Research of Android Malware Detection based on ACO Optimized Xgboost Parameters Approach
Jie Ling, Xuejing Wang, Yu Sun
Available Online April 2019.
- https://doi.org/10.2991/icmeit-19.2019.60How to use a DOI?
- ACO, Xgboost, Android, malware, detection classification.
- 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).
- 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 PB - Atlantis Press SP - 364 EP - 371 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 -