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PROJECT


Detect Cyber Intrusion Using Machine Learning

In this project, we’ll implement machine learning based classifiers to detect and classify several types of cyber attacks.

Detect Cyber Intrusion Using Machine Learning

You will learn to:

Clean network traffic by removing redundancies.

Create data visualizations in Python.

Create machine learning based classifiers to detect cyber attacks.

Evaluate the accuracy of machine learning based classifiers.

Skills

Machine Learning

Data Science

Cyber Security

Intrusion Detection

Prerequisites

Basic knowledge of Python programming

Basic understanding of machine learning

Basic knowledge of plotting

Technologies

NumPy

Python

Pandas

Matplotlib

Scikit-learn

Project Description

A cyber attack happens every 39 seconds. An intrusion detection system acts as the first line of defense to detect these attacks. In this project, we’ll implement machine learning based classifiers that can accurately detect and classify several types of cyber attacks. The classifiers will learn patterns of benign and malicious activities from existing network traffic datasets. Using this learning, the classifiers will detect and flag malicious intrusions.

In this project, we’ll use SIMARGL2021, a publicly available dataset that contains benign and malicious network traffic. Firstly, we’ll explore the dataset to understand its basics, such as the number of features, type of attacks, and redundancy in the data. Next, we’ll visualize the data to understand the different labels and their proportion in the datasets. Then, we’ll train and test machine learning models using multiple classifiers such as random forest, decision tree, and Gaussian Naive Bayes. Finally, we’ll assess the accuracy of the trained classifiers.

Project Tasks

1

Data Preprocessing

Task 0: Get Started

Task 1: Import Libraries and Modules

Task 2: Preprocess the Dataset

Task 3: Explore the Dataset

Task 4: Standardize and Encode the Data

Task 5: Separate Labels and Split the Data into Train and Test Subsets

2

Train and Test Random Forest

Task 6: Train the Random Forest Classifier

Task 7: Test the Random Forest Classifier

3

Train and Test Decision Tree

Task 8: Train the Decision Tree Classifier

Task 9: Test the Decision Tree Classifier

4

Train and Test Naive Bayes

Task 10: Train the Naive Bayes Classifier

Task 11: Test the Naive Bayes Classifier

5

Compare Attack Detection Capability

Task 12: Compare the Accuracy and Training Times

Congratulations!