Question 1 [ 10 Marks]
(a} Explain the difference between Supervised Machine Learning and Unsupervised Machine
Learning by giving one example of a technique for each.
[8 marks]
(b) What would you summarise as the role of text mining in Security Analytics. [2 marks]
Question 2 [ 10 Marks]
(a) R is a full featured, object-oriented programming language, which is more than a scripting
language to perform statistical calculations, besides the description provided so far, what
makes Ra very flexible and powerful tool for data analysts.
[4 marks]
(b) What does it mean to say Python is an interpreted programming language? [2 marks]
(c) Give and explain any two other features that makes Python more ideal for data analytics?
[4 marks]
Question 3 [10 marks]
The dataset X = {xl, ... , x/+u } is divided into two sets, XL and XU. That is, X = XL U XU, where the
points in XL= {xl, ...,x/} are provided with the labels from YL ={yl, ...,yl}, and for the points in XU=
{x/+1, ... , xl+u }, the labels are not known.
(a) What Machine Learning model is presented here?
[ 2 marks]
(b) Give two reasons why you classified the model as such in (a).
[ 4 marks]
(c) which cybersecurity scenario would be ideal to apply the model you identified in (a) and
why?
[4 marks]
Question 4 [10 marks]
Machine Learning (ML) techniques can analyse threats and respond to attacks and security
incidents quickly in an automated way. Give and explain any five cybersecurity problems where
ML techniques could be applied
[10 marks]
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