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Research Article

Engineering and Technology Research 5(4): 063-065, July2022
DOI: 10.15413/etr.2022.107

ISSN: 2682 5716
2022 Academia Publishing

Abstract

 

Prediction of malware analysis using machine learning and deep learning techniques
 

Accepted 12th May, 2022

 

Maheswari Kota

Gandhi Nagar, Rushikonda, Visakhapatnam, Andhra Pradesh 530045, India.
 

E-mail: mkota@gitam.in.
 

The term malware refers to software that harms or exploits devices and computers. Corporations and government agencies struggle with malware, including viruses, worms, Trojan horses, ransomware, and spyware. The traditional method of detecting malware relies on anti-virus signatures, heuristics, and sandbox testing, which require manual analysis by security analysts and researchers (Owaida, 2021). Organizations have a hard time keeping up with malware threats daily as new attacks and variants emerge. Machine learning (ML) and artificial intelligence (AI) can detect unknown malware by automatically learning the malware patterns based on large volumes of historical data. With its unique capabilities, artificial intelligence/machine learning has become an integral part of the latest malware detection solutions, complementing heuristics and signature-based methods. This study attempts to construct Malware detection algorithms based on multiple machine learning and deep learning techniques using Python in Google Colab that can significantly identify the difference between malicious and non-malicious files. This proposed method tries to take a sample dataset collected from the Kaggle.com website as input, and then the implementation of the pre-processing step is done, followed by applying the concepts of ML and deep learning to predict the malware and reduce the false positives. As a result, RBM and Hybrid models turned out to give the best results with reduced test loss in the case of RBM.

Key words: Malware, Machine learning, deep learning, artificial intelligence.

Cite this article as:
Maheswari K (2022). Prediction of malware analysis using machine learning and deep learning techniques. Eng. Technol. Res. 5(4): 063-065.


This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.




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