2019
DOI: 10.1007/978-3-030-11196-0_56
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Clustering Android Applications Using K-Means Algorithm Using Permissions

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Cited by 4 publications
(2 citation statements)
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“…In each iteration, N-1 sets of samples are selected for training and the other one is left to validate the precision of the classifier [65]. N � 10 was selected to carry out the experiments, according to the N-fold performance obtained in studies related to the detection and identification of malware ( [28,57,66,67]). Likewise, for both scenarios, the initial dataset (CDI), the dataset with features selected by PCA (CDPCA) and the dataset with features selected by Logistic Regression (CDLR) were considered.…”
Section: Experimental Results Of the Selection Of Featuresmentioning
confidence: 99%
“…In each iteration, N-1 sets of samples are selected for training and the other one is left to validate the precision of the classifier [65]. N � 10 was selected to carry out the experiments, according to the N-fold performance obtained in studies related to the detection and identification of malware ( [28,57,66,67]). Likewise, for both scenarios, the initial dataset (CDI), the dataset with features selected by PCA (CDPCA) and the dataset with features selected by Logistic Regression (CDLR) were considered.…”
Section: Experimental Results Of the Selection Of Featuresmentioning
confidence: 99%
“…The K-nearest neighbor algorithm [9] operates as a supervised learning model that achieves the classification of Android malware by measuring the Euclidean distance between different feature values in the geometric space. K-means clustering algorithm [10], an unsupervised learning algorithm, is typically employed for family categorization of Android malware with the objective of finding centroids among N data points, thereby minimizing the mean square distance from each data point to its nearest centroid. Zhao et al [11] aimed to improve the accuracy of Android malware detection by employing boosting and bagging.…”
Section: Related Workmentioning
confidence: 99%