Protecting Androids from Malware Menace Using Machine Learning And Deep Learning

DOI

10.2991/978-94-6463-471-6_28How to use a DOI?

Keywords

Ensemble Learning; SNN; Random Forest; Stacking Classifier; Permissions

Abstract

Mobile devices have become integral to our lives. Among operating systems, Android holds the largest market share, making it a prime target for attackers. While various solutions exist for Android malware detection, there remains a need for effective attribute selection methods. In this work, we introduce an Android malware detection technique that employs machine learning to distinguish between safe and dangerous applications. By reducing the feature vector dimension, training time decreases, and real-time malware detection becomes feasible. A number of multiple linear regression techniques are evaluated, including support vector machines, decision trees, Naïve Bayes, and k-nearest neighbors. Additionally, we employ the stacking classifier method an ensemble learning technique to enhance classification performance. This stacking classifier combines Random Forest and Sequential Neural Networks and performs testing. Our results surprisingly show good performance with linear regression models, eliminating the need for excessively complicated techniques.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - C. Siva Kumar
AU  - S. Mohan Krishna
AU  - V. Ebinazer
AU  - N. Narasimha Naidu
AU  - P. Pawan Kalyan
PY  - 2024
DA  - 2024/07/30
TI  - Protecting Androids from Malware Menace Using Machine Learning And Deep Learning
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 285
EP  - 291
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_28
DO  - 10.2991/978-94-6463-471-6_28
ID  - Kumar2024
ER  -