Malware Identification - Supervised Learning - Creating a Model to Detect Malware Using Supervised Learning Algorithms - Computer Science Assignment Help

Assignment Task:

SCENARIO 

Following your initial consultation with N00BIoT, the software development team has extracted data sets based upon your recommendations. 

N00BIoT intends to launch a new version of Email Sentry at the end of the year. It will be marketed as N00BIoT ES2 (Powered By AI). 

The software team is scrambling to produce a reliable email detector and has turned to you to provide the machine learning expertise and analysis to deliver a product with the following goals: 

• Very low false-positives on malware detection 

• High level of sensitivity in detecting malware. 

TASK 

You are to apply supervised machine learning algorithms to the data provided. You will train your ML model using the MalwareSample set, and then test them against the EmailSamples data set. 

All analyses are to be done using R. You will report on your findings. 

Part 1 – Preparing your data for constructing a supervised learning model using MalwareSamples10000.csv 

You will need to write the appropriate code to, 

i. Import the dataset MalwareSamples10000.csv into R studio.

ii. Set the random seed using your student ID.

iii. Partition the data into training and test sets using an 80/20 split. 

The variable isMalware is the classification label and the outcome variable. 

Part 2 – Evaluating your supervised learning models

a) Select three supervised learning modelling algorithms to test against one another by running the following code. Make sure you enter your student ID into the command set.seed(.). Your 3 modelling approaches are given by myModels. 

library(dplyr) set.seed(Enter your student ID) models.list1 <- c("Logistic Ridge Regression", "Logistic LASSO Regression", 

"Logistic Elastic-Net Regression") models.list2 <- c("Classification Tree", 

"Bagging Tree", "Random Forest") myModels <- c("Binary Logistic Regression", 

sample(models.list1,size=1), sample(models.list2,size=1)) myModels %>% data.frame 

b) For each of your supervised learning approaches you will need to: 

i. Run the algorithm in R on the training set.

ii. optimise the parameter(s) of the model (except for binary logistic regression modelling). 

iii. Evaluate the predictive performance of the model on the test set, and provide the confusion matrix for the estimates/predictions, along with the sensitivity, specificity and accuracy of the model.

iv. Perform recursive feature elimination (RFE) on the logistic regression model (only) to ensure the model is not overfitted. See Workshop 5 for an example, except in this instance, specify the argument function=lrFuncs in the rfeControl(.) command instead.

c) For the logistic regression model, report on the RFE process and the final logistic regression model, including information on which k-fold CV was used, and the number of repeated CV if using repeatedcv.

d) For the other two models, report how the models are tuned, including information on search range(s) for the tuning parameter(s), which k-fold CV was used, and the number of repeated CVs (if applicable), and the final optimal tuning parameter values and relevant CV statistics (where appropriate).

e) Report on the predictive performances of the three models and how they compare to each other. 

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