updated the readme file

README.md

@@ -1,6 +1,6 @@
 ---
 title: Cyberattack Detection ML Approach
-emoji: 💥
+emoji: 🛡️
 colorFrom: red
 colorTo: pink
 sdk: streamlit
@@ -11,4 +11,78 @@ license: mit
 short_description: UNSW Dataset in an ML-based analysis
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# Cyber Attack Detection ML Approach
+
+This Streamlit app provides an interactive analysis of the UNSW-NB15 dataset, a popular benchmark for evaluating network intrusion detection systems. The app leverages machine learning techniques to classify network traffic as either normal or indicative of various attack types.
+
+## About the UNSW-NB15 Dataset
+
+The UNSW-NB15 dataset was created by the Cyber Security Lab at the University of New South Wales, Canberra. It's a comprehensive dataset containing network traffic captures (tcpdump) and system call traces. The dataset includes a variety of modern attack types, making it a valuable resource for training and testing intrusion detection systems. Key features of the dataset include:
+
+- **Diverse Attack Types:** Covers a wide range of attacks such as Fuzzers, Backdoor, DoS, Exploits, Generic, Reconnaissance, Shellcode, and Worms.
+- **Realistic Network Traffic:** Generated using a realistic network environment, simulating real-world scenarios.
+- **Labeled Data:** Each network flow is labeled with its corresponding attack type or as normal traffic, enabling supervised learning.
+
+## App Purpose
+
+This app aims to:
+
+1. **Visualize and Explore the Data:** Provide an interface to view the dataset's structure, data types, and descriptive statistics. This allows users to understand the characteristics of the UNSW-NB15 dataset.
+2. **Train and Evaluate Machine Learning Models:** Implement and compare the performance of several machine learning classifiers, specifically:
+   - Naive Bayes
+   - Decision Tree
+   - K-Nearest Neighbors
+3. **Analyze Model Performance:** Present confusion matrices and classification reports to evaluate the effectiveness of each model in detecting different attack types. This helps users understand the strengths and weaknesses of each algorithm.
+4. **Facilitate Learning:** Serve as an educational tool for learning about network intrusion detection, machine learning classification, and dataset analysis.
+
+## Installation
+
+To run this app, you need to have Python installed along with the following libraries:
+
+- streamlit
+- datasets
+- pandas
+- huggingface_hub
+- scikit-learn
+- seaborn
+- matplotlib
+- numpy
+- Pillow
+
+You can install the required libraries using pip:
+
+```bash
+pip install streamlit datasets pandas huggingface_hub scikit-learn seaborn matplotlib numpy Pillow
+```
+
+## Usage
+
+1. Ensure you have set the `HF_TOKEN` environment variable with your Hugging Face token.
+2. Run the Streamlit app:
+
+```bash
+streamlit run app.py
+```
+
+## Features
+
+- **Dataset Information:** View the dataset's structure, data types, and descriptive statistics.
+- **Naive Bayes Classifier:** Train and evaluate a Naive Bayes model.
+- **Decision Tree Classifier:** Train and evaluate a Decision Tree model.
+- **K-Nearest Neighbor Classifier:** Train and evaluate a K-Nearest Neighbor model.
+- **Confusion Matrix and Classification Report:** Visualize the performance of each model.
+
+## Screenshots
+
+!Cybersecurity
+
+## License
+
+This project is licensed under the MIT License.
+
+## Acknowledgements
+
+- The UNSW-NB15 dataset creators at the University of New South Wales, Canberra.
+- The Hugging Face team for providing the datasets and tools.

app.py

@@ -27,7 +27,7 @@ if not hf_token:
 # Login to Hugging Face Hub
 login(token=hf_token)
 
-st.title("Cyber Attack Detection ML Approach")
+st.title("🛡️📈 Cyber Attack Detection ML Approach")
 st.write("This app loads and analyzes the UNSW_NB15_training-set.csv dataset.")
 
 # Display image