial component addresses data preprocessing and class imbalance. Students are expected to clean and transform the data, apply scaling and encoding, perform feature engineering and investigate approaches such as SMOTE, undersampling and cost-sensitive learning. Each preprocessing choice must be justified in terms of its potential impact on model performance. Students must select and train at least two machine learning models, with suggested approaches including Logistic Regression, Random Forest, XGBoost and Neural Networks. Model development incorporates cross-validation and hyperparameter tuning. Evaluation uses fraud-relevant measures including Precision, Recall, F1 score, AUC and precision-recall curves, supported by confusion matrices, ROC curves and feature-importance visualisations. The project concludes with critical consideration of professional and ethical issues in cloud-based AI, including bias, fairness, transparency, data privacy and sustainability. The overall assessment therefore integrates cloud-platform evaluation, machine learning development, imbalanced classification, model evaluation and responsible AI practice. Overview word count: approximately 340 words.
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Data Science / Deep Learning
3,000 words
Advanced Research Topics (7PAM2016) — Building GANs from Scratch and Applying Them to Medical Imaging, Network Traffic and Sketch Generation
This Masters-level assessment asks for a complete generative adversarial network study, delivered as an annotated code submission carrying sixty per cent of the marks and a six-to-eight page technical report carrying the remaining forty. The work spans four separate GAN implementations, moving from a controlled synthetic setting into three contrasting real-world application domains. Part one builds a GAN from scratch in PyTorch on synthetic two-dimensional data. The tutorial sine-wave generator is reproduced first as a baseline, then a new distribution is modelled — a noisy parametric curve of the form y = sin(2x) + 0.3cos(5x) with an additive noise term — before the architecture itself is varied. Activation functions and layer depth are altered systematically and the resulting sample distributions plotted against the originals, so the effect of each architectural choice on convergence and sample fidelity can be seen rather than asserted. Part two applies the same principles at scale across three domains. The medical strand trains a DCGAN on the OCTMNIST subset of MedMNIST, generating synthetic optical coherence tomography retinal images, tracking generator and discriminator losses across training, and evaluating output both visually and quantitatively using Fréchet Inception Distance. A conditional GAN extension conditions the generator on class label so that images for a chosen retinal pathology can be produced on demand. The cybersecurity strand shifts from images to feature vectors, using preprocessed CICIDS 2017 network intrusion data. Benign and DoS traffic is combined and explored for class balance, a GAN is built to synthesise tabular feature vectors rather than pixels, and real against generated distributions are compared through PCA and t-SNE projections, with a discussion of how well the model generalises across attack types. The creative strand trains a DCGAN on the QuickDraw 'birthday cake' sketch category, tracking visual outputs epoch by epoch and benchmarking generated sketches against real ones, with an extension covering additional categories of differing sketch complexity. The accompanying report explains the analysis steps and the reasoning behind each architectural decision rather than restating textbook definitions of the method. It gives brief descriptions of the models used, presents generated samples and loss curves as figures, interprets the evaluation metrics, and reflects honestly on failure modes — training instability, mode collapse, and the visible flaws in synthetic output that determine whether such data is fit for downstream use. The code is written as reusable functions, commented for a reader other than its author, and reproduces every figure and numerical value quoted in the report.
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Cyber Security for Business and Cloud Management
This activity aims to assess your comprehension of the diverse concepts discussed in this module. You must use the frameworks and concepts covered in this module's delivery to respond to all the tasks below. Scenario ShieldSafe Analytics Ltd. is a fast-growing health analytics company specialising in AI-driven patient diagnostics and telehealth platforms. Operating across multiple countries, the company processes high volumes of real-time patient data, including biometric and genomic records. Due to the increased reliance on remote healthcare and IoT-enabled medical devices, their infrastructure has expanded into hybrid cloud environments. Recently, ShieldSafe experienced a suspected data exfiltration incident involving anomalous traffic from one of its diagnostic platforms integrated with third-party cloud APIs. As a result, executive leadership has raised concerns about the company’s vulnerability to adversarial information operations, particularly in relation to data manipulation, misinformation, and insider threats. As a Junior Cybersecurity Strategist, you’ve been recruited to support the lead cyber intelligence consultant in reviewing vulnerabilities within their information environment, exploring offensive and defensive Information Operations (IO) concepts, and crafting robust cyber defence mechanisms. The leadership also wants to migrate a legacy electronic health record (EHR) system used across its African operations to a more scalable and secure cloud infrastructure. However, concerns exist regarding cross-border data protection laws, insider threats, and the strategic use of information in potential cyber warfare scenarios.
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L7 Cyber Security for Business and Cloud Management
This activity aims to assess your comprehension of the diverse concepts discussed in this module. You must use the frameworks and concepts covered in this module's delivery to respond to all the tasks below. Scenario ShieldSafe Analytics Ltd. is a fast-growing health analytics company specialising in AI-driven patient diagnostics and telehealth platforms. Operating across multiple countries, the company processes high volumes of real-time patient data, including biometric and genomic records. Due to the increased reliance on remote healthcare and IoT-enabled medical devices, their infrastructure has expanded into hybrid cloud environments. Recently, ShieldSafe experienced a suspected data exfiltration incident involving anomalous traffic from one of its diagnostic platforms integrated with third-party cloud APIs. As a result, executive leadership has raised concerns about the company’s vulnerability to adversarial information operations, particularly in relation to data manipulation, misinformation, and insider threats. As a Junior Cybersecurity Strategist, you’ve been recruited to support the lead cyber intelligence consultant in reviewing vulnerabilities within their information environment, exploring offensive and defensive Information Operations (IO) concepts, and crafting robust cyber defence mechanisms. The leadership also wants to migrate a legacy electronic health record (EHR) system used across its African operations to a more scalable and secure cloud infrastructure. However, concerns exist regarding cross-border data protection laws, insider threats, and the strategic use of information in potential cyber warfare scenarios.
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