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FSDroid:- A feature selection technique to detect malware from Android using Machine Learning Techniques

FSDroid

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Abstract

With the recognition of free apps, Android has become the most widely used smartphone operating system these days and it naturally invited cyber-criminals to build malware-infected apps that can steal vital information from these devices. The most critical problem is to detect malware-infected apps and keep them out of Google play store. The vulnerability lies in the underlying permission model of Android apps. Consequently, it has become the responsibility of the app developers to precisely specify the permissions which are going to be demanded by the apps during their installation and execution time. In this study, we examine the permission-induced risk which begins by giving unnecessary permissions to these Android apps. The experimental work done in this research paper includes the development of an effective malware detection system which helps to determine and investigate the detective influence of numerous well-known and broadly used set of features for malware detection. To select best features from our collected features data set we implement ten distinct feature selection approaches. Further, we developed the malware detection model by utilizing LSSVM (Least Square Support Vector Machine) learning approach connected through three distinct kernel functions i.e., linear, radial basis and polynomial. Experiments were performed by using 2,00,000 distinct Android apps. Empirical result reveals that the model build by utilizing LSSVM with RBF (i.e., radial basis kernel function) named as FSdroid is able to detect 98.8% of malware when compared to distinct anti-virus scanners and also achieved 3% higher detection rate when compared to different frameworks or approaches proposed in the literature.

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Notes

  1. https://en.wikipedia.org/wiki/Android_(operating_system)

  2. http://gs.statcounter.com/os-market-share/mobile/worldwide

  3. https://www.statista.com/statistics/266210/number-of-available-applications-in-the-google-play-store/

  4. https://source.android.com/security/overview/kernel-security

  5. https://www.gdatasoftware.com/news/2017/04/29715-350-new-android-malware-apps-every-hour

  6. https://play.google.com/store?hl=en_IN

  7. http://blog.trendmicro.com/trendlabs-security-intelligence/a-look-at-google-bouncer/

  8. http://gs.statcounter.com/os-market-share/mobile/worldwide

  9. https://source.android.com/security/reports/Google_Android_Security_2017_Report_Final.pdf

  10. https://source.android.com/security/reports/Google_Android_Security_2017_Report_Final.pdf

  11. https://developer.android.com/training/permissions/requesting.html

  12. https://www.microsoft.com/en-in/windows/comprehensive-security

  13. https://www.virustotal.com/

  14. http://apk.hiapk.com/

  15. http://www.appchina.com/

  16. http://android.d.cn/

  17. http://www.mumayi.com/

  18. http://apk.gfan.com/

  19. http://slideme.org/

  20. http://download.pandaapp.com/?app=soft&controller=android#.V-p3f4h97IU

  21. http://202.117.54.231:8080/

  22. Malware families are identified by VirusTotal.

  23. https://www.statista.com/statistics/271774/share-of-android-platforms-on-mobile-devices-with-android-os/

  24. https://data.mendeley.com/datasets/9b45k4hkdf/1

  25. In this study, we use the Min-max normalization approach to normalize the data. This approach is based on the principle of linear transformation, which bring each data point \(D_{q_{i}}\) of feature Q to a normalized value \(D_{q_{i}},\) that lie in between 0 − 1. Following equation is considered to find the normalized value of \(D_{q_{i}}:\)

    $$Normalized(D_{q_{i}})=\frac{D_{q_{i}}-min(Q)}{max(Q)-min(Q)},$$

    where min(Q) & max(Q) are the minimum and maximum significance of attribute Q, respectively.

  26. https://developer.android.com/guide/topics/permissions/overview

  27. Name of the extracted feature sets are available at url: https://github.com/ArvindMahindru66/Computer-and-security-dataset for reserachers and academicians.

  28. In our study, we fixed the value of T = 3 and d = 5 for performing experiment with both of the linear and polynomial kernel.

  29. In our study, we fixed the value of γ = 10 for performing experiment with RBF kernel.

  30. Performance Parameters are calculated on the basis of training and testing data set.

  31. To perform experiment we collect 1000 distinct Android apps from real-world

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  1. Department of Computer Science and Applications, D.A.V. University, Sarmastpur, 144012, Jalandhar, India

    Arvind Mahindru

  2. Department of Computer Science and Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, 144011, India

    Arvind Mahindru & A.L. Sangal

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Mahindru, A., Sangal, A. FSDroid:- A feature selection technique to detect malware from Android using Machine Learning Techniques. Multimed Tools Appl 80, 13271–13323 (2021). https://doi.org/10.1007/s11042-020-10367-w

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