Effective and Efficient Android Malware Detection and Category Classification Using the Enhanced KronoDroid Dataset

Table 7

Related work on malware detection and category classification using different datasets.

Source	Approach and year	Name/Year of dataset	Time frame	Features and approach	Dynamic	Malware detection	Category classification
[42]	Semisupervised learning (2018)	2016	N/A	Hybrid feature selection: aggregate information	Emulator	Accuracy: 91.23%	N/A (dynamic accuracy: 80.3%)
[38]	Deep artificial Neural network (2021)	CICANDMAL2019	N/A	Hybrid feature selection: N/A	Real device	Static accuracy: 93.40%	4 categories (static accuracy: 92.5%) (dynamic accuracy: 80.3%)
[43]	Machine learning (2018)	Updroid	2014–2018	Hybrid feature selection: N/A	Emulator	Detection as categorization	Accuracy: 96.37%
[44]	Deep learning (2020)	DL-driod dataset 2019	N/A	Hybrid Feature ranking: InfoGain	Real device	Accuracy: 98.5%	N/A
[18]	Deep neural network with pseudolabel (2020)	CICMALDroid2020	2017-2018	Dynamic feature selection: N/A	Emulator	Detection as categorization	4 categories (F1-score: 97.8%)
[7]	Machine learning (2021)	CCS-CICAndMal2020	N/A	Dynamic feature selection: N/A	Emulator	Detection as categorization	12 categories (precision: 98.4%)
[45]	Machine learning (2017)	Drebin,Mcafee Praguard	N/A	Static feature selection: mean decrease impurity (MDI)	N/A	99.82%	99.26% into families
[46]	Machine learning (2018)	Drebin, Genome VirusShare	2009–2017	Dynamic feature selection: N/A	Emulator	97.4%	97.8% into families
[47]	Machine learning (2020)	VirusShare AndroZoo	2010–2017	Dynamic feature importance: Top100	Emulator	F1: 92.88% (same year); F1: 71.81% (across year)	N/A
[48]	Machine learning (2021)	APKPure, random dataset	N/A	Static, feature selection: LR based	N/A	Accuracy: 96.3%	N/A
[49]	Machine learning (2021)	APKPure, VirusShare	N/A	Static, feature selection: filter-based	N/A	F-measure: 95%	N/A
[50]	Machine learning (2022)	Mendeley repository	N/A	Dynamic, features selection: embedded BFE	Emulator	F-measure: 99%	N/A
[51]	Machine learning (2022)	Multiple repositories	N/A	Dynamic, feature selection: rough set analysis (RSA) and principal component analysis (PCA)	Emulator	Detection rate: 98.8%	N/A
[52]	Machine learning (2023)	AndroZoo and Drebin	N/A	Static, feature selection: wrapper based (DDQN)	N/A	Detection rate: 95.6%	N/A
[23]	Deep neural network with pseudo label stack auto encoder (2022)	CICMALDroid2020	2017-2018	Hybrid feature selection: N/A	Emulator	Accuracy: 98.28%	5 categories (precision: 98.4%)
[53]	Ensemble (random forest)	KronoDroid	2008–2020	Hybrid, features selection: Chi-squared	Real and emulated device	Accuracy: 95%; Precision: 95%	N/A
Our	Machine learning (random forest)	Subset of 2020 KronoDroid	2008–2020	Hybrid, filter-based features selection	Real device	Accuracy: 98.03%	15 categories (accuracy: 87.6%)

The values in bold emphasizes aspects of this research that are important in comparison to existing research.