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

Ahmad FIRDAUS; Nor Badrul ANUAR; Ahmad KARIM; Mohd Faizal Ab RAZAK

doi:10.1631/FITEE.1601491

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

Regular Papers | Updated：2022-05-19

- Discovering optimal features using static analysis and a genetic search based method for Android malware detection
- 一种使用静态分析和遗传搜索在Android恶意软件检测中搜索最优特征的方法
- Frontiers of Information Technology & Electronic Engineering Vol. 19, Issue 6, Pages: 712-736(2018)
- Affiliations：
  
  Department of Computer System and Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
  Faculty of Computer System & Software Engineering, University Malaysia Pahang, Gambang 26300, Malaysia
  Department of Information Technology, Bahauddin Zakariya University, Multan 60000, Pakistan
- Author bio：
  
  Ahmad FIRDAUS, E-mail: ahmadfirdaus@um.edu.my
  Nor Badrul ANUAR, E-mail: badrul@um.edu.my
- Funds：
- DOI：10.1631/FITEE.1601491
  CLC： TP309.5
- Received：22 August 2016，
  
  Revised：2018-;6-;8，
  
  Published：2018-06
- Accepted：
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Ahmad FIRDAUS, Nor Badrul ANUAR, Ahmad KARIM, et al. Discovering optimal features using static analysis and a genetic search based method for Android malware detection[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(6): 712-736.
DOI：

Ahmad FIRDAUS, Nor Badrul ANUAR, Ahmad KARIM, et al. Discovering optimal features using static analysis and a genetic search based method for Android malware detection[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(6): 712-736. DOI： 10.1631/FITEE.1601491.

摘要

移动设备制造商在全球范围内快速开发各种Android版本。同时，网络罪犯也在实施各种恶意行为，例如跟踪用户活动、窃取个人数据以及实施银行诈骗。由于在日常生活中使用Android进行重要通信的人群数量庞大，这些网络罪犯从中获得了巨大非法收益。为此，安全从业者通过静态和动态分析对恶意软件进行识别。静态分析具有整体代码覆盖、低资源消耗和快速处理的优势。然而，静态分析需要最少量的特征才能对恶意软件进行有效分类。因此，我们采用基于遗传算法（GA）的遗传搜索（GS）在106个字符串中选择特征。为评估由GS确定的最佳特征，我们使用了5种机器学习分类器，分别是Naïve Bayes（NB）、功能树（FT）、J48、随机森林（RF）和多层感知器（MLP）。在这5种分类器中，FT仅使用6种特征，获得最高准确度（95％）和最高真正率（TPR）（96.7％）。

Abstract

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 communications. 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.

关键词

Keywords

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