Anamoly Detection | About Us
Who We Are - Find Out About Our Team and Vision.
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.
This application provides the following key functionalities:
- The application displays two primary datasets:
KDDTrain+.txt
andKDDTest+.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.
- 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.
- 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.
- The project includes various visualizations to help understand the data and the model's performance.
- These visualizations include confusion matrices and other relevant charts.
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.
- Gaurav Borse
- Shubham Thorat
- Urmila Narvade
- Vaishnavi Pratale
- Programming Language: Python
- Framework: Streamlit for the frontend, scikit-learn for machine learning
- Datasets:
KDDTrain+.txt
andKDDTest+.txt
, which are standard datasets used for network intrusion detection.
If you have any questions or suggestions, please contact us at [gborse108@gmail.com].
For more details, visit our GitHub repository.
How to Make a Multi-Page Web App | Streamlit #16
Launch the web app:
To recreate this web app on your own computer, do the following.
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
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 this repo and unzip as your working directory.
streamlit run app.py