Telemetry Analyzer

A robust tool for analyzing telemetry data, leveraging machine learning to identify patterns, anomalies, and critical insights within various system metrics.

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Table of Contents

• About the Project
• Features
• How the ML Model Works
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Project Structure
• Contributing
• License
• Contact
• Acknowledgements

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About the Project

This repository contains the code for the Telemetry Analyzer, a project designed to automatically process and analyze high-volume telemetry streams from various sources like IoT devices, servers, or network infrastructure. It utilizes advanced machine learning techniques to automate the identification of anomalous behavior, performance bottlenecks, and resource utilization trends, thereby providing valuable insights into the operational health and stability of monitored systems.

The core problem this project aims to solve is the overwhelming challenge of manually sifting through vast amounts of telemetry data to identify critical events or deviations from normal operation. By automating this process, the Telemetry Analyzer helps reduce downtime, improve system reliability, and enable proactive problem-solving.

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Features

• Automated Data Ingestion: Seamlessly ingest telemetry data from various formats like CSV, JSON, or direct streams.
• Real-time Anomaly Detection: Apply a trained machine learning model to detect anomalies in incoming telemetry data as it arrives.
• Comprehensive Feature Engineering: Automatically derive meaningful features from raw telemetry to enhance model performance.
• Intuitive Data Visualization: Generate interactive plots and dashboards to visualize telemetry trends and highlight detected anomalies.
• Configurable Alerting System: Set up alerts for critical anomalies, integrating with common notification services (e.g., Slack, email).
• Scalable Architecture: Designed to handle large volumes of telemetry data efficiently.

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How the ML Model Works

The machine learning model at the heart of this Telemetry Analyzer is responsible for identifying deviations from normal operating patterns within continuous telemetry streams. It is primarily focused on unsupervised anomaly detection.

Model Type

The primary model used for anomaly detection in this project is an Isolation Forest. This algorithm is particularly effective for high-dimensional datasets and works by isolating anomalies rather than profiling normal data.

Training Data

The model is trained on historical telemetry data representing known "normal" system behavior. It's crucial that this training dataset is clean and representative of the system's healthy state, ideally without significant anomalies. The training dataset typically includes features such as:

• timestamp: The time of the telemetry reading.
• cpu_usage_percent: Percentage of CPU utilized.
• memory_free_mb: Free memory in megabytes.
• disk_io_rate_mbps: Disk I/O rate in MB/s.
• network_latency_ms: Network latency in milliseconds.
• error_count: Number of errors reported.

Feature Engineering

Before feeding data to the Isolation Forest model, the following feature engineering steps are performed to enhance its ability to detect subtle anomalies:

1. Timestamp Conversion: Convert raw timestamps into numerical features like epoch time or relative time from start.
2. Lagged Features: Create features representing the value of a metric from previous time steps (e.g., cpu_usage_percent_lag1).
3. Rolling Statistics: Calculate rolling averages, standard deviations, min, and max values over a defined window (e.g., 5-minute rolling average of cpu_usage_percent).
4. Difference Features: Compute the difference between current and previous values to capture rate of change.
5. Normalization/Standardization: Scale numerical features to a common range (e.g., using StandardScaler) to prevent features with larger magnitudes from dominating the model.

Model Architecture and Training Process

The Isolation Forest model does not require explicit "training" in the traditional supervised learning sense with labeled anomalies. Instead, it builds an ensemble of isolation trees. Each tree is built by recursively partitioning the dataset based on random feature selections and random split points. Anomalies, being "few and different," are typically isolated closer to the root of these trees with fewer splits, while normal points require more splits to be isolated.

The training process involves:

• Dataset Sampling: Training on a representative sample of normal telemetry data.
• Tree Construction: Building a specified number of isolation trees (n_estimators, e.g., 100 or 200).
• Contamination Parameter: A contamination parameter is set (e.g., 0.01 for 1% expected anomalies) which helps in setting the threshold for anomaly scores.

Anomaly Detection/Prediction Logic

Once the Isolation Forest model is "trained" on normal data, it operates as follows for new incoming telemetry:

1. Data Ingestion: New telemetry data points are ingested, typically in a streaming fashion or from batch files.
2. Preprocessing: The same feature engineering steps (timestamp conversion, lagged features, rolling statistics, normalization) applied during training are rigorously applied to the new data.
3. Anomaly Scoring: The preprocessed new data is fed into the trained Isolation Forest model. The model computes an anomaly score for each data point. This score reflects how "isolated" a data point is; lower scores indicate a higher likelihood of being an anomaly.
4. Thresholding and Classification: Based on the contamination parameter set during training (or a manually defined threshold), a decision boundary is determined. If the anomaly score for a data point falls below this threshold, it is classified and flagged as an anomaly.
5. Reporting and Alerting: Detected anomalies are logged, reported, and can trigger alerts via integrated notification systems.

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Getting Started

To get a copy of the project up and running on your local machine for development and testing purposes, follow these simple steps.

Prerequisites

Before you begin, ensure you have the following installed:

• Python 3.9+: It's highly recommended to use a modern Python version.
• pip: Python package installer, usually comes with Python.
• Git: For cloning the repository.

Installation

1. Clone the repository:

   git clone [https://github.com/Llalithsaikumar/telemetry_Analyzer.git](https://github.com/Llalithsaikumar/telemetry_Analyzer.git)
   cd telemetry_Analyzer

2. Create and activate a virtual environment (highly recommended): A virtual environment isolates your project's dependencies from your system's Python packages.

   python -m venv venv
   # On Windows:
   .\venv\Scripts\activate
   # On macOS/Linux:
   source venv/bin/activate

3. Install the required dependencies: This project relies on a requirements.txt file for its dependencies.

   pip install -r requirements.txt

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Usage

This section details how to run and interact with the Telemetry Analyzer.

Running the Anomaly Detector

The main entry point for running the telemetry analysis and anomaly detection is main.py. You'll typically provide an input telemetry data file and an output directory for results.

python main.py --input_file data/raw/sample_telemetry_data.csv --output_dir results/anomaly_reports