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Context for API Calls in Malware vs Benign Programs

Chandrasekaran, Monika
http://orcid.org/0000-0002-8534-6443
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1626357313703682

Abstract Details

2021, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
The current progress in computer technology is matched by the increase in malware and cyber-attacks, resulting in a nearly constant battle between establishing a complete malware detection technique and newly evolving smart malicious code. Malicious code gain access into the system through network or any connection with external device. In this digital world they are spread easily through internet and when executed causes severe impacts. Machine learning methods are proven to be efficient than signature based methods in detecting new malware.The analysis of malware is made difficult by the fact that, to a large extent, malware and benign code use the same instructions.This suggests that the difference in behavior might be due not to the instructions used, but in how they are used. In particular, the context in which instructions are used seems to play an important role in deciding between malicious and benign code.We have progressed towards defining and extracting the context of API from Portable Execution files of the Windows operating system. It is suggested that the context can be used as a feature in a machine learning algorithm towards identifying attempts to corrupt the system and to elude the antivirus scanners by code obfuscation.
Anca Ralescu, Ph.D. (Committee Chair)
Kenneth Berman, Ph.D. (Committee Member)
Chia Han, Ph.D. (Committee Member)
Dan Ralescu, Ph.D. (Committee Member)
83 p.
Computer Science
Malware detection; API calls; Skipgram Model

Recommended Citations

Citations

  • Chandrasekaran, M. (2021). Context for API Calls in Malware vs Benign Programs [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1626357313703682

    APA Style (7th edition)

  • Chandrasekaran, Monika. Context for API Calls in Malware vs Benign Programs. 2021. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1626357313703682.

    MLA Style (8th edition)

  • Chandrasekaran, Monika. "Context for API Calls in Malware vs Benign Programs." Master's thesis, University of Cincinnati, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1626357313703682

    Chicago Manual of Style (17th edition)

ucin1626357313703682
152
© 2021, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.