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

Unified Approach for Android Malware Detection: Feature Combination and Ensemble Classifier

Authors
V. Jyothsna1, *, Kavya Priya Dasari2, Sravani Inuguru2, Venkat Bharath Reddy Gowni2, Jaya Teja Reddy Kudumula2, K. Srilakshmi3
1Associate Prof, Dept of IT, Sree Vidyanikethan Engineering College, Tirupathi, India
2UG Scholar, Dept of IT, Sree Vidyanikethan Engineering College, Tirupathi, India
3Lecturer, Sri Padmavathi Women’s Degree &, PG College, Tirupathi, India
*Corresponding author. Email: jyothsna1684@gmail.com
Corresponding Author
V. Jyothsna
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_47How to use a DOI?
Keywords
Android; machine learning; malware; anomaly detection; feature enhancement
Abstract

As the smartphone market has expanded enormously, particularly in the Android environment, the necessity for robust anti-malware security has become increasingly apparent. By harnessing the power of machine learning and large datasets, this model demonstrates exceptional capabilities in identifying subtle malicious trends. This study delves into the importance of coexistence in malware detection.This methodology analyzes coexistence patterns crucial for effective malware detection and develops a dataset that integrates these key features. Addressing data imbalance using the SMOTE technique enhances dataset representativeness. Feature selection via Extra Trees Classifier optimizes pattern detection, improving classification precision. This methodology significantly enhances cybersecurity in dynamic digital settings, detecting Android malware with high accuracy. The voting classifier (with MLP, CatBoost, and XGBoost) trained on the above dataset achieved 98% accuracy. This work represents a substantial advancement in efficient and adaptable malware detection techniques tailored for the evolving Android ecosystem.

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_47How 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  - V. Jyothsna
AU  - Kavya Priya Dasari
AU  - Sravani Inuguru
AU  - Venkat Bharath Reddy Gowni
AU  - Jaya Teja Reddy Kudumula
AU  - K. Srilakshmi
PY  - 2024
DA  - 2024/07/30
TI  - Unified Approach for Android Malware Detection: Feature Combination and Ensemble Classifier
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 485
EP  - 495
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_47
DO  - 10.2991/978-94-6463-471-6_47
ID  - Jyothsna2024
ER  -