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Security Vetting Of Android Applications Using Graph Based Deep Learning Approaches

Poudel, Prabesh
http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1617199500076786

Abstract Details

2021, Master of Science (MS), Bowling Green State University, Computer Science.
Along with the immense popularity of Android applications, the Android ecosystem is under constant threat of malware attacks. This issue warrants developing efficient tools to detect malware apps. There is a large body of work in the literature that has applied static analysis for malware detection. For instance, one popular idea has been to extract API-calls from the app code and then to use those API-calls as artifacts to train machine learning models to classify malware and benign apps. However, most of this line of work does not incorporate the true execution sequence of the API-calls, and thus misses out to capture a potentially rich signature. Furthermore, while evaluating the vetting accuracy, many of the prior work report their primary results on a randomly selected test set that are not spatially consistent (malware percentage in the test set approximating real-world scenario) and/or temporally consistent (having correct time split of train and test data) which artificially inflates the performance of the model. In this thesis, we explore if tracking the true sequence of the API-calls improves the effectiveness of the vetting process and present results ranging from testing on a random test set to a spatially and temporally consistent test set. We perform deep learning-based malware classification using a graph that we name API sequence graph which preserves the true sequence of API calls. The experiments show that our best performing model achieves AuPRC ranging from 0.977 to 0.86 and an F1-score of 0.955 to 0.83 depending on the consistency of the test set. The results show that our best-performing model, based on the true sequence of API calls, outperforms a quasi-sequence-based model.
Sankardas Roy, Ph.D. (Advisor)
Jong Kwan Lee, Ph.D. (Committee Member)
Qing Tian, Ph.D. (Committee Member)
70 p.
Computer Science
android security vetting; sequence of API calls; capsule graph neural network; android; deep learning; static analysis;

Recommended Citations

Citations

  • Poudel, P. (2021). Security Vetting Of Android Applications Using Graph Based Deep Learning Approaches [Master's thesis, Bowling Green State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1617199500076786

    APA Style (7th edition)

  • Poudel, Prabesh. Security Vetting Of Android Applications Using Graph Based Deep Learning Approaches. 2021. Bowling Green State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1617199500076786.

    MLA Style (8th edition)

  • Poudel, Prabesh. "Security Vetting Of Android Applications Using Graph Based Deep Learning Approaches." Master's thesis, Bowling Green State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1617199500076786

    Chicago Manual of Style (17th edition)

bgsu1617199500076786
321
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This open access ETD is published by Bowling Green State University and OhioLINK.