Research Article
Android Malware Detection Based on Static Analysis and Data Mining Techniques: A Systematic Literature Review
@INPROCEEDINGS{10.1007/978-3-031-40467-2_4, author={Hemant Rathore and Soham Chari and Nishant Verma and Sanjay K. Sahay and Mohit Sewak}, title={Android Malware Detection Based on Static Analysis and Data Mining Techniques: A Systematic Literature Review}, proceedings={Broadband Communications, Networks, and Systems. 13th EAI International Conference, BROADNETS 2022, Virtual Event, March 12-13, 2023 Proceedings}, proceedings_a={BROADNETS}, year={2023}, month={7}, keywords={Android Deep Learning Machine Learning Malware Detection Static Analysis}, doi={10.1007/978-3-031-40467-2_4} }- Hemant Rathore
Soham Chari
Nishant Verma
Sanjay K. Sahay
Mohit Sewak
Year: 2023
Android Malware Detection Based on Static Analysis and Data Mining Techniques: A Systematic Literature Review
BROADNETS
Springer
DOI: 10.1007/978-3-031-40467-2_4
Abstract
Android applications are proliferating, which has led to the rise of android malware. Many research studies have proposed various detection frameworks for android malware detection. Literature suggests that static malware detection techniques are practical and assuring for detecting android malware. This paper presents a thorough survey of data mining-based static malware detection. We briefly discuss the growth of android malware and current detection techniques and offer a comprehensive analysis and summary of studies for each data mining-based malware detection phase, such as data acquisition, preprocessing, feature extraction, learning algorithms, and evaluation. Finally, we highlight some challenges and open issues in data mining-based android malware detection. This review will help understand the complete picture of static android malware detection and serve as a basis for malware detection in general.
