Malware Analysis Using Machine Learning Tools and Techniques in IT Industry-Reference-Cited by-同舟云学术

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Malware Analysis Using Machine Learning Tools and Techniques in IT Industry

Published:2023 Issue: Volume: Page:195-209
ISSN:2191-6853
Container-title:Artificial Intelligence and Cyber Security in Industry 4.0
language:
Short-container-title:

Author:

Bhuvaneswari Amma N. G.^ORCID,Akshay Madhavaraj R.

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-2115-7_8

Reference25 articles.

1. Aslan, Ö., Yilmaz, A.A.: A new malware classification framework based on deep learning algorithms. IEEE Access 9, 87936–87951 (2021)

2. Batouche, A., Jahankhani, H.: A comprehensive approach to android malware detection using machine learning. Information Security Technologies for Controlling Pandemics, pp. 171–212 (2021)

3. Benign, Files, M.P.: Malware Dataset. https://www.kaggle.com (2022)

4. Damaševičius, R., Venčkauskas, A., Toldinas, J., Grigaliu¯nas, Š.: Ensemble-based classification using neural networks and machine learning models for windows pe malware detection. Electronics 10(4), 485 (2021)

5. Damodaran, A., Troia, F.D., Visaggio, C.A., Austin, T.H., Stamp, M.: A comparison of static, dynamic, and hybrid analysis for malware detection. J. Comput. Virol. Hacking Tech. 13(1), 1–12 (2017)

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