/IoT_Anomalies_Detection_Using_Deep_Learning

Primary LanguageJupyter Notebook

Real-Time Anomaly Detection System

Overview

This project is designed to monitor real-time humidity and temperature data captured by IoT sensors and detecting any abnormal behavior that could indicate a failure or intrusion into the system and sending SMS alerts if anomalies are detected. The project leverages a Temporal Convolutional Network (TCN) model to make predictions and identify anomalies.

Features

  • Real-time data monitoring
  • Anomaly detection using a TCN model
  • SMS alerts for detected anomalies
  • Data standardization and preprocessing
  • Continuous operation with periodic checks
  • Data Preparation
  • Explore and Visualize data - Time Serie Problem
  • Train a Deep Learning model
  • Communicate results

Requirements

  • Python 3.x
  • TensorFlow
  • Keras
  • pymysql
  • numpy
  • pandas
  • scikit-learn
  • twilio

Set up the database:

  • Ensure you have a MySQL server running.
  • Create a database named sensors_data.
  • Create a table named sensors with columns for humidity, temperature, and time.

Contribution

Feel free to fork this repository and submit pull requests. For major changes, please open an issue first to discuss what you would like to change.