Anamoly Detection | About Us

About the Anomaly Detection App

Who We Are - Find Out About Our Team and Vision.

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Project Overview

In today's connected world, IoT (Internet of Things) networks are increasingly becoming targets for cyber-attacks. This project aims to develop a machine learning model to predict and identify potential attacks in IoT networks, thus helping to secure these networks from malicious activities.

Functionality

This application provides the following key functionalities:

1. Dataset Display

• The application displays two primary datasets: KDDTrain+.txt and KDDTest+.txt.
• These datasets are used to train and test the anomaly detection model.
• You can view the structure and content of these datasets on the Dataset Display page.

2. Anomaly Detection

• Users can input various features of network traffic to predict whether the traffic is normal or an attack.
• The model classifies attacks into different categories such as DOS, Probe, U2R, and Sybil.

3. Model Evaluation

• The application provides metrics such as accuracy, precision, and recall to evaluate the performance of the model.
• Users can see how well the model performs in distinguishing between normal and malicious network traffic.

4. Visualization

• The project includes various visualizations to help understand the data and the model's performance.
• These visualizations include confusion matrices and other relevant charts.

Vision

Our vision is to create a robust and efficient system for detecting anomalies in IoT networks, ensuring the security and integrity of connected devices. By leveraging machine learning, we aim to provide a tool that can help in early detection and prevention of cyber-attacks, thus safeguarding sensitive information and maintaining the smooth operation of IoT systems.

Team

• Gaurav Borse
• Shubham Thorat
• Urmila Narvade
• Vaishnavi Pratale

Technical Details

• Programming Language: Python
• Framework: Streamlit for the frontend, scikit-learn for machine learning
• Datasets: KDDTrain+.txt and KDDTest+.txt, which are standard datasets used for network intrusion detection.

Contact Us

If you have any questions or suggestions, please contact us at [[email protected]].

For more details, visit our GitHub repository.

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Steps to run

Watch the tutorial video

How to Make a Multi-Page Web App | Streamlit #16

Demo Video

Launch the web app:

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Commands

Reproducing this web app

To recreate this web app on your own computer, do the following.

Create conda environment

Firstly, we will create a conda environment called multipage

conda create -n multipage python=3.7.9

Secondly, we will login to the multipage environement

conda activate multipage

Install prerequisite libraries

Download requirements.txt file

wget https://raw.githubusercontent.com/dataprofessor/ml-auto-app/main/requirements.txt

Pip install libraries

pip install -r requirements.txt

Download and unzip this repo

Download this repo and unzip as your working directory.

Launch the app

streamlit run app.py

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Output