Update yasaswita branch

.vscode/settings.json

@@ -0,0 +1,5 @@
+{
+    "githubPullRequests.ignoredPullRequestBranches": [
+        "main"
+    ]
+}

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2025 SpringBoardMentor193s
+Copyright (c) 2025 yasaswitaraja
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
@@ -9,13 +9,4 @@ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 copies of the Software, and to permit persons to whom the Software is
 furnished to do so, subject to the following conditions:
 
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+[Full MIT License Text here or link to https://opensource.org/licenses/MIT]

README.md

@@ -1,2 +1,287 @@
-# SentinelNet
-The goal of this project is to develop an AI-powered Network Intrusion Detection System (NIDS) capable of identifying malicious network trafic and cyber-attacks in real time. By leveraging machine learning techniques, the system will classify trafic as normal or suspicious based on historical data. 
+<div align="center">
+
+<img src="https://em-content.zobj.net/source/google/387/locked_1f512.png" width="80"/>
+
+# 🔐 SentinelNet
+
+### AI-Powered Network Intrusion Detection System
+
+[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://sentinel-net-ids.streamlit.app/)
+![Python](https://img.shields.io/badge/Python-3.8+-3776AB?style=flat&logo=python&logoColor=white)
+![Scikit-learn](https://img.shields.io/badge/Scikit--learn-ML-F7931E?style=flat&logo=scikit-learn&logoColor=white)
+![Streamlit](https://img.shields.io/badge/Streamlit-Dashboard-FF4B4B?style=flat&logo=streamlit&logoColor=white)
+![License](https://img.shields.io/badge/License-MIT-green?style=flat)
+![Status](https://img.shields.io/badge/Status-Live-brightgreen?style=flat)
+
+> **Detect cyber attacks in network traffic using machine learning — upload a dataset, choose a model, and get instant results.**
+
+[🚀 Live Demo](https://sentinel-net-ids.streamlit.app/) · [📂 Repository](https://github.com/yasaswitaraja/SentinelNet) · [🐛 Report Bug](https://github.com/yasaswitaraja/SentinelNet/issues)
+
+</div>
+
+---
+
+## 📌 Table of Contents
+
+- [About the Project](#-about-the-project)
+- [Live Demo](#-live-demo)
+- [Features](#-features)
+- [Tech Stack](#-tech-stack)
+- [Datasets](#-datasets)
+- [ML Models](#-ml-models)
+- [How It Works](#-how-it-works)
+- [Project Structure](#-project-structure)
+- [Getting Started](#-getting-started)
+- [Deployment](#-deployment)
+- [Attack Categories](#-attack-categories)
+- [Screenshots](#-screenshots)
+- [License](#-license)
+
+---
+
+## 🛡️ About the Project
+
+**SentinelNet** is an AI-powered **Network Intrusion Detection System (NIDS)** that leverages machine learning to classify network traffic as **normal or malicious** in real time.
+
+The system analyzes labeled network traffic datasets, applies multiple ML algorithms, and provides detailed evaluation metrics — all through an interactive Streamlit dashboard. It is built to work with industry-standard cybersecurity datasets like **NSL-KDD** and **CICIDS2017**.
+
+> **Problem it solves:** Traditional rule-based intrusion detection systems struggle with novel attacks. SentinelNet uses ML to learn patterns from historical data and generalize to unseen threats.
+
+---
+
+## 🚀 Live Demo
+
+👉 **[https://sentinel-net-ids.streamlit.app/](https://sentinel-net-ids.streamlit.app/)**
+
+Upload any labeled network traffic CSV and detect intrusions instantly — no setup required.
+
+---
+
+## ✨ Features
+
+| Feature | Description |
+|---|---|
+| 📂 **CSV Upload** | Upload any labeled network traffic dataset |
+| 🤖 **4 ML Models** | Logistic Regression, Random Forest, Decision Tree, Gradient Boosting |
+| 📊 **Evaluation Metrics** | Accuracy, Precision, Recall, F1 Score |
+| 🔢 **Confusion Matrix** | Visual breakdown of TP, FP, TN, FN |
