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Android Ransomware Detection Using Supervised Machine Learning Techniques Based on Traffic Analysis.

Amnah Albin Ahmed¹, Afrah Shaahid¹, Fatima Alnasser¹

¹Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia.

Sensors (Basel, Switzerland)

|January 11, 2024

Summary

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This summary is machine-generated.

This study explores artificial intelligence (AI) for detecting Android ransomware. Machine learning and deep learning models were developed, with Decision Tree (DT) showing high accuracy and Support Vector Machine (SVM) achieving perfect recall.

Area of Science:

Cybersecurity
Artificial Intelligence
Machine Learning

Background:

Android devices are increasingly targeted by cybercriminals for data theft and ransomware attacks.
Ransomware poses a significant threat, causing data loss, financial damage, and operational disruption.
Existing AI-based methods for Android ransomware detection show promise but require further exploration, especially ensemble and deep learning models.

Purpose of the Study:

To develop and evaluate efficient, precise, and robust machine learning (ML) and deep learning (DL) models for Android ransomware detection.
To compare the performance of various ML and DL algorithms, including ensemble and tabular attention networks.
To investigate the impact of feature selection on model performance for binary classification of benign and ransomware traffic.

Keywords:

android security cyber-attacks deep learning ensemble learning machine learning ransomware attacks

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Main Methods:

Utilized a publicly available dataset with 392,035 records of benign and 10 types of Android ransomware traffic.
Trained and tested Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), ensemble models, Feedforward Neural Network (FNN), and Tabular Attention Network (TabNet).
Conducted two experiments: one using all dataset features and another using the top 19 features.

Main Results:

Decision Tree (DT) achieved the highest accuracy (97.24%), precision (98.50%), and F1-score (98.45%).
Support Vector Machine (SVM) demonstrated perfect recall (100%), indicating effective identification of all ransomware instances.
Both experiments yielded excellent results, highlighting the efficacy of the deployed ML and DL techniques.

Conclusions:

ML and DL techniques are effective for building robust Android ransomware detection systems.
The study provides valuable insights into model performance, with DT and SVM showing particular strengths.
Further research can build upon these findings to enhance mobile security against evolving cyber threats.