CN7031 Big Data Analytics Assessment Brief 2026 | UEL

Resit Submission instructions

• Cover sheet to be attached to the front of the assignment whensubmitted
• Question paper to be attached to assignment when submitted
• All pages to be numbered sequentially

Dear students,

This is your Resit Assessment. Prior attempting this assignment, please carefully read relevant materials found in the Module Revision Material of the course shell.

Assessment Resit Submission Rules and Important Notes:

1. You are able to use the feedback and constructive comments provided by your tutor in order to improve/enhance your work. Please ensure that you work on your initial piece of assignment and improve it based on the feedback received.
   
2. During the resit period you are given the opportunity to revise and resubmit originally failed module assessment(s), but no further academic instruction will be provided. However, you are able to use the feedback and constructive comments provided by your tutor in order to improve/enhance your work.
3. Your assessment should be submitted via the appropriate VLE Submission Link by 11:59 PM (VLE) time, at the end of the resit week, the specific date of which has been provided to you on the day your access was granted to the resit module.
   
   
4. Assignments submitted up to 24 hours late will be accepted, but the assignment mark will be subject to a deduction of 5 marks from the mark awarded. Work submitted more than 24 hours late after the submission deadline will be recorded as 0%
   
5. We are here to help and support you during the resit period, so if you need any nontutor, technical assistance for any issues affecting your ability to submit your resit assignment please contact the Resubmission Services via resubmission@unicaf.org as a first step to getting in touch and allow 48 hours for us to answer you before moving on to Student Support.
   
   
6. The maximum mark attainable for the components upon reassessment will be 50%. Please write your solutions clearly and concisely. If you do not explain your answer you will be given no credit. You must write your own solution. Copying someone else's solution will be considered plagiarism and may result in failing the whole course.

This coursework (CRWK) must be attempted as an individual work. This coursework is divided into two sections: (1) Big Data analytics on a real case study and (2) presentation.

Overall mark for CRWK comes from two main activities as follows:

1- Big Data Analytics report (around 5,000 words, with a tolerance of ± 10%) (60%)
2- Presentation (around 1000 words, with a tolerance of ± 10%) (40%)

Marking Scheme Big Data Analytics report

Big Data Analytics using Hadoop and Spark UEL-CN-7031 - Big Data Analytics

Tasks:

(1) Understanding Dataset: UNSW-NB15

The raw network packets of the UNSW- NB15 dataset was created by the IXIA PerfectStorm tool in the Cyber Range Lab of the Australian Centre for Cyber Security
(ACCS) for generating a hybrid of real modern normal activities and synthetic contemporary attack behaviours. Tcpdump tool used to capture 100 GB of the raw traffic (e.g., Pcap files). This data set has nine types of attacks, namely, Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnaissance, Shellcode and Worms. The Argus and Bro-IDS tools are used and twelve algorithms are developed to generate totally 49 features with the class label.

a) The features are described here.

b) The number of attacks and their sub-categories is described here.

c) In this coursework, we use the total number of 10-million records that was stored in the CSV file (download). The total size is about 600MB, which is big enough to employ big data methodologies for analytics. As a big data specialist, firstly, we would like to read and understand its features, then apply modeling techniques. If you want to see a few records of this dataset, you can import it into Hadoop HDFS, then make a Hive query for printing the first 5-10 records for your understanding.

(2) Big Data Query & Analysis by Apache Hive [30 marks]

This task is using Apache Hive for converting big raw data into useful information for the end users. To do so, firstly understand the dataset carefully. Then, make at least 4 Hive queries (refer to the marking scheme). Apply appropriate visualization tools to present your findings numerically and graphically. Interpret shortly your findings.

Finally, take screenshot of your outcomes (e.g., tables and plots) together with the scripts/queries into the report.

(3) Advanced Analytics using PySpark [50 marks]

In this section, you will conduct advanced analytics using PySpark.

3.1. Analyze and Interpret Big Data (15 marks)

We need to learn and understand the data through at least 4 analytical methods (descriptive statistics correlation. hypothesis testing, density estimation, etc.). You need to present your work numerically and graphically. Apply tooltip text, legend, title, X-Y labels etc. accordingly to help end- users for getting insights.

3.2. Design and Build a Classifier (35 marks)

a) Design and build a binary classifier over the dataset. Explain your algorithm and its configuration. Explain your findings into both numerical and graphical representations. Evaluate the performance of the model and verify the accuracy and the effectiveness of your model. [15 marks]

b) Apply a multi-class classifier to classify data into ten classes (categories): one normal and nine attacks (e.g., Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnaissance, Shellcode and Worms). Briefly explain your model with supportive statements on its parameters, accuracy and effectiveness. [20 marks]

(4) Individual Assessment [10 marks]

Discuss (1) what other alternative technologies are available for tasks 2 and 3 and how they are differ (use academic references), and (2) what was surprisingly new thinking evoked and/or neglected at your end?

(5) Documentation [10 marks]

Document all your work. Your final report must follow 5 sections detailed in the "format of final submission" section (refer to the next page). Your work must demonstrate appropriate understanding of academic writing and integrity.