#

Advances in Intelligent Systems and Technologies

Book Series

About the Book
About the Author
Table of Contents

Buy this Book

eBook
  • • Included format: Online and PDF
  • • eBooks can be used on all reading devices
  • • ISSN : 2959-3042
  • • ISBN : 978-9914-9946-4-3


Hardcover
  • • Including format: Hardcover
  • • Shipping Available for individuals worldwide
  • • ISSN : 2959-3034
  • • ISBN : 978-9914-9946-5-0


Services for the Book

Download Product Flyer
Download High-Resolutions Cover

1st International Conference on Emerging Trends in Mechanical Sciences for Sustainable Technologies

Analysis of Malware Detection using Machine Learning

Mohanraj A, Ashrey Deepak Mudliar, Gopi K and Kavin Kumar V, Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India.


Online First : 18 August 2023
Publisher Name : AnaPub Publications, Kenya.
ISSN (Online) : 2959-3042
ISSN (Print) : 2959-3034
ISBN (Online) : 978-9914-9946-4-3
ISBN (Print) : 978-9914-9946-5-0
Pages : 179-182

Abstract


Malware is simply the mechanism that performs malicious actions. It functions as an executable machine performed model which performs detection. Attackers always use the malware to steal the sensitive information. It is injected into the system and renders the whole component which then make the system to be not compatible to operate in the organization which in change a threat to many. It is broadly referred to many of the malicious malware substances like Worm, Trojan, Backdoor, Botnet, Ransomware, Rootkit.

Keywords


Malware, Android, Machine Learning, APK.

  1. Statista, https://www.statista.com/.
  2. M. G. Schultz, E. Eskin, F. Zadok, and S. J. Stolfo, “Data mining methods for detection of new malicious executables,” Proceedings 2001 IEEE Symposium on Security and Privacy. S&P 2001, doi: 10.1109/secpri.2001.924286.
  3. J.Zico Kolter and Marcus A. Maloof, “Learning to detect and classify malicious executables in the wild,” in Journal of Machine Learning Research, pp. 2721–2744, 2006.
  4. Weinberger, K., Saul, L, “Distance metric learning for large margin nearest neighbor classification”,Journal of Machine Learning Research 10, 207–244, 2009.
  5. M. G. Miller, N. E. Matsakis, and P. A. Viola, “Learning from one example through shared densities on transforms,” Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662), doi: 10.1109/cvpr.2000.855856.
  6. S. Alam, Z. Qu, R. Riley, Y. Chen, and V. Rastogi, “Droidnative: Semantic-based detection of android native code malware,” arXiv preprint arXiv:1602.04693, 2016.
  7. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, pp. 1097–1105, 2012.
  8. Moser, C. Kruegel, and E. Kirda, “Exploring Multiple Execution Paths for Malware Analysis,” 2007 IEEE Symposium on Security and Privacy (SP ’07), May 2007, doi: 10.1109/sp.2007.17.
  9. Z. Yuan, Y. Lu, and Y. Xue, “Droiddetector: android malware char- acterization and detection using deep learning,” Tsinghua Science and Technology, vol. 21, no. 1, pp. 114–123, 2016.
  10. Desnos et al., “Androguard-reverse engineering, malware and goodware analysis.\
  11. H. Anandakumar, R. Arulmurugan, “Early Detection of Lung Cancer using Wavelet using Neural Networks Classifier”, Book Title “Computational Vision and Bio Inspired Computing” Lecture Notes in Computational Vision and Biomechanics, Springer Book Series, Volume No – 28, Chapter No – 09, ISBN 978-3-319-71766-1.
  12. Niranjani and N. S. Selvam, “Overview on Deep Neural Networks: Architecture, Application and Rising Analysis Trends,” EAI/Springer Innovations in Communication and Computing, pp. 271–278, 2020, doi: 10.1007/978-3-030-44407-5_18

Cite this article


Mohanraj A, Ashrey Deepak Mudliar, Gopi K and Kavin Kumar V, “Analysis of Malware Detection using Machine Learning”, Advances in Intelligent Systems and Technologies, pp. 179-182, August. 2023. doi:10.53759/aist/978-9914-9946-4-3_28

Copyright


© 2023 Mohanraj A, Ashrey Deepak Mudliar, Gopi K and Kavin Kumar V. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.