Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

Research of Android Malware Detection based on ACO Optimized Xgboost Parameters Approach

Authors
Jie Ling, Xuejing Wang, Yu Sun
Corresponding Author
Xuejing Wang
Available Online April 2019.
DOI
https://doi.org/10.2991/icmeit-19.2019.60How to use a DOI?
Keywords
ACO, Xgboost, Android, malware, detection classification.
Abstract
In order to deal with low efficiency and accuracy of detection caused by the improper selection of Xgboost parameters in Android malware detection. In this paper, we introduce Ant Colony Optimization (ACO) into Xgboost parameters optimization and propose an approach based on ACO optimize Xgboost parameters in Android malware detection. Selecting features such as permissions, intents and APIs in AndroidManifest.xml and smali files and extra the optimal feature subset, then apply to the proposed method. The experimental results show that the proposed method effectively improves accuracy of detection and reduces false positive rate compared with the Xgboost algorithm optimized by Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
Part of series
Advances in Computer Science Research
Publication Date
April 2019
ISBN
978-94-6252-708-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmeit-19.2019.60How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Jie Ling
AU  - Xuejing Wang
AU  - Yu Sun
PY  - 2019/04
DA  - 2019/04
TI  - Research of Android Malware Detection based on ACO Optimized Xgboost Parameters Approach
BT  - 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.60
DO  - https://doi.org/10.2991/icmeit-19.2019.60
ID  - Ling2019/04
ER  -