2022 International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN) 2022
DOI: 10.1109/icstsn53084.2022.9761336
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Information Gain-based Feature Selection Method in Malware Detection for MalDroid2020

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Cited by 4 publications
(2 citation statements)
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“…In addition, information gain has been widely used in various datasets; it is also useful for a dataset that has a hybrid data type. Agrawal and Kshirsagar proposed an information gain-based feature selection method for Android malware detection [28].Kumar et al proposes a method for mobile malware detection that uses information gain and support vector machine [29]. Shu et al proposes an information gain-based semi-supervised feature selection algorithm for hybrid data, considering symbolic, numerical, and missing features, and demonstrates its superiority over other feature selection methods using experiments on ten UCI datasets.…”
Section: B Feature Selection (Information Gain (Ig))mentioning
confidence: 99%
“…In addition, information gain has been widely used in various datasets; it is also useful for a dataset that has a hybrid data type. Agrawal and Kshirsagar proposed an information gain-based feature selection method for Android malware detection [28].Kumar et al proposes a method for mobile malware detection that uses information gain and support vector machine [29]. Shu et al proposes an information gain-based semi-supervised feature selection algorithm for hybrid data, considering symbolic, numerical, and missing features, and demonstrates its superiority over other feature selection methods using experiments on ten UCI datasets.…”
Section: B Feature Selection (Information Gain (Ig))mentioning
confidence: 99%
“…There have been numerous major advances in AM detection by utilizing deep learning approaches in recent years [10]. Researchers have investigated the efficiency of ensemble approaches, which integrate many classifiers to improve detection accuracy and robustness [11].…”
Section: Introductionmentioning
confidence: 99%