.png){kind=icon}
返回
Android malware detection applying feature selection techniques and machine learning
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-022-13767-2.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Android operating system is known as one of the most popular mobile operating systems. The malware intrusion increases in the same pace as the production of applicable software. Propagation of new and transformed malware in seconds is a critical challenge in malware detection. Android software supplies thousands of features, providing assistance to identify malware applications. In this paper, a novel method based on a random forest algorithm, which applied three different feature selection techniques is proposed. This paper assesses the consequence of applying three different feature selection types including effective, high weight and effective group feature selection. Experiments conducted on Drebin dataset indicate applying the feature selection methods ameliorate the accuracy in terms of metrics and required time. In addition, comparison between the candidate feature selection model and a variety of algorithms as baselines proves the merit of applying feature selection on Random Forest, which outperforms other models based on several metrics.
Keyword:
Android operating system
Malware detection
Machine learning
Random forest
Feature selection
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
Estimation of ultrasound reference values for the lower limb peripheral nerves in adults.png)
Medicine
OAAI
MedicineIF0
OAAI分享收藏Android malware detection through hybrid features fusion and ensemble classifiers: The AndroPyTool framework and the OmniDroid dataset通过混合功能融合和集成分类器进行Android恶意软件检测: AndroPyTool框架和OmniDroid数据集INFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
分享收藏
查看更多内容