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Real-time Database Sync
1,500 words

Network Security Evaluation and Monitoring – Reconnaissance, Incident Response and APTs

This coursework assesses the research and analytical abilities required to design and evaluate an effective network security evaluation and monitoring solution. The scenario places the student in the role of a network security evaluation specialist responsible for helping a client design and build a monitoring solution for a complex client network. The client operates in the defence and security sector, works with government departments, multinational organisations and foreign agencies, and handles sensitive information. The network includes several server farms, gateway nodes, hundreds of client nodes, internal application services, externally accessible services and wireless access points. The organisation is considered vulnerable to threats such as sabotage and intellectual property theft. The coursework requires all questions to be answered in the given order within a single report. An abstract is not required, and students are expected to use technical terminology precisely. Relevant and clearly labelled illustrations are encouraged. Where assumptions are required about security software, hardware or services already deployed on the network, these assumptions must be clearly identified in a dedicated “Assumptions” section at the beginning of the report. Question 1 focuses on detecting network reconnaissance originating from inside the organisation. Students must explain how an insider could collect and use reconnaissance information for malicious purposes, identify the types of data that should be collected and the appropriate network locations for collection, and justify the selection of monitoring data. The question also requires recommendations for suitable tools and configurations to detect reconnaissance activity, together with strategies for dealing with the scale and high traffic volume of the client network. This section carries 30 marks and has a suggested length of 500 words. Question 2 focuses on incident response following a confirmed security incident. The scenario involves suspicious out-of-hours activity and an external flash drive connected to a workstation at gateway 10, a large number of files being opened on a file server at gateway 9, and significant traffic between the workstation and a database server at gateway 5. Students must determine which previously collected data would be relevant, explain the evidence expected from that data, and recommend additional network and endpoint data that should be collected. The proposed approach must be forensically sound so that evidence can potentially be used in court. This section carries 50 marks and has a suggested length of 700 words. Question 3 addresses Advanced Persistent Threats (APTs) and evaluates the effectiveness of the proposed monitoring solution. Students must recommend appropriate testing to determine whether the monitoring system operates according to its specifications and objectives, explain the types, timing and location of testing, and identify suitable qualifications, certifications, knowledge and tool experience for security testers. The section also requires discussion of APT behaviour and how the proposed monitoring mechanisms could detect or prevent such activity. This section carries 20 marks and has a suggested length of 300 words. Overall, the coursework develops skills in network security monitoring, reconnaissance detection, incident response, digital forensics, security testing and APT detection. It requires students to connect technical monitoring strategies with practical security, legal and operational considerations within a complex organisational network environment.

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Computing and Digital Technologies 3,000 words

Contemporary Computing and Digital Technologies: AI Agents Hackathon Reflective Report

This postgraduate reflective assessment forms part of the Contemporary Computing and Digital Technologies module and is based on experiential learning undertaken through an AI-focused hackathon. The hackathon theme, “AI Agents Unleashed – Building the Future of Automation,” requires MSc students from computing-related disciplines to collaborate on intelligent agent-based solutions capable of automating complex tasks, solving real-world problems and supporting human decision-making. The hackathon encourages students to investigate agent-based system design, intelligent automation and responsible AI development. Potential applications include autonomous cybersecurity monitoring, multi-agent systems for information gathering and decision support, automated data pipelines, intelligent software-development assistants, and conversational systems such as virtual tutors or career coaches. Students may use code-based approaches or platforms such as Flowise, Microsoft Power Automate and Make.com, while more advanced implementations can use technologies including LangChain, AutoGen, Python-based agent SDKs, APIs and large language models. The 3,000-word Individual Reflective Report, worth 70% of the module assessment, evaluates the student's learning and professional development arising from these experiential activities. The first component is a 2,000-word Portfolio of Evidence, requiring evidence-based reflection on participation in the hackathon. Students should evaluate their leadership and teamwork competencies using concrete evidence such as screenshots, code commits and feedback while identifying key lessons for personal and professional development. They must also consider how the experience applies to future research, career development or professional practice. The remaining 1,000 words comprise a Critical Self-Reflection examining the student's personal contribution and achievement of learning-contract goals. Students are expected to critically consider challenges encountered, how those challenges were addressed, lessons learned and their development as effective collaborative team members. Overall, the assessment integrates technical experimentation, reflective practice, teamwork, leadership, professional development and responsible use of emerging AI technologies, supported by a structured portfolio of evidence

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