🚨 WiFi Anomaly Detection System 🔐

A real-time anomaly detection web application that identifies suspicious WiFi traffic using Machine Learning and displays live results on an interactive dashboard.

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🧠 ML Model

We trained an XGBoost Classifier on the CSE-CIC-IDS 2018 dataset, specifically on one day's worth of WiFi traffic (02-15-2018.csv) due to dataset size (~6GB+).

✅ Model Highlights:

• Algorithm: XGBoost
• Accuracy: 99.67%
• Binary Classification: Normal (0) vs Anomaly (1)
• Features Used: Flow Duration, Packet Stats, IAT Mean/Std, etc.

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🧰 Tech Stack

Layer	Tools Used
🧠 Model	Python, Scikit-learn, XGBoost
🌐 Backend	Flask (REST API)
📊 Dashboard	HTML/CSS, JS (optional extension)

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