Network Intrusion Detection System, SNS'2021

🕵️ Introduction

Develop a network intrusion detection system Implement IDS with machine learning.

💾 Dataset

📁 Files

Following files are available in the resources section:

• full.csv - This CSV contains contains training dataset containing.

• test.csv - This CSV will be used for actual evaluation for the leaderboard!

🚀 Submission

• Prepare a csv containing the predicted csv with label as the target.
• Name of the above file should be submission.csv.
• Sample submission format available at sample_submission.csv in the resorces section.

Make your first submission here 🚀 !!

🖊 Evaluation Criteria

During evaluation the F1 score will be calcuated over all the testing images.

Assignment Details

Note1:- Number beside the question denotes the number allocated to that question. Full marks is 100. You may either get 0 or full marks for each part of the question.No partial marks are there.Good luck 

Note2:- It is strongly recommended that no student is allowed to copy programs from others. Hence, if there is any duplicate in the assignment, simply both the parties will be given zero marks without any compromise. Rest of assignments will not be evaluated further and assignment marks will not be considered towards final grading in the course. No assignment will be taken after deadline. Please upload in code along with a README file in the course moodle portal through a ZIP file (Groupnumber-Lab4.zip). 

Marks Distribution

• use at least 3 different ML algorithm and compare their performance 20 
• Performance metrics should include precision,recall,f1 score, accuracy. 30 
• Now use your best algorithm to predict the label of test.csv 50 

Submission Details for Moodle

• submit full report in pdf(convert ipynb to pdf) , group contribution report.
• your folder should contain 3 files - report.pdf , group-contri.pdf
• please be sure that your submission follow the above points else you may get less marks as it will evaluated automatically 

• 📱 Contact

• Ankush Mitra