Machine Learning for Malware Analysis: Methods, Challenges, and Future Directions

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Author(s): Krishna Yadav (National Institute of Technology, Kurukshetra, India), Aarushi Sethi (National Institute of Technology, Kurukshetra, India), Mavneet Kaur (National Institute of Technology, Kurukshetra, India)and Dragan Perakovic (University of Zagreb, Croatia)
Copyright: 2022
Pages: 18
Source title: Advances in Malware and Data-Driven Network Security
Source Author(s)/Editor(s): Brij B. Gupta (National Institute of Technology, Kurukshetra, India)
DOI: 10.4018/978-1-7998-7789-9.ch001

Keywords: Cyber and Network Security / Information Science Reference / Information Security & Privacy / Security & Forensics

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Abstract

Companies and organizations are collecting all sorts of data ranging from nominal feedback like customer reviews to highly classified data like medical records. With data being such a critical aspect of most of the operations around us, cybercriminals are looking for an opportunity to misuse this information. One such device that cybercriminals use to further their malicious intent is malware. Over the years, these cybercriminals have become immensely powerful using the knowledge of previous attacks. Hence, malware analysis and methods to troubleshoot the problems arising due to malware attacks is the need of the hour. Over time, different new approaches have been developed to defend malware. However, in recent times, machine learning-based malware analysis has gained popularity. The capacity to detect possible future malware by learning from existing malware patterns makes this method very popular. In this chapter, the authors have introduced different malware and the machine learning-based approach that has been developed in recent times to mitigate malware.

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