Welcome to the InfoSci Platform
Reference Hub4
Digital Transformation and Cybersecurity Challenges: A Study of Malware Detection Using Machine Learning Techniques

Digital Transformation and Cybersecurity Challenges: A Study of Malware Detection Using Machine Learning Techniques

Fatimah Al Obaidan, Saqib Saeed
Copyright: © 2021 |Pages: 24
ISBN13: 9781799869757|ISBN10: 179986975X|ISBN13 Softcover: 9781799888833|EISBN13: 9781799869764
DOI: 10.4018/978-1-7998-6975-7.ch011
Cite Chapter Cite Chapter

MLA

Al Obaidan, Fatimah, and Saqib Saeed. "Digital Transformation and Cybersecurity Challenges: A Study of Malware Detection Using Machine Learning Techniques." Handbook of Research on Advancing Cybersecurity for Digital Transformation, edited by Kamaljeet Sandhu, IGI Global, 2021, pp. 203-226. https://doi.org/10.4018/978-1-7998-6975-7.ch011

APA

Al Obaidan, F. & Saeed, S. (2021). Digital Transformation and Cybersecurity Challenges: A Study of Malware Detection Using Machine Learning Techniques. In K. Sandhu (Ed.), Handbook of Research on Advancing Cybersecurity for Digital Transformation (pp. 203-226). IGI Global. https://doi.org/10.4018/978-1-7998-6975-7.ch011

Chicago

Al Obaidan, Fatimah, and Saqib Saeed. "Digital Transformation and Cybersecurity Challenges: A Study of Malware Detection Using Machine Learning Techniques." In Handbook of Research on Advancing Cybersecurity for Digital Transformation, edited by Kamaljeet Sandhu, 203-226. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-6975-7.ch011

Export Reference

Mendeley
Favorite

Abstract

Digital transformation has revolutionized human life but also brought many cybersecurity challenges for users and enterprises. The major threats that affect computers and communication systems by damaging devices and stealing sensitive information are malicious attacks. Traditional anti-virus software fails to detect advanced kind of malware. Current research focuses on developing machine learning techniques for malware detection to respond in a timely manner. Many systems have been evolved and improved to distinguish the malware based on analysis behavior. The analysis behavior is considered a robust technique to detect, analyze, and classify malware, categorized into two models: a static and dynamic analysis. Both types of previous analysis have advantages and limitations. Therefore, the hybrid method combines the strength of static and dynamic analyses. This chapter conducted a systematic literature review (SLR) to summarize and analyze the quality of published studies in malware detection using machine learning techniques and hybrid analysis that range from 2016 to 2021.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.