+| 📈 **Feature Importance** | Top 10 most influential features for tree-based models |
+| 📋 **Classification Report** | Per-class precision, recall, F1 |
+| 🔎 **Sample Predictions** | See actual vs predicted labels for test rows |
+| ⚙️ **Configurable Split** | Adjust train/test split ratio from the sidebar |
+| 🎨 **Dark Cyber Theme** | Premium dark UI designed for security professionals |
+| 📱 **Responsive** | Works on desktop and tablets |
+
+---
+
+## 🛠️ Tech Stack
+
+| Layer | Technology |
+|---|---|
+| **Language** | Python 3.8+ |
+| **Web App** | Streamlit |
+| **Machine Learning** | Scikit-learn |
+| **Data Processing** | Pandas, NumPy |
+| **Visualization** | Matplotlib |
+| **Notebooks** | Jupyter Notebook |
+| **Deployment** | Streamlit Community Cloud |
+| **Version Control** | GitHub |
+
+---
+
+## 🗂️ Datasets
+
+SentinelNet is designed to work with two industry-standard NIDS datasets:
+
+### 1. NSL-KDD
+A classic, widely-used benchmark dataset for network intrusion detection research. Contains labeled connection records with 5 traffic categories.
+
+- ✅ Removes duplicate records from KDDCup'99
+- ✅ Balanced class distribution
+- ✅ Standard benchmark for comparing NIDS models
+
+### 2. CICIDS2017
+A modern, realistic dataset generated by the Canadian Institute for Cybersecurity with actual network traffic captures.
+
+- ✅ Contains real PCAP files converted to flow features
+- ✅ Covers modern attack types (DDoS, PortScan, Brute Force)
+- ✅ More representative of current real-world traffic
+
+> Any other labeled CSV dataset with features + a target column will also work.
+
+---
+
+## 🤖 ML Models
+
+| Model | Strength | Best For |
+|---|---|---|
+| **Logistic Regression** | Fast, interpretable baseline | Binary classification, quick benchmarks |
+| **Decision Tree** | Highly interpretable, rule-based | Explainable predictions |
+| **Random Forest** | High accuracy, handles imbalance well | General-purpose, most datasets |
+| **Gradient Boosting** | Best accuracy, handles complex patterns | High-performance detection |
+
+> 💡 **Recommendation:** Random Forest and Gradient Boosting typically give the best results on NIDS datasets due to their ability to handle non-linear patterns and class imbalance.
+
+---
+
+## ⚙️ How It Works
+
+```
+┌─────────────────────────────────────────────────┐
+│              USER UPLOADS CSV DATASET           │
+└─────────────────────┬───────────────────────────┘
+                      │
+                      ▼
+┌─────────────────────────────────────────────────┐
+│              DATA PREPROCESSING                 │
+│  • Encode categorical features (LabelEncoder)  │
+│  • Handle missing values (median imputation)   │
+│  • Encode target labels                         │
+│  • Stratified Train/Test Split (default 80/20) │
+│  • Feature Scaling (StandardScaler)            │
+└─────────────────────┬───────────────────────────┘
+                      │
+                      ▼
+┌─────────────────────────────────────────────────┐
+│              MODEL TRAINING                     │
+│  User selects one of 4 ML models               │
+│  Model trains on scaled training data          │
+└─────────────────────┬───────────────────────────┘
+                      │
+                      ▼
+┌─────────────────────────────────────────────────┐
+│              EVALUATION & VISUALIZATION         │
+│  • Accuracy / Precision / Recall / F1          │
+│  • Confusion Matrix                            │
+│  • Feature Importance Chart                    │
+│  • Sample Predictions Table                    │
+│  • Full Classification Report (optional)       │
+└─────────────────────────────────────────────────┘
+```
+
+---
+
+## 📁 Project Structure
+
+```
+SentinelNet/
