Android Malware Detection Using Genetic Algorithm based Optimized Feature Selection and Machine Learning

Click here to download base paper

Click here to download base paper

Android Malware Detection is of utmost importance due to open-source characteristic and Google backing has the largest global market share and hence is the target. Being the world’s most popular operating system, it has drawn the attention of cyber criminals operating particularly through wide distribution of malicious applications. This paper proposes an effectual machine-learning based approach for Android Malware Detection making use of evolutionary Genetic algorithm for discriminatory feature selection. Selected features from Genetic algorithm are used to train machine learning classifiers and their capability in identification of Malware before and after feature selection is compared. The experimentation results validate that Genetic algorithm gives most optimized feature subset helping in reduction of feature dimension to less than half of the original feature-set. Classification accuracy of more than 94% is maintained post feature selection for the machine learning based classifiers, while working on much-reduced feature dimension, thereby, having a positive impact on computational complexity of learning classifiers.

Abstract:

As mobile devices continue to dominate the digital landscape, the threat of Android malware has become a significant concern. This postgraduate project aims to enhance Android malware detection using a novel approach that combines Genetic Algorithm (GA)-based optimized feature selection with machine learning techniques. The proposed system employs advanced algorithms to identify and select the most relevant features, optimizing the efficiency and accuracy of malware detection.

Existing System:

Current Android malware detection systems often struggle with the evolving nature of malware, and their reliance on predefined feature sets can result in limited adaptability. This project addresses these limitations by introducing a dynamic feature selection mechanism using Genetic Algorithms, allowing the system to adapt and improve its detection capabilities over time.

Proposed System:

The proposed system introduces a Genetic Algorithm-based approach to optimize feature selection for Android malware detection. Machine learning algorithms are employed to analyze these selected features, enhancing the system’s ability to identify and classify malware accurately. This dynamic approach improves the system’s adaptability to new and evolving malware threats.

System Requirements:

• Minimum 8GB RAM
• Dual-core processor
• 100GB of free storage space
• Android Studio for development and testing

Hardware and Software Requirements:

• Hardware: Personal computer or laptop
• Software: Android Studio, Python, Machine Learning libraries (e.g., scikit-learn), Genetic Algorithm libraries

Architecture:

The system architecture comprises three main components: the Genetic Algorithm-based feature selection module, the machine learning module for malware detection, and the Android application interface. The Genetic Algorithm optimizes feature selection, providing a subset of relevant features to the machine learning module, which then classifies the applications as malicious or benign. The Android application interface serves as the user-friendly front-end for interacting with the system.

Technologies Used:

• Programming Language: Python for algorithm development
• Machine Learning Libraries: scikit-learn, TensorFlow
• Genetic Algorithm Library: DEAP (Distributed Evolutionary Algorithms in Python)
• Android Development: Android Studio, Java

Web User Interface:

The system includes a web-based user interface to facilitate interaction and monitoring. This interface allows users to input applications for analysis, view detection results, and visualize the optimization process carried out by the Genetic Algorithm. It provides a user-friendly experience for both researchers and security professionals involved in Android malware detection.

This project aims to contribute to the field of mobile security by leveraging Genetic Algorithms and machine learning to create a robust and adaptive system for Android malware detection. The integration of a web-based user interface enhances accessibility and usability, making it a valuable tool for security practitioners and researchers alike.

• Feature extraction,
• Malware,
• Genetic algorithms,
• Machine learning,
• Support vector machines,
• Smart phones,
• Machine learning algorithms

Machine Learning (ML) can be a powerful tool for Android malware detection. Here are some ways ML can be applied for this purpose:

1. Feature Extraction:

• Extract relevant features from Android apps. These features could include permissions requested, API calls, code patterns, and other behavioral characteristics.

1. Dataset Preparation:

• Build a labeled dataset containing both benign and malicious apps. The dataset is used to train and test the ML model. Ensure the dataset is representative of the real-world distribution of apps.

1. Model Training:

• Use supervised learning to train a machine learning model. Popular algorithms for this task include decision trees, random forests, support vector machines, and deep learning models (e.g., neural networks).

1. Behavioral Analysis:

• ML models can analyze the behavior of apps in real-time. Deviations from normal behavior patterns may indicate malicious activity. This can include runtime analysis of API calls, network activity, and resource usage.

1. Anomaly Detection:

• ML models can be trained to detect anomalies in app behavior. If an app behaves significantly differently from the norm, it could be flagged for further investigation.

1. Ensemble Methods:

• Combine multiple models into an ensemble for more robust malware detection. Each model may excel at detecting certain types of threats, contributing to an overall improved detection rate.

1. Transfer Learning:

• Transfer learning can be employed by using pre-trained models on a larger dataset and fine-tuning them for Android malware detection. This approach leverages knowledge gained from other domains.

1. Continuous Monitoring:

• Implement continuous monitoring to adapt the model to new types of malware. Periodically update the model using new data to ensure it remains effective against evolving threats.

1. User Feedback Integration:

• Consider incorporating user feedback into the ML model. If users report an app as malicious, this information can be used to improve the model and its accuracy.

1. Cloud-Based ML Services:
   • Utilize cloud-based ML services that offer pre-trained models or allow you to train your models on cloud infrastructure. This can be especially useful for resource-intensive tasks.

Remember that ML models are not foolproof and may have false positives or false negatives. Therefore, combining ML-based detection with traditional security measures and user awareness is essential for a comprehensive Android malware detection strategy. Learn about our android or ios development strategy with Flutter

Diagrams

Creating a visual representation of the architecture is beyond the scope of plain text, but I can provide you with a textual description that you can use as a basis to create an architecture diagram using tools like draw.io, Lucidchart, or any other diagramming tool.

System Architecture Diagram:

1. User Interface Module:
   • Web-based interface for user interaction.
   • Input parameters for the Genetic Algorithm and Machine Learning models.
   • Visualization of results.
2. Genetic Algorithm Module:
   • Input: Android malware dataset.
   • Feature selection using Genetic Algorithm.
   • Output: Optimized feature set.
3. Machine Learning Module:
   • Input: Optimized feature set, Android malware dataset (training and testing).
   • Training machine learning models (e.g., Random Forest, Support Vector Machines).
   • Output: Trained models.
4. Android Malware Detection Module:
   • Input: New Android app features.
   • Utilizes the trained machine learning models for malware detection.
   • Output: Malware detection results.
5. Database Module:
   • Stores datasets for training and testing.
   • Historical data for analysis.

System Flow:

1. User configures parameters and initiates the process through the web interface.
2. Genetic Algorithm Module receives Android malware dataset, performs feature selection, and outputs the optimized feature set.
3. Machine Learning Module takes the optimized feature set and Android malware dataset, trains machine learning models, and produces the trained models.
4. User inputs new Android app features through the web interface.
5. Android Malware Detection Module uses the trained models to detect malware and provides results.
6. Database Module stores datasets and results for analysis and future improvements.

Interaction:

• The user interacts primarily through the web interface, configuring parameters and receiving results.
• Modules communicate internally, with the Genetic Algorithm Module feeding the optimized feature set to the Machine Learning Module, and the Machine Learning Module providing results to the Android Malware Detection Module.

This textual representation can serve as a guide for creating a visual representation of the architecture. Use symbols and connectors in your chosen diagramming tool to represent the different modules, their interactions, and the flow of data through the system.