Anomaly Detection in IoT Devices

This project uses machine learning algorithms to detect anomalies in data generated by IoT devices. The goal is to identify unusual behaviors or malfunctions that could indicate security breaches, device failures, or performance issues.

Features

• Real-time anomaly detection in IoT device data streams.
• Identifies unusual patterns and potential device failures.
• Utilizes unsupervised learning models like Autoencoders, Isolation Forest, and LSTM networks.
• Provides alerts and detailed insights for detected anomalies.

Installation

To set up the Anomaly Detection system on your local machine, follow these steps:

1. Clone the Repository

   git clone https://github.com/username/iot-anomaly-detection.git
   cd iot-anomaly-detection

2. Set Up Virtual Environment (Optional but Recommended)

   python3 -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install Required Packages

   pip install -r requirements.txt

Usage

To start monitoring IoT data for anomalies, run the main script:

python app.py

Analyze device data in real-time and receive alerts for detected anomalies.

Results

The system effectively identifies anomalies in IoT device data, providing timely alerts that help in preventing potential failures and security breaches.