Android Malware Detection System using Machine Learning

Kaur, Amanpreet; Lal, Sangeeta; Goel, Shruti; Agarwal, Astha

Abstract

Detecting Android malware is imperative for safeguarding user privacy, securing data, and preserving device performance. Consequently,
numerous studies have underscored the complexities associated with Android malware detection, prompting a multidimensional approach
to tackle these challenges effectively. This research leverages machine learning techniques, emphasizing feature extraction, classification
algorithms, and both supervised and unsupervised learning methodologies. The exploration begins with in-depth Exploratory Data
Analysis (EDA) to gain insights into the dataset, paving the way for informed decision-making. Principal Component Analysis (PCA) is
employed for dimensionality reduction, a pivotal step in handling the multivariate nature of the data. The integration of API calls,
clustering, and anomaly detection further enriches the model's capability to discern between benign and malicious applications. Crucially,
the study delves into the intricacies of sampling, evaluation, and the Confusion Matrix to quantify the model's performance accurately.
The utilization of diverse classification algorithms, including Support Vector Machines (SVM), Multi-Layer Perceptrons (MLP), Random
Forest, GaussianNB, Decision Tree, and Logistic Regression, underscores the comprehensive nature of the approach. These algorithms
collectively contribute to a robust and versatile Android malware detection model capable of adapting to varying threat scenarios. The
dataset employed for training and evaluation is sourced from Kaggle, encompassing 29,999 Android applications categorized as benign
or malicious based on permissions sought. Current detection methods, deemed resource-intensive and exhaustive, face the challenge of keeping pace with the relentless evolution of new malware strains. This research seeks to address this gap by proposing a sophisticated, machine learning-driven model that not only enhances accuracy but also demonstrates efficiency and adaptability in the face of a dynamic threat landscape.

Citation

Kaur, A., Lal, S., Goel, S., Pandey, M., & Agarwal, A. (2024). Android Malware Detection System using Machine Learning. . https://doi.org/10.1145/3675888.3676049

• Conference Name: The International Conference on Contemporary Computing (IC3)
• Conference Location: India
• Start Date: Aug 8, 2024
• End Date: Aug 10, 2024
• Acceptance Date: Jun 26, 2024
• Online Publication Date: Oct 28, 2024
• Publication Date: Aug 8, 2024
• Deposit Date: Aug 12, 2024
• Publicly Available Date: Aug 9, 2025
• Publisher: Association for Computing Machinery (ACM)
• Pages: 186-191
• DOI: https://doi.org/10.1145/3675888.3676049
• Public URL: https://keele-repository.worktribe.com/output/884315
• Related Public URLs: https://dl.acm.org/conference/ic3