Discovering optimal features using static analysis and a genetic search based method for Android malware detection

Firdaus, Ahmad and Anuar, Nor Badrul and Karim, Ahmad and Razak, Mohd Faizal Ab (2018) Discovering optimal features using static analysis and a genetic search based method for Android malware detection. Frontiers of Information Technology & Electronic Engineering, 19 (6). pp. 712-736. ISSN 2095-9184, DOI

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Mobile device manufacturers are rapidly producing miscellaneous Android versions worldwide. Simultaneously, cyber criminals are executing malicious actions, such as tracking user activities, stealing personal data, and committing bank fraud. These criminals gain numerous benefits as too many people use Android for their daily routines, including important communi-cations. With this in mind, security practitioners have conducted static and dynamic analyses to identify malware. This study used static analysis because of its overall code coverage, low resource consumption, and rapid processing. However, static analysis requires a minimum number of features to efficiently classify malware. Therefore, we used genetic search (GS), which is a search based on a genetic algorithm (GA), to select the features among 106 strings. To evaluate the best features determined by GS, we used five machine learning classifiers, namely, Naïve Bayes (NB), functional trees (FT), J48, random forest (RF), and multilayer perceptron (MLP). Among these classifiers, FT gave the highest accuracy (95%) and true positive rate (TPR) (96.7%) with the use of only six features.

Item Type: Article
Uncontrolled Keywords: Android; Genetic algorithm; Machine learning; Malware; Static analysis; TP309.5
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Computer Science & Information Technology > Department of Computer System & Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 26 Aug 2019 06:45
Last Modified: 26 Aug 2019 06:45

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