Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

Protecting Androids from Malware Menace Using Machine Learning And Deep Learning

Authors
C. Siva Kumar1, *, S. Mohan Krishna2, V. Ebinazer2, N. Narasimha Naidu2, P. Pawan Kalyan2
1Professor, Department of DS, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, India
2UG Scholar, Department of Computer Science and Systems Engineering, Sree Vidyanikethan Engineering College, Tirupati, India
*Corresponding author. Email: Svkumar650@gmail.com
Corresponding Author
C. Siva Kumar
Available Online 30 July 2024.
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.

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Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
978-94-6463-471-6
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_28How to use a DOI?
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.

Cite this article

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  -