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Android Malware Detection through Permission and App Component Analysis using Machine Learning Algorithms

Kulkarni, Keyur
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1525454213460236

Abstract Details

2018, Master of Science, University of Toledo, Engineering (Computer Science).
Improvement in technology has inevitably altered the tactic of criminals to thievery. In recent times, information is the real commodity and it is thus subject to theft as any other possessions: cryptocurrency, credit card numbers, and illegal digital material are on the top. If globally available platforms for smartphones are considered, the Android open source platform (AOSP) emerges as a prevailing contributor to the market and its popularity continues to intensify. Whilst it is beneficiary for users, this development simultaneously makes a prolific environment for exploitation by immoral developers who create malware or reuse software illegitimately acquired by reverse engineering. Android malware analysis techniques are broadly categorized into static and dynamic analysis. Many researchers have also used feature-based learning to build and sustain working security solutions. Although Android has its base set of permissions in place to protect the device and resources, it does not provide strong enough security framework to defend against attacks. This thesis presents several contributions in the domain of security of Android applications and the data within these applications. First, a brief survey of threats, vulnerability and security analysis tools for the AOSP is presented. Second, we develop and use a genre extraction algorithm for Android applications to check the availability of those applications in Google Play Store. Third, an algorithm for extracting unclaimed permissions is proposed which will give a set of unnecessary permissions for applications under examination. Finally, machine learning aided approaches for analysis of Android malware were adopted. Features including permissions, APIs, content providers, broadcast receivers, and services are extracted from benign (~2,000) and malware (5,560) applications and examined for evaluation. We create feature vector combinations using these features and feed these vectors to various classifiers. Based on the evaluation metrics of classifiers, we scrutinize classifier performance with respect to specific feature combination. Classifiers such as SVM, Logistic Regression and Random Forests spectacle a good performance whilst the dataset of combination of permissions and APIs records the maximum accuracy for Logistic Regression
Ahmad Javaid (Committee Chair)
Devabhaktuni Vijay (Committee Member)
Alam Mansoor (Committee Member)
Computer Science
Android, malware, application security, static features, classification

Recommended Citations

Citations

  • Kulkarni, K. (2018). Android Malware Detection through Permission and App Component Analysis using Machine Learning Algorithms [Master's thesis, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1525454213460236

    APA Style (7th edition)

  • Kulkarni, Keyur. Android Malware Detection through Permission and App Component Analysis using Machine Learning Algorithms. 2018. University of Toledo, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1525454213460236.

    MLA Style (8th edition)

  • Kulkarni, Keyur. "Android Malware Detection through Permission and App Component Analysis using Machine Learning Algorithms." Master's thesis, University of Toledo, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1525454213460236

    Chicago Manual of Style (17th edition)

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© 2018, all rights reserved.
This open access ETD is published by University of Toledo and OhioLINK.