+├── app.py                  ← Main Streamlit web application
+├── requirements.txt        ← Python dependencies
+├── README.md               ← Project documentation
+├── notebooks/              ← Jupyter notebooks for EDA & experiments
+│   ├── eda_nslkdd.ipynb
+│   └── eda_cicids2017.ipynb
+├── data/                   ← Dataset storage
+│   ├── NSL-KDD/
+│   └── CICIDS2017/
+├── analysis/               ← Analysis scripts and outputs
+├── outputs/                ← Saved plots and results
+├── script/                 ← Standalone Python scripts
+│   └── main.py
+├── docs/                   ← Additional documentation
+└── documentation/          ← Project reports
+```
+
+---
+
+## 🏁 Getting Started
+
+### Prerequisites
+
+- Python 3.8 or above
+- pip
+
+### Installation
+
+```bash
+# 1. Clone the repository
+git clone https://github.com/yasaswitaraja/SentinelNet.git
+
+# 2. Navigate into the project folder
+cd SentinelNet
+
+# 3. Install dependencies
+pip install -r requirements.txt
+
+# 4. Run the app
+streamlit run app.py
+```
+
+Open **http://localhost:8501** in your browser.
+
+### Quick Test
+
+1. Download the [NSL-KDD dataset](https://www.unb.ca/cic/datasets/nsl.html) or use any labeled CSV
+2. Upload via the sidebar
+3. Select a model
+4. Click **Run Intrusion Detection**
+
+---
+
+## 🌐 Deployment
+
+This project is deployed on **Streamlit Community Cloud** — free hosting for Streamlit apps.
+
+### Deploy Your Own Fork
+
+1. Fork this repository
+2. Go to [share.streamlit.io](https://share.streamlit.io)
+3. Sign in with GitHub
+4. Click **New App** → select your forked repo
+5. Set **Main file path** to `app.py`
+6. Click **Deploy** ✅
+
+The app auto-redeploys on every `git push` to the main branch.
+
+---
+
+## 🎯 Attack Categories (NSL-KDD)
+
+| Category | Full Name | Description | Example |
+|---|---|---|---|
+| 🟢 **Normal** | Normal Traffic | Legitimate network activity | Regular web browsing |
+| 🔴 **DoS** | Denial of Service | Overwhelms target with requests | SYN Flood, Ping of Death |
+| 🟡 **Probe** | Reconnaissance | Scanning for vulnerabilities | Port Scanning, Nmap |
+| 🟣 **R2L** | Remote to Local | Unauthorized remote access attempt | Password brute force |
+| 🟠 **U2R** | User to Root | Privilege escalation attack | Buffer overflow exploit |
+
+---
+
+## 📊 Evaluation Metrics Explained
+
+| Metric | Formula | Why It Matters in NIDS |
+|---|---|---|
+| **Accuracy** | (TP+TN) / Total | Overall correctness |
+| **Precision** | TP / (TP+FP) | Fewer false alarms |
+| **Recall** | TP / (TP+FN) | Fewer missed attacks ⚠️ Most critical |
+| **F1 Score** | 2 × (P×R)/(P+R) | Balanced metric for imbalanced data |
+
+> ⚠️ **In cybersecurity, Recall is the most critical metric.** Missing a real attack (False Negative) is far more dangerous than a false alarm (False Positive).
+
+---
+
+## 🙏 Credits & Acknowledgements
+
+- Inspired by **[Meta SeamlessM4T](https://github.com/facebookresearch/seamless_communication)** research on AI-powered detection
+- Dataset: **[NSL-KDD](https://www.unb.ca/cic/datasets/nsl.html)** — University of New Brunswick
+- Dataset: **[CICIDS2017](https://www.unb.ca/cic/datasets/ids-2017.html)** — Canadian Institute for Cybersecurity
+- Forked from: **[SpringBoardMentor193s/SentinelNet](https://github.com/SpringBoardMentor193s/SentinelNet)**
+- Built with ❤️ using [Streamlit](https://streamlit.io) + [Scikit-learn](https://scikit-learn.org)
+
+---
+
+## 📄 License
+
+Distributed under the **MIT License**. See `LICENSE` for more information.
+
+---
+
+<div align="center">
+
+Made with ❤️ by [yasaswitaraja](https://github.com/yasaswitaraja)
+
+⭐ **Star this repo if you found it useful!** ⭐
+
+**[🚀 Live Demo](https://sentinel-net-ids.streamlit.app/)** · **[📂 GitHub](https://github.com/yasaswitaraja/SentinelNet)**
+
+</div>