MFEMDroid: A Novel Malware Detection Framework Using Combined Multitype Features and Ensemble Modeling

Table 1

Summary of proposed approaches with the relevant works.

Paper	Year	Input data			Classifier/algorithm
		D	S-S	M-S	I-ML	I-DL	E-ML	E-DL
Kim et al. [22]	2018	√			√
Bhat et al. [23]	2023	√					√
Li et al. [24]	2022	√						√
Ganesh et al. [10]	2017		√			√
Alslan et al. [25]	2019		√		√
Alazab et al. [26]	2020			√	√
Khariwal et al. [27]	2020			√	√
Wang et al. [28]	2019			√		√
Wu et al. [29]	2021			√		√
Zhu et al. [30]	2023			√		√
Bakhshinejad et al. [31]	2020		√					√
Ficco et al. [32]	2021	√						√
This paper				√				√

Note: D: dynamic features; S-S: single static feature; M-S: multitypes of static features; I-ML: individual machine learning classifier; I-DL: individual deep learning algorithm; E-ML: the ensemble of multiple machine learning classifiers; E-DL: the ensemble of multiple deep learning algorithms.