Creating a Model to Detect Malware using Supervised Learning Algorithms - Engineering Assignment Help

Assignment Task :

Background 

N00BIoT Email Sentry 2.0 N00BIoT’s Email Sentry is a malware detection platform. The first version of Email Sentry wasn’t particularly effective, so the N00BIoT commissioned you as an expert in machine learning. An early phase of the project used principal component analysis to determine if there were specific factors about emails that could help to identify malicious emails. 

Based on this the N00BIoT software team tried to further refine their malware classification system. The results were still underwhelming. 

The decision has been made to explore further supervised learning models to create a more effective malware classifier. 

MalwareSamples Data The programming team has again provided you with email data. Two sets of data are provided to help with your investigation of the accuracy of various supervised learning models. 

The first data file MalwareSamples10000.csv is a curated dataset. The data are sampled from emails such that approximately 50% of the data contain malware samples, and 50% of the data are from legitimate emails. This data set may be used for training of your machine learning models. 

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.

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