Project Title : Anomaly Detection in IoT Systems

Objective:
To develop a machine learning model that can automatically detect unusual behavior or anomalies in IoT (Internet of Things) systems, such as sensor data from smart devices, to identify potential issues, errors, or security threats.

What It Does:
The system monitors real-time data from IoT devices (like sensors, smart thermostats, wearables) and flags data points that deviate significantly from normal behavior, helping in predictive maintenance and fraud detection.

Key Concepts:

Anomaly Detection: Identifying patterns in data that do not conform to expected behavior.

Unsupervised Learning: No labeled data, so the model learns to detect anomalies by itself.

Time-Series Analysis: Often used since IoT data is sequential (e.g., sensor readings over time).

Steps Involved:

Dataset Collection:

Use public IoT datasets like Kaggle IoT-23, NAB (Numenta Anomaly Benchmark), or your own data from IoT devices.

Data can include temperature readings, device status, GPS coordinates, etc.

Preprocessing:

Clean data by handling missing values, outliers, or noise.

Normalize or standardize data to ensure consistency.

For time-series data, perform techniques like resampling or smoothing.

Feature Engineering:

Extract features from raw sensor data, such as moving averages, standard deviations, or time-based features (e.g., day of the week).

Use domain knowledge to create relevant features.

Model Building:

Traditional ML Models: Isolation Forest, One-Class SVM, k-Means clustering for anomaly detection.

Deep Learning Models: Autoencoders, LSTM-based models for detecting anomalies in time-series data.

Statistical Methods: Z-Score, ARIMA, or moving average models for detecting deviations.

Model Evaluation:

Use metrics like precision, recall, F1-score, or AUC-ROC for assessing how well the model detects anomalies.

Evaluate with both synthetic anomaly data and real-world cases.

Deployment (Optional):

Integrate the anomaly detection model into the IoT system for real-time monitoring.

Alert system for users or operators when an anomaly is detected, possibly triggering maintenance or investigation.

Applications:

Predictive Maintenance: Detect device malfunctions before they fail.

Security: Identify unusual access patterns or security breaches in IoT devices.

Quality Control: Ensure sensor readings are within expected ranges in industrial IoT systems.

Smart Homes/Health: Detect abnormal behavior in smart home systems or health monitoring devices.

Tools & Technologies:

Languages: Python

Libraries: Scikit-learn, TensorFlow/Keras, PyTorch, Pandas, NumPy, Matplotlib

Platforms: Jupyter Notebooks, Google Colab, IoT platforms (e.g., ThingSpeak, AWS IoT Core)