Deep-Learning and Machine-Learning-Based Techniques for Malware Detection and Data-Driven Network Security

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Author(s): Praneeth Gunti (National Institute of Technology, Kurukshetra, India), Brij B. Gupta (National Institute of Technology, Kurukshetra, India)and Francisco José García Peñalvo (University of Salamanca, Spain)
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.ch003

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

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Abstract

A never-ending fight is taking place among malware creators and security experts as the advances in malware are daunting. The machine learning strategies are indeed the new mode of researching malware. The purpose of this chapter is to explore machine learning methods for malware recognition and in general deep learning methods. The chapter gives complete explanations of the techniques and resources used in a standard machine learning process for detecting malware. It examines the study issues that are posed by existing study methods and introduces the potential avenues of study in future. By administering a study to the participants, scholars have a better knowledge of the malware detections. The authors start by discussing simple dynamic modelling methods, their importance to the data analytics of malware, and their implementations. They use open access resources such as virustotal.com that review sample of dynamic analysis in reality.

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Deep-Learning and Machine-Learning-Based Techniques for Malware Detection and Data-Driven Network Security

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