Facebook Twitter Linkedin
  home bred bred bred bred bred bred bred  
btn Academia Journal of Biotechnology
btn Journal of Business and Economic     Management
btn  Academia Journal of Medicinal Plants
btn Academia Journal of Environmental     Sciences
btn Academia Journal of Agricultural     Research
btn Academia Journal of Educational     Research
btn Academia Journal of Food Research
btn Academia Journal of Scientific     Research
btn Academia Journal of Microbiology    Research
btn  Engineering and Technology
btn Academia Journal of Pharmacy and     Pharmacology
btn Medicine and Medical Sciences
btn Conference Publishing
btn Grants Application
btn  Impact Factor
btn Conference Partners and Sponsors
btn Partners and Indexing Bodies
btn Building New Future Journals. Start a     new journal with Academia Publishing.     Write and submit your proposal
btn Indexed by CABI


  1. Eng Technol Res

Google Scholar


Related Articles

  1. Google Scholar

  2. PubMed


Research Article

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

ISSN: 2682 5716
2022 Academia Publishing



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.

Copyright 2022 Academia Publishing. All rights reserved