Android Traffic Analysis 🚦

Welcome to the Android Traffic Analysis repository! This project focuses on detecting Android malware by analyzing network traffic patterns within a telecommunications context. We utilize statistical hypothesis testing and data visualization to evaluate various traffic features, such as DNS query times, TCP packets, and volume bytes. Our goal is to distinguish between benign and malicious Android applications effectively.

Table of Contents

1. Introduction
2. Project Overview
3. Features
4. Technologies Used
5. Installation
6. Usage
7. Data Analysis
8. Data Visualization
9. Statistical Hypothesis Testing
10. Contributing
11. License
12. Contact

Introduction

In today’s digital landscape, Android devices are ubiquitous, making them a prime target for malware. This project aims to enhance cybersecurity by providing tools to analyze network traffic and identify potential threats. By leveraging data science techniques, we can create a safer environment for Android users.

Project Overview

The Android Traffic Analysis project combines network security with data analysis. We focus on:

• Analyzing network traffic patterns.
• Using statistical methods to evaluate traffic features.
• Visualizing data for better understanding and insights.

This project serves as a vital resource for researchers and developers interested in malware detection and network security.

Features

• Malware Detection: Identify malicious Android applications through network traffic analysis.
• Statistical Analysis: Use hypothesis testing to evaluate the significance of traffic features.
• Data Visualization: Create informative visual representations of traffic data.
• Comprehensive Documentation: Detailed instructions and explanations to facilitate understanding and usage.

Technologies Used

This project utilizes various technologies and libraries, including:

• Python: The primary programming language for analysis and visualization.
• NumPy: For numerical operations and data manipulation.
• Pandas: For data analysis and handling structured data.
• SciPy: For advanced statistical computations.
• Matplotlib: For creating static, animated, and interactive visualizations.
• Seaborn: For statistical data visualization.

Additional topics include:

• Android
• Cybersecurity
• Data Analysis
• Data Science
• Network Security
• Telecommunications

Installation

To set up the project on your local machine, follow these steps:

1. Clone the Repository:

   git clone https://github.com/minhtungonep/android-traffic-analysis.git
   cd android-traffic-analysis

2. Install Required Libraries: Ensure you have Python installed. Then, install the necessary libraries using pip:

   pip install numpy pandas scipy matplotlib seaborn

3. Download Releases: Visit the Releases section to download the latest release files. Execute the files as instructed in the documentation.

Usage

Once you have installed the project and its dependencies, you can start analyzing network traffic. The main script to run is analyze_traffic.py. Use the following command:

python analyze_traffic.py

This script will guide you through the analysis process, allowing you to input your traffic data and receive results based on the analysis.

Data Analysis

Data analysis is at the core of this project. We focus on key traffic features:

• DNS Query Times: Measure the time taken for DNS queries to complete.
• TCP Packets: Analyze the number of TCP packets sent and received.
• Volume Bytes: Evaluate the total volume of data transferred.

By comparing these features between benign and malicious applications, we can identify patterns that suggest malicious behavior.

Data Visualization

Data visualization plays a crucial role in understanding traffic patterns. We use Matplotlib and Seaborn to create various types of plots, including:

• Histograms: To show the distribution of DNS query times.
• Box Plots: To compare the volume of bytes transferred between benign and malicious apps.
• Scatter Plots: To visualize relationships between different traffic features.

Visualizations help in quickly identifying trends and anomalies in the data.

Statistical Hypothesis Testing

Statistical hypothesis testing allows us to determine if the observed differences in traffic features are significant. We use tests such as:

• t-tests: To compare means between two groups.
• Chi-square tests: To evaluate the relationship between categorical variables.

By applying these tests, we can confidently make inferences about the data and its implications for malware detection.

Contributing

We welcome contributions from the community. If you wish to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Commit your changes and push to your branch.
4. Open a pull request detailing your changes.

Please ensure your code adheres to the existing style and includes appropriate tests.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For questions or feedback, please reach out to the project maintainer:

• Name: Minh Tung
• Email: minhtung@example.com

Feel free to visit the Releases section for updates and downloads. Thank you for your interest in the Android Traffic Analysis project!