CTEC 350 intusion and detection

everything in pdf below except video

Programming Project1
You should be submitted in Blackboard CTEC350\ Project1:
Due Date: See the Blackboard
Description: You are required to work in a group of two. Teams will be formed when project is
given, and if it doesn’t work out with your team member you are responsible to work on the
project individually. You should work on this project outside of the class time.
Explanation:
In this project, you will perform machine learning based insider threat detection. You need to
download the open-source code and testing datasets from the given URL (GitHub).
https://github.com/lcd-dal/feature-extraction-for-CERT-insider-threat-test-datasets
Then you need to download and install python3, numpy, pandas, joblib libraries in order to run
the program. Remember you should run this program in Windows 10.
Motivation: Insider threats have always been one of the most severe challenges to cybersecurity.
It can lead to the destruction of the organization’s internal network system and information
leakage.
Project Goals
In this project, you need run the program and plot the accuracy and loss results. Accuracy is
perhaps the best-known Machine Learning model validation method used in classification
problems. Accuracy tells the percentage of accurate predictions. We calculate it by dividing the
number of correct predictions by the total number of predictions. Machines learn by means of a
loss function. It’s a method of evaluating how well specific algorithm models the given data. If
predictions deviate too much from actual results, loss function would cough up a very large
number.
Project Deliverables (SUBMISSION)
1. If your program is working, submit the source code of your program. You must
demonstrate your program is working during the demonstration. Submit the source code,
input file(s), output file(s), and visualizations/results.
2. For this project, the main results will be the plots of accuracy and loss function.
3. Please be sure to include thorough comments throughout your program where necessary.
The comments should explain the code step-by-step during the demonstration.
4. Zip all files or submit all files at once. This should be in report format.
5. Prepare a demonstration of your project for the class. This should be done in PowerPoint
format; 15-20 slides are required.
LATE ASSIGNMENT:
– Any project received after the time of the class is considered late (NO EXCUSES) and
is graded with 0