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

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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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